research paper

is there anyone that can help with a research paper for Evinence-based practice?

ChamberlainCollege of Nursing NR449 Evidence-Based Practice

NR449 RUA Topic Search Strategy x Revised 07/25/16 1

Required Uniform Assignment: Topic Search Strategy

PURPOSE
The Topic Search Strategy Paper is the first of three related assignments which are due in Unit 3. The purpose of

this initial paper is to briefly describe your search strategies when identifying two articles that pertain to an

evidence-based practice topic of interest.

COURSE OUTCOMES

This assignment enables the student to meet the following course outcomes.

CO 1: Examine the sources of knowledge that contribute to professional nursing practice. (PO #7)

CO 2: Apply research principles to the interpretation of the content of published research studies. (POs #4 and
#8)

DUE DATE
Refer to the course calendar for due date. The college’s Late Assignment policy applies to this activity.

POINTS POSSIBLE
This assignment is worth 160 points. The college’s Late Assignment policy applies to this activity.

REQUIREMENTS
You will be assigned a group in unit 2 (located in the team collaboration tab) to formulate an evidence-based
practice topic of interest that will be used to complete the unit 3 and unit 5 independent assignments, as well as
the group PowerPoint presentation in unit 7.

The paper will include the following.

a. Clinical Question
a. Describe problem
b. Significance of problem in terms of outcomes or statistics
c. Your PICOT question in support of the group topic
d. Purpose of your paper

b. Levels of Evidence
a. Type of question asked
b. Best evidence found to answer question

c. Search Strategy
a. Search terms
b. Databases used (you may use Google Scholar in addition to the library databases; start with

the Library)
c. Refinement decisions made
d. Identification of two most relevant articles

d. Format
a. Correct grammar and spelling
b. Use of headings for each section
c. Use of APA format (sixth edition)
d. Page length: three to four pages

PREPARING THE PAPER

1. Please make sure you do not duplicate articles within your group.
2. Paper should include a title page and a reference page.

Chamberlain College of Nursing NR449 Evidence-Based Practice

NR449 RUA Topic Search Strategy x Revised 07/25/16 2

DIRECTIONS AND ASSIGNMENT CRITERIA

Assignment
Criteria

Points % Description

Clinical Question 45 28 1. Problem is described. What is the focus of your group’s work?
2. Significance of the problem is described. What health

outcomes result from your problem? Or what statistics
document this is a problem? You may find support on
websites for government or professional organizations.

3. What is your PICOT question?
4. Purpose of your paper. What will your paper do or describe?

This is similar to a problem statement. “The purpose of this
paper is to . . .”

Levels of
Evidence

20 13 1. What type of question are you asking (therapy, prognosis,
meaning, etc.)?

2. What is the best type of evidence to be found to answer that
question (e.g., RCT, cohort study, qualitative study)?

Search Strategy 65 41 1. Search topic(s) provided. What did you use for search terms?
2. What database(s) did you use? Link your search with the

PICOT question described above.
3. As you did your search, what decisions did you make in

refinement to get your required articles down to a reasonable
number for review? Were any limits used? If so, what?

4. Identify the two most relevant and helpful articles that will
provide guidance for your next paper and the group’s work.
Why were these two selected?

Format 30 18 1. Correct grammar and spelling
2. Use of headings for each section: Clinical Question, Level of

Evidence, Search Strategy, Conclusion
3. APA format (sixth

ed.)

4. Paper length: three to four pages

Total 160 100

Chamberlain College of Nursing NR449 Evidence-Based Practice

NR449 RUA Topic Search Strategy docx Revised 07/25/2016 3

GRADING RUBRIC

Assignment
Criteria

Outstanding or Highest
Level of Performance

A (92–100%)

Very Good or High Level of
Performance

B (84–91%)

Competent or Satisfactory
Level of Performance

C (76–83%)

Poor, Failing or
Unsatisfactory Level of

Performance
F (0–75%)

Clinical Question
45 points

ALL elements present
1. Problem is presented clearly.
2. Significance of problem

described completely.
3. PICOT question is presented.
4. Purpose of paper is stated.

42–45 points

All but one element present
1. Problem is presented clearly.
2. Significance of problem

is described completely.
3. PICOT question is presented.
4. Purpose of paper is stated.

38–41 points

ALL but two elements present
1. Problem is presented clearly.
2. Significance of problem

is described completely.
3. PICOT question is presented.
4. Purpose of paper is stated.

34–37 points

Three or more elements missing
1. Problem is presented clearly.
2. Significance of problem is

described completely.
3. PICOT question is presented.
4. Purpose of paper is stated.

0–33 points

Levels of Evidence
20 points

1. Accurately identifies type of
question being asked.

2. Accurately identifies best type
of evidence available to
answer question being asked.

19-20 points

1. Accurately identifies type of
question being asked.

2. Inaccurately identifies best
type of evidence available to
answer question being asked.

17-18 points

1. Incompletely or inaccurately
identifies type of question
being asked.

2. Incompletely or inaccurately
identifies best type of
evidence available to answer
question being asked.

16 points

1. Does not identify type of
question being asked.

2. Does not identify best type of
evidence available to answer
question being asked.

0–15 points

Search Strategy
65 points

ALL elements present
1. Search topic(s) and terms

provided.
2. Includes database(s) used for

search and links to PICOT
question.

3. Explains process of refining
search to locate evidence.

4. Identifies and defends the
choice of the two most
relevant articles to provide
guidance for your next paper
and the group’s work.

60-65 points

All but one element present
1. Search topic(s) and terms

provided.
2. Includes database(s) used for
search and links to PICOT
question.
3. Explains process of refining
search to locate evidence.
4. Identifies and defends the
choice of the two most
relevant articles to provide
guidance for your next paper
and the group’s work.

55-59 points

ALL but two elements present
1. Search topic(s) and terms

49-54 points

Three or more elements missing
1. Search topic(s) and terms

0–48 points

Chamberlain College of Nursing NR449 Evidence-Based Practice

NR449 RUA Topic Search Strategy x Revised 07/25/2016 4

Format
30 p

o
i
n
t
s

1. Grammar and mechanics are
free of errors.

2. Headings are free of errors
and include all of the
following.

a. Clinical Question
b. Level of

Evidence
c. Search Strategy
d. Conclusion

3. APA format is used
without errors.

4. Total length: Three to four
pages, excluding references
and title page.

28–30 points

1. Grammar and mechanics
have no more than one
type of error.

2. Headings are free of
errors and include three
of the following.

a. Clinical Question
b. Level of Evidence
c. Search Strategy

d. Conclusion
3. APA format is used
without errors.

4. Total length: Three to four
pages, excluding
references and title page.

26–27 points

1. Grammar and mechanics
have no more than two types
of errors.

2. Headings are free of
errors and include two of
the following.
a. Clinical Question
b. Level of

Evidence
c. Search Strategy
d. Conclusion
3. APA format is used
without errors.

4. Total length: less than three
or more than four pages,
excluding references and
title page.

23–25 points

1. Grammar and mechanics have
three or more types of errors.

2. Headings have errors, are
missing, or include just one of
the following.

a. Clinical Question
b. Level of Evidence
c. Search Strategy

d. Conclusion
3. APA format is used

without errors.
4. Total length: less than three

or more than four pages,
excluding references and title
page.

0–22 points

Total Points Possible = 160 points

Research Article

Use of an Anti-Infective Medication Review
Process at Hospital Discharge to Identify
Medication Errors and Optimize Therapy

Christy P. Su, PharmD, BCPS
1
, Levita Hidayat, PharmD

2
,

Shafiqur Rahman, MD
3
, and Veena Venugopalan, PharmD, BCPS, AQ-ID

Abstract
Background: Medication reconciliation is a major patient safety concern, and the impact of a structured process to evaluate anti-
infective agents at hospital discharge warrants further review. Objective: The aim of this study was to (1) describe a structured,
multidisciplinary approach to review anti-infectives at discharge and (2) measure the impact of a stewardship-initiated anti-
microbial review process in identifying and preventing anti-infective-related medication errors (MEs) at discharge. Methods: A
prospective study to evaluate adult patients discharged on anti-infectives was conducted from October 2013 to May 2014. The
antimicrobial stewardship program (ASP) classified interventions on anti-infective regimens into predefined ME categories.
Results: Forty-five patients who were discharged on 59 anti-infective prescriptions were included in the study. The most
common indications for anti-infective regimens at discharge were pneumonia (22%, n ¼ 10), bacteremia (18%, n ¼ 8), and skin and
soft tissue infections (16%, n ¼ 7). An ME was identified in 42% (n ¼ 19/45) of anti-infective regimens. Seventy percentage of ASP
team recommendations were accepted which resulted in an avoidance of MEs in 68% (n ¼ 13/19) of patients with an ME prior to
discharge. Conclusion: This study describes the outcomes of a stewardship-initiated review process in preventing MEs at
discharge. Developing a systematic process for a multidisciplinary ASP team to review all anti-infectives can be a valuable tool in
preventing MEs at hospital discharge.

Keywords
antimicrobial stewardship, transitions of care, hospital discharge, medication errors, medication reconciliation

Introduction

Transition of care from hospital to community can be a high-

risk period for medication errors (MEs).
1

The National

Coordinating Council for Medication Error Reporting and

Prevention (NCCMERP) defines ME as any preventable event

that may cause or lead to inappropriate medication use or

patient harm, while the medication is in the control of the

health-care provider, patient, or consumer.
2

Forster and col-

leagues noted that 66% of adverse events (AEs) occurring in
patients following hospital discharge were medication related

and could be prevented in 27% of cases. Furthermore, anti-
infective agents were identified as one of the most common

medication classes associated with adverse drug events with a

reported rate of 5.1 AE per 100 prescriptions.
3,4

ME prevention is a major patient safety concern which has

received national attention.
5

Many patients who receive anti-

microbials in hospitals are also discharged on antimicrobial

therapy, to complete the treatment course at home, in long-

term acute care centers, skilled nursing facilities, outpatient

infusion centers, or dialysis centers.
6,7

In the absence of anti-

microbial stewardship oversight at these transitions of care

points, patients may be discharged from hospitals on

inappropriate therapy. This presents a unique opportunity for

antimicrobial stewardship programs (ASP) to be involved in

the discharge process. We conducted a pilot study at The

Brooklyn Hospital Center (TBHC), a 416-bed community

teaching facility in Brooklyn, New York. The objectives of this

study were to (1) describe a structured, multidisciplinary

approach to review anti-infective prescriptions at discharge and

(2) measure the impact of a stewardship-initiated anti-infective

review process in identifying and preventing anti-infective-

related MEs at discharge. The experience gained from this

1
Department of Pharmacy, Memorial Hermann Greater Heights Hospital,

Houston, TX, USA
2 Global Health Science, The Medicines Company, Parsipanny, NJ, USA
3 Division of Infectious Diseases, The Brooklyn Hospital Center, Brooklyn, NY,

USA
4
Department of Pharmacotherapy and Translational Research, College of

Pharmacy, University of Florida, Gainesville, FL, USA

Corresponding Author:

Christy P. Su, Department of Pharmacy, Memorial Hermann Greater Heights

Hospital, 1635 North Loop West, Houston, TX 77008, USA.

Email: christy.su@memorialhermann.org

Journal of Pharmacy Practice
2019, Vol. 32(5) 488-492
ª The Author(s) 2018
Article reuse guidelines:
sagepub.com/journals-permissions
DOI: 10.1177/0897190018761411
journals.sagepub.com/home/jpp

mailto:christy.su@memorialhermann.org

https://sagepub.com/journals-permissions

https://doi.org/10.1177/0897190018761411

http://journals.sagepub.com/home/jpp

http://crossmark.crossref.org/dialog/?doi=10.1177%2F0897190018761411&domain=pdf&date_stamp=2018-03-13

study is critical in identifying institutional resources needed to

implement an anti-infective review process and sustain it to

produce desired outcomes.

Methods

A single-center prospective study was conducted at TBHC

from October 2013 to May 2014. TBHC has a centralized

pharmacy model. Pharmacists in the central inpatient pharmacy

provide distributional services and primarily serve in a drug

dispensing role. There are also clinical pharmacy specialists

integrated into the patient care teams within the medical inten-

sive care, family medicine, and pediatrics units. These pharma-

cists perform a combination of clinical and distributional

activities. At the time of this study, the pharmacy department

operated with 11 clinical pharmacy specialists which included

coverage for inpatient and outpatient clinical services. The

ASP was established in 2004 and comprised of infectious dis-

eases (ID) physicians, ID clinical pharmacists, and a PGY-2 ID

resident. The ASP was actively involved in prospective anti-

infective review during hospitalization; however, no process

was in place for the assessment of discharge treatment.

To begin the development of a systematic process to review

anti-infective agents at discharge, one hospital service was

selected during this study period, with future plans to expand

the initiative hospital-wide. Patients greater than 18 years of age

who were discharged from the family medicine service on intra-

venous (IV) or oral anti-infective therapy were included in this

initiative. MEs were identified according to NCCMERP and

were classified into the following predefined categories by

Heintz and colleagues: safety, efficacy, or simplification.
8

Safety

interventions included those related to ordering laboratory tests,

adjusting doses due to renal dysfunction, avoiding central line

placement, avoiding unnecessary anti-infective agents, reassess-

ment of patient’s stability, or adjusting therapy due to drug

interactions. Efficacy interventions included those related to

anti-infective selection, dose, or extending the duration of ther-

apy. Simplification interventions included those related to reduc-

ing the frequency of dosing, performing IV to oral interchange,

reducing the number of anti-infective agents prescribed, or short-

ening the duration of therapy (Table 1). Each anti-infective agent

prescribed could have more than 1 type of intervention.

The stepwise process of implementing the review process is

depicted in Figure 1. The ASP clinical pharmacist contacted the

family medicine team daily for a list of patients with an anticipated

discharge within 48 hours. The ASP team then screened these

patients for anti-infective prescriptions through electronic medi-

cal records. Patients who had a prescribed anti-infective agent

were evaluated by the ASP team for appropriateness based on

evidence-based practice guidelines. Potential interventions that

were identified were then verbally communicated and discussed

with the primary team physician. However, if a patient received an

ID consultation during hospital admission, the ID consultant

would be contacted and changes to treatment regimens were made

collaboratively with the ASP team. All recommendations were

made prior to patient discharge and the number of accepted inter-

ventions and types were quantified. Descriptive statistics were

used to present the results. This study was conducted in compli-

ance with the hospital’s institutional review board.

Results

Forty-five patients discharged from the family medicine ser-

vice were included in the final analysis. Of 59 anti-infective

agents prescribed, the route of administration was oral for

42 (71%) agents and intravenous for 17 (29%) agents. The most
common indications for anti-infective regimens at

discharge

were pneumonia (22%, n ¼ 10), bacteremia (18%, n ¼ 8), and
skin and soft tissue infections (16%, n ¼ 7; Table 2). Four
patients were discharged on regimens for more than 1 indica-

tion. Most patients were discharged on an oral cephalosporin,

penicillin, or fluoroquinolone.

Table 1. Study Definitions.

Safety

interventions

Ordering laboratory tests, adjusting dose, avoiding
central line placement, avoiding unnecessary anti-

infective agents, patient stability, and drug interactions

Efficacy
interventions

Optimizing anti-infective selection, dose, and
extending duration of therapy

Simplification

interventions

Reducing the frequency of dosing, IV-PO interchange,

reducing the number of anti-infective agents
prescribed, and shortening duration of therapy

Abbreviations: IV, intravenous; PO, oral.
Intervention types at hospital discharge for prescribed anti-infective agents.

Primary team contacted for list of
an�cipated discharges in following

48 hours

Prescrip�on entered in
electronic medical record for

an�-infec�ve therapy

An�-infec�ve therapy evaluated
by ASP team

Recommenda�ons made to
primary team prior to pa�ent

discharge

Figure 1. Flowchart process of identifying and evaluating anti-
infective therapies prescribed at hospital discharge.

Su et al 489

An ME was identified in 42% (n ¼ 19/45) of patients
reviewed. Overall, 56% (n ¼ 33/59) individual anti-
infectives had at least 1 associated ME which were further clas-

sified as being related to safety, efficacy, and simplification in

33% (n ¼ 11), 33% (n ¼ 11), and 33% (n ¼ 11) of cases, respec-
tively (Figure 2).

Most frequent interventions in each category made by the

ASP team were as follows: optimizing selection of anti-

infective (n ¼ 7), avoidance of unnecessary anti-infectives
(n ¼ 6), reduction in number of anti-infectives prescribed
(n ¼ 5). The duration of anti-infective therapy was reduced for
4 prescriptions. Recommendations were made in multiple ME

categories in 10 patients. Seventy percentage (n ¼ 23/33) of
ASP team recommendations were accepted which resulted in

an avoidance of MEs in 68% (n ¼ 13/19) of patients with an
ME prior to discharge. Recommendations made in the simpli-

fication category had a higher acceptance rate (Figure 2). Nine

patients overall from this study were readmitted within 30 days

of discharge, of which 1 patient had an infection-related read-

mission. Of note, this patient did have a simplification-related

ME identified on the previous admission that was intervened

upon and accepted.

Discussion

This study evaluated the impact of a structured, multidisciplin-

ary approach of anti-infective prescription review at discharge

to prevent MEs. The Joint Commission on Accreditation of

Healthcare Organizations designated medication reconciliation

as a National Patient Safety Goal, recognizing its potential impact

on reducing patient harm during transitions of care.
5

Studies have

revealed that medication discrepancies occur more frequently on

discharge than on admission.
9

In our evaluation, MEs were iden-

tified in 42% of anti-infective regimens prescribed at discharge
which is similar to the findings by Yogo and colleagues.

10
These

investigators conducted a retrospective cohort study on the appro-

priateness of therapy in adult patients discharged on oral antibio-

tics. Prescriptions were retrospectively reviewed for antibiotic

selection, indication, dose, and duration of therapy. Overall, they

concluded that 53% of antibiotic prescriptions reviewed were
deemed inappropriate. The most common reasons for inappropri-

ateness were related to efficacy due to excessive treatment dura-

tion, suboptimal antibiotic selection, and incorrect dose. Our

study noted MEs in 12% (n ¼ 4), 21% (n ¼ 7), and 6% (n ¼ 2)
of cases in similar categories. Scarpato and colleagues also iden-

tified 70.7% of antibiotic prescriptions at discharge to be inap-
propriate and concluded that on average, patients were sent from

the hospital on 3.8 days of unnecessary antibiotics.
11

Our comprehensive anti-infective review process was suc-

cessful in averting MEs in 68% of patients. However, a more
targeted approach to performing this review includes identifi-

cation of specific indications highly prone to MEs. In our

cohort, pneumonia and skin and soft tissue infections were

among the top 3 infections that required an intervention. This

result further suggests that prescriptions for these common

infections should be closely reviewed for appropriateness prior

Figure 2. Medication error classifications (n ¼ 33). A total of 33
interventions were recommended in 19 patients. Each anti-infective
agent could have more than one type of intervention. Reasons for
rejected interventions were as follows for safety: avoiding unneces-
sary anti-infectives (n ¼ 2), adjusting dose (n ¼ 1), ordering labora-
tory tests (n ¼ 1), patient stability (n ¼ 1); efficacy: anti-infective
selection (n ¼ 1), anti-infective dosing (n ¼ 1), extending duration of
therapy (n ¼ 1); simplification: reducing number of anti-infectives
prescribed (n ¼ 2).

Table 2. Baseline Demographics.

Patient Characteristics n ¼ 45

Age in years, mean (SD) 60 (19)

Female, n (%) 22 (49)
Location, n (%)

Medical floor 45 (100)
Indicationa, n (%)

Pneumonia 10 (22)

Bacteremia 8 (18)
SSTI 7 (16)

UTI 7 (16)
Clostridium difficile infection 5 (11)

Osteomyelitis 4 (9)
Other 8 (18)

Anti-infectives prescribed n ¼ 59

Route, n (%)
Oral 42 (71)

Intravenous 17 (29)
Anti-infective classa, n (%)

Cephalosporin 12 (20)
Penicillin 10 (17)

Fluoroquinolone 7 (12)
Vancomycin 6 (10)

Macrolide 5 (8)
Anti-tuberculosis 4 (7)

Tetracycline 3 (5)
Sulfamethoxazole/trimethoprim 3 (5)

Metronidazole 3 (5)

Other 6 (10)

Abbreviations: SD, standard deviation; SSTI, skin and soft tissue infection; UTI,
urinary tract infection.
Fifty-nine anti-infective agents were prescribed for 45 patients.
a
Four patients were discharged on regimens for more than 1 indication.

490 Journal of Pharmacy Practice 32(5)

to discharge. Due to the small study sample size, there were no

obvious trends with respect to safety interventions having the

lowest intervention success rate. One possible explanation for

this observation is that safety interventions could prolong dis-

charge since it included ordering of additional laboratory tests

and required extended monitoring of patient stability.

Our results highlight the impact of ASP teams in assisting

with the review of antimicrobial therapy prior to discharge to

enhance patient safety and treatment efficacy.
5,9-11

In our expe-

rience, there is tremendous value in integrating an ID-trained

practitioner in this process. However, the lack of resources with

regard to ID-trained personnel can be a barrier to implementa-

tion of an anti-infective review process. In such settings with

limited resources, clinical pharmacists with an interest in ID

may assume this role. Trained pharmacists that perform med-

ication reconciliation at discharge have demonstrated a

decrease in MEs and a reduction in readmission rates.
12

Through this study, it was determined that immense man-

power and resources are required for long-term sustainability

of a discharge antibiotic review program. The major limitation

of this study was inability of the study investigators to capture

all patients discharged on anti-infectives from the family med-

icine service. The lack of dedicated personnel such as social

workers, clinical case managers, or patient navigators focused

on coordinating discharge planning contributed to this limita-

tion. The investigators in this study spent approximately

1 to 2 hours each day reviewing patients for the study, 20 to

30 minutes of this time was utilized in identifying, reviewing,

and making recommendations. The rest of the time was used to

contact teams to obtain anticipated patient discharge lists. This

ineffective system of communication resulted in patients being

discharged without anti-infective treatment review. At the time

of the study, there was a clinical pharmacy specialist that

rounded with one of the family medicine teams. This pharma-

cist could only assist with identifying patient discharges for this

team. So, the number of patients reported as discharged on anti-

infectives represents a convenience sample suggesting that the

scope and impact of this type of program on ME identification

is much greater. Another limitation of the study was that this

review process was only conducted for 1 hospital service.

Other hospital services may have different prescribing patterns,

which could lead to different types and rates of MEs than

described here. Expanding this study throughout the hospital

would allow for the assessment of MEs in a variable patient

population.

“Transitions of care” refers to the movement of patients

between health-care practitioners, settings, and home as their

condition and care needs change.
13

Pharmacists, as members of

a multidisciplinary team, can facilitate effective transitions of

care by conducting medication intervention. Phatak and col-

leagues described the impact of pharmacist involvement at

transitions of care by targeting patients on high-risk medica-

tions, which included anti-infectives.
14

This study was able to

demonstrate a reduction in readmission rates and MEs.

We were unable to continue the discharge anti-infective

review program at our institution beyond this study because

the immense manpower and resources required for long-term

sustainability were unattainable. ASPs should work collabora-

tively with unit-based pharmacists who are responsible for des-

ignated areas, as they are most familiar with patients and their

hospital course. Engaging frontline pharmacists in this process

is essential since they are more likely to have direct contact

with primary teams, and other relevant staff members involved

with the discharge planning process. An additional benefit of

having unit-based pharmacists involved is that conversations

with primary teams can be initiated during patient care rounds

and recommendations to optimize anti-infective regimens can

be made together. For example, if a discharge medication

requires therapeutic drug monitoring, labs can be ordered while

the patient is still admitted to ensure dose appropriateness at

discharge. With time, working with the same hospital person-

nel should foster a stronger understanding of the value of an

anti-infective review process at discharge.

Conclusion

Anti-infectives are highly prone to MEs at discharge. This

study describes the outcomes of a stewardship-initiated review

process in preventing MEs at discharge. Over half of anti-

infective agents reviewed at discharge in this study had an

identified ME. The ASP team’s recommendations were suc-

cessful in preventing MEs in the majority of patients with an

inappropriate anti-infective regimen at discharge. Despite the

identification of MEs, not all of the ASP team’s recommenda-

tions were accepted, which suggests there is a need for further

education of house staff on patient safety, MEs, and the goals of

antimicrobial stewardship. Developing a systematic process for

a multi-disciplinary ASP team to review all anti-infectives can

be a valuable tool in preventing MEs at hospital discharge.

However, without dedicated, trained personnel to assist with

coordinating this process, long-term sustainability is a chal-

lenge. Use of a transitions of care program with unit-based

pharmacists to review anti-infective prescriptions at discharge

should be considered.

Authors’ Note

Presented as a poster at the 2014 Interscience Conference on Antimi-

crobial Agents and Chemotherapy Annual Meeting, Washington, DC,

September 6, 2014. News feature published in Am J Health Syst

Pharm. 2015 Feb;72(4):264-5.

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to

the research, author

ship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, author-

ship, and/or publication of this article.

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38(3):353-355.

12. Fera T, Anderson C, Kanel KT, et al. Role of a care transition

pharmacist in a primary care resource center. Am J Health Syst

Pharm. 2014;71(18):1585-1590.

13. The Joint Commmission. Transitions of care: the need for a more

effective approach to continuing patient care. https://www.jointcom

mission.org/assets/1/18/Hot_Topics_Transitions_of_Care .

14. Phatak A, Prusi R, Ward B, et al. Impact of pharmacist involve-

ment in the transitional care of high-risk patients through med-

ication reconciliation, medication education, and postdischarge

call-backs (IPITCH study). J Hosp Med. 2016;11(1):39-44.

492 Journal of Pharmacy Practice 32(5)

http://www.nccmerp.org/about-medication-errors

https://www.jointcommission.org/assets/1/6/NPSG_Chapter_HAP_Jan2017

https://www.jointcommission.org/assets/1/18/Hot_Topics_Transitions_of_Care

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R E S E A R C H A R T I C L E

Impact of pharmacist-ledmedication reconciliation onadmissionusing
electronic medical records on accuracy of discharge prescriptions
Dona S. Lawrence1, Noman Masood1, Dawn Astles2, Claire E. Fitzgerald3, Ata Ul. Bari1

1 Pharmacy Department, Northern Beaches Health Service, Manly Hospital, Manly, Australia
2 Pharmacy Department, Northern Sydney Local Health District (NSLHD), Sydney, Australia
3 Pharmacy Department, Northern Beaches Health Service, Mona Vale, Australia

Abstract

Background: Unintentional medication errors are common at hospital discharge and have the potential to cause significant patient harm.
Current electronic medical records systems offer the facility to change the process of medication reconciliation by pharmacists.
Aim: To test the impact of pharmacist-led medication reconciliation at admission recorded on the electronic medication form, on the time-
liness and accuracy of discharge prescriptions.
Method:A prospective pre-andpost-interventionalobservationalstudywascarriedout fromJuneto October2013attwodistricthospitals.
Pharmacists recorded admission medication using National Medication Management Plan (phase 1) and the electronic medication form
in patients’ electronic medical records (phase 2). Data collected included time taken for the medical officer to complete the medication
form in electronic medical records, the number of times the medical officer was contacted by the pharmacist completing the discharge rec-
onciliation and unintentional discharge medication discrepancy types.
Results: In total 118 patients were included: 66 patients in phase 1 and 52 in phase 2. Data were analysed using chi-squared test, Fisher’s
exact test and Mann–Whitney test. There was a significant (33–13%, p < 0.0001) reduction in the proportion of medication orders with a discrepancy. This was because of the significant (25.5–1.9%, p < 0.0001) reduction in discrepancies relating to patients’ usual medication. Time taken for the medical officer to complete the medication form in electronic medical records decreased from 37 s/item (interquartile range, 29–48; n = 51) to 21 s/item (interquartile range, 11–35; n = 35) (p < 0.001). The number of telephone calls to medical officers decreased from 95 to 73%. Conclusion: This integrated approach to medication reconciliation has highlighted patient safety benefits, and reduced medical and pharmacy workload.

Keywords: medication, pharmacists, discharge.

INTRODUCTION

Medication reconciliation is a formal process of obtaining,
verifying and documenting an accurate list of a patient’s
current medicines at the time of admission and comparing
this list with the admission, transfer and discharge orders,
to identify and resolve the discrepancies.1

Prescribing at discharge remains a process with poten-
tially significant risk for patient harm, and complete med-
ication reconciliation lends itself to improving patient
safety.1–4 A systematic review stated that patients with at
least one medication discrepancy at hospital discharge var-

ied considerably from 25 to 80% and the proportion of med-
ication orders with a discrepancy ranged from 8.4 to 16.3%.
Of the 20 studies which reported the most frequent type of
unintentional medication discrepancy, all but 3 reported
that medication omission was the most frequent type iden-
tified.5 Omission of medicine from the discharge summary
list was associated with an increased risk (by a factor of 2.3)
of hospital readmission or adverse medicine event.2

Previouslocalauditsundertakenattwodistricthospitals
in the Sydney metropolitan area have shown that the risk of
medication errors owing to incomplete medication recon-
ciliation remains high at discharge with 68%6 and 73%7

of electronic discharges having at least one unintentional
error. Errors of omission were the most frequent type,
accounting for 45% in one study and the second most com-
mon, accounting for 22% in the second study. Interestingly,
handwritten medication history on admission (MHOA)
forms completed by pharmacists were available to medical

Address for correspondence: Dona S. Lawrence, Pharmacy Depart-
ment, Manly Hospital, 150 Darley Road, Manly, New South Wales
2095, Australia
E-mail: Dona.Lawrence@health.nsw.gov.au

Official Journal of the Society of Hospital Pharmacists of Australia

doi: 10.1002/jppr.1091

officers for 65% of patients but it was observed that these
forms had no impact on prescribing errors at discharge.6

Several Australian studies have investigated the impact
of electronicdischargereferral (eDR)systems onthequality
of medication prescribing at discharge. Garret and McCor-
mack8 found that the number of discharges with one or
moreprescribing errors decreasedsignificantly after imple-
mentation of the eDR system (57.6 vs 34.8%, p < 0.001). Ng et al.9 reported the percentage of electronic discharge med- ication profiles with changes made after reconciliation ran- ged from 19 to 40%. Callen et al.10 found that 13.3% of electronic summaries contained medication errors with medication omission the most common type.

In the last few years, there has been considerable uptake
of electronic solutions to the admission medication recon-
ciliation process, largely owing to increasing infiltration
of vendors using sophisticated medication history tools.11

The currently available electronic medical records (eMR)
system implemented across Northern Sydney Local Health
District offers the facility of maintaining medication forms
by a pharmacist, in addition to medical staff. This medica-
tion form is automatically imported by the medical officer
into the electronic discharge summary. The local Sustaina-
ble Access Committee at the hospital identified the
discharge medication management process as a high prior-
ity for clinical redesign. With the aim that, in addition to
improving patient safety, a redesign could potentially
improve patient flow and may support the organisation
in meeting its National Emergency Access Target (NEAT).

To the best of our knowledge, no published studies com-
pare the effect of pharmacist-documented best possible
medication history (BPMH) recorded in the electronic med-
ication form at admission, which is then used as the basis of
the electronic discharge medication profile, on uninten-
tional medication discrepancies at discharge and time for
the medical officer to complete the medication form on
the eDR. Our study aims to address this gap in knowledge
by testing the impact of pharmacist-led medication recon-
ciliation at admission recorded on the electronic medica-
tion form, on the timeliness and accuracy of discharge
prescriptions.

METHODS

This is a prospective pre- and post-intervention observa-
tional study carried out over a 4-month period from June
until October 2013 at two district hospitals. A 150-bed pub-
lic hospital, comprising mostly general medical, general
surgical and orthopaedic patients, serviced by 5.5 full-time
equivalent pharmacists. Second, a 200-bed public hospital,
comprising mostly general medical, general surgical and
aged-care specialities, serviced by 5.6 full-time equivalent

pharmacists. Consecutive adult patients were included if
they were reviewed by a pharmacist (working hours,
08:30–17:00 hours, Monday–Friday), on one or more med-
ications, the BPMH was completed and recorded in the
emergency department (ED) by the ED pharmacist, the
patient was admitted to hospital, and the patient’s eDR
was screened and reconciled by the ward pharmacist. Rec-
onciliation by the clinical pharmacist was carried out as per
the Society of Hospital Pharmacists of Australia Standards
of Practice for the Provision of Medication Reconciliation.12

Phase 1 (Normal Process)

For the first 8 weeks (24 June 2013–20 August 2013), the ED
pharmacist used the handwritten National Medication
Management Plan when recording BPMH and reconciling
with the patient’s National Inpatient Medication Chart
(NIMC) see Figure 1. Data were collected from patients
presenting to the EDs at the two hospitals, who were then
admitted to hospital and discharged from a medical ward
only. The ED pharmacist recorded the time taken to com-
plete the medication history. Once completed, the pharma-
cist selected ‘other’ in the NIMC for additional charts
option, and annotated ‘Med History form complete’. The
ED pharmacist recorded if there was a medication form
in the patient’s eMR (Cerner Firstnet/Powerchart; Cerner,
Kansas City, MO, USA) from a previous admission, or if the
ED medical officer recorded a BPMH on the medication
form in the patient’s eMR at admission. At discharge, data
were collected on the following basis: time taken for the
medical officer to complete the medication form on the
electronic discharge, time taken for the clinical pharmacist
to complete discharge reconciliation, number of times
the medical officer was contacted by the clinical pharma-
cist, thenumber of medication orderswith anunintentional
discrepancy, and the number and type of discrepancies
(each medication order may have more than one type
of discrepancy).

Phase 2 (Intervention Process)

In the second 8 weeks (27 August 2013–24 October 2013),
the ED pharmacist recorded the BPMH on the electronic
medication form in eMR and reconciled with the patient’s
NIMC (see Figure 1). Data were collected from patients
presenting to the ED at one of the hospitals who were then
admitted to the hospital and discharged from either the
medical, surgical, emergency medical unit or orthopaedic
wards. The ED pharmacist followed a locally approved
procedure when entering the BPMH into the patient’s
eMR and saved it to a pharmacy specific consult note. Once
completed, the pharmacist selected ‘other’ in the NIMC
for additional charts, and annotated ‘eMR Med History

Pharmacist-led medication reconciliation 167

complete’. For this project, the ward pharmacists did not
update the electronic medication chart throughout the
patients hospital stay. All data collected were as per phase 1.

Clinical pharmacists completed an audit tool and classi-
fied the discrepancies for the discharges they reviewed
during their daily activities. A review of the published
literature found considerable variation in the type of
discrepancies or transcription errors identified on

discharge;8,10,13,14 therefore, our audit tool was developed
based on the previous local audit tool and amended follow-
ing consultation with clinical pharmacists and the medica-
tion safety pharmacist. A description and examples of the
discrepancies are shown in Table 1. For the purpose of
the present investigation a discrepancy, or transcription
error, was defined as any unintended inconsistency
between the medication the patient should be discharged

Normal Process (Phase 1) Intervention Process (Phase 2)

On admission, pharmacist records a
handwritten BPMH on the NMMP.

Pharmacist uses NMMP to
reconcile medication on NIMC.

On discharge, MO create s a Medication Form on
eMR. Medication from the most recent past
admission will auto-populate the Medication
Form (often very out of date). Date last updated is
displayed in Medications Last Updated field at the
bottom of the profile. MO refers to NMMP and
NIMC before transferring relevant medication in
to the Medication Form in eMR for current
admission. Adds/amends all medications on
admission (status = Medication continued – dose
Unchanged).

On admission, pharmacist records
BPMH on Medication Form in eMR
and saves to a Pharmacy Consult note.

Pharmacist uses printed version of
Medication Form in the Pharmacy
Consult note to reconcile against NIMC.

On discharge, MO create s a
Medication Form on eMR.
Medication list from the pharmacists
BPMH will auto-populate the
Medication Form. MO refers to
NIMC before transferring relevant
medication in to the Medication Form
in eMR for current admission.

MO continues to review medication for current admission. Adds any medications
commenced this admission (status = New Medication). Lists any medications discontinued
(status = Medication ceased). Lists any changes to dose (status = Dose increased or Dose
reduced).

Once all medication r eviewed and updated, the Medication Form is imported in to the
electronic discharge referral. MO saves as “interim”. Printed, signed by MO and handed to
pharmacist for discharge reconciliation. Electronic medication profile can be updated again
prior to discharge if necessary. Final version is imported into the electronic discharge
summary and a final reconciliation and sign -off is completed by pharmacist before
dispensing.

BPMH, best possible medication history; eMR , electronic Medical Records ; MO, medical officer;
NIMC, National inpatient medication chart; NMMP, National medication management plan

Figure 1 Medication reconciliation process.

168 D. S. Lawrence et al.

with and the medication form in the eDR, identified by the
clinical pharmacist, discussed and verified with the pre-
scriberandreconciled bythechangebeing re-importedinto
the final discharge summary byeither the medical officer or
the pharmacist. The pharmacists timed themselves when
recording the BPMH and reconciling the discharge referral.
Time taken for the medical officer to complete the medica-
tion form on the electronic discharge was noted by the
investigational pharmacist by looking at the timeline
on eMR.

Data Analysis

Categoricalvariableswerecomparedusingchi-squaretests
or Fisher’s exact test when appropriate. Time taken to rec-
ord BPMH is presented as median (interquartile range) and
differences betweentimesinphase1andphase2werecom-
pared using Mann–Whitney tests. Analysis was performed
using Microsoft Office Excel 2007 and GraphPad Software
QuickCalcs 2014. Phase 2 data were analysed for all wards
and for medical wards only owing to the disparity in meth-
odology used between phase 1 which collected data on
medical ward discharges only and phase 2 which collected
data on medical, surgical, emergency medical unit and
orthopaedic ward discharges. Unintentional discrepancies
at discharge were analysed as a whole and also further stra-
tified into patient’s usual medication discrepancies (i.e.
from BPMH at admission) and any new discrepancies

(i.e. medication changes made in the hospital; new medica-
tion missing; frequency/dose/route of administration
missing or incorrect for a new medication; or new changes
to admission medication).

RESULTS

Data were collected from 66 patients in phase 1 and
52 patients in phase 2.

Admission Medication Reconciliation

The median number of items per BPMH was 11 (8.14) in
phase 1 and 12 (8.14) in phase 2 (p = 0.94). The ED medical
officers did not complete any medication forms in patient’s
eMR at admission in phases 1 or 2. The percentage of med-
ication forms in patient’s eMR from previous admissions
was similar between phase 1 (64%) and phase 2 (65%)
(p = 0.84). The median time for ED pharmacists to take
and record a BPMH was 54 s/medication order (40,73)
in phase 1 and 50 s/medication order (39,70) in phase 2
(p = 0.42).

Discharge Medication Reconciliation

AsseeninTable 2,thenumberofdischargeswithatleastone
discrepancy was reduced by approximately one-third
(97–65%, p < 0.0001). When comparing phase 1 and phase

Table 1 Discrepancy types and examples

Type of discrepancy Examplea

Omission (unintentional deletion of a
drug)

Clopidogrel commenced in hospital following two transient ischaemic attacks. Not on discharge
prescription.

Commission (unintentional addition
of a drug)

Amoxycillin/clavulanic acid 875 mg/125 mg for 3 days and doxycycline for 3 days on 2013 discharge
prescription. Carried over from the 2011 discharge summary in error.

Incorrect or missing dose Cholecalciferol dose increased in hospital as vitamin D level was 46 nmol/L (50–130 nmol/L), but
discharge prescription was for pre-admission dose.

Incorrect or missing frequency Clindamycin 300 mg twice daily on discharge prescription. Increased to 8 hourly after pharmacist
intervention.

Stopped medication not listed under
the ceased section

Buprenorphine patch ceased in hospital and patient commenced on oxycodone SR. Buprenorphine
patch still listed as current medication on discharge prescription.

Length of therapy missing or
inappropriate

Length of antibiotic course on discharge prescription missing on numerous occasions.

Route of administration incorrect or
missing

Tiotropium 18 micrograms listed as oral on discharge prescription instead of inhalation puffer.

Ceased (medication incorrectly listed
as ceased)

Fentanyl12 micrograms/h patchandtramadolSR100 mglisted asceasedmedication onthedischarge
prescription. The patient was not on either medication at hospital admission. The entry was carried
over from an old discharge summary.

Status incorrect Levodopa/carbidopa 100 mg/25 mg listed as ‘medication ceased’ on the discharge prescription when
it should have been ‘dose increased’.

Formulation/dosage form Diltiazem 180 mg daily on discharge prescription. Slow-release preparation was not specified.

a Examples were obtained from this study.

Pharmacist-led medication reconciliation 169

2 discharges, there was a significant reduction in the propor-
tion of medication orders with a discrepancy (33–13%, p < 0.0001) and a significant reduction in total discrepancies per medication order (36–14%, p < 0.0001). The latter was calculated because medication orders may have more than one discrepancy type. When comparing phase 1 and phase 2 discharges for medical wards only, there was a significant reduction in the proportion of medication orders with a dis- crepancy (33–21%, p = 0.005) and a significant reduction in totaldiscrepanciespermedicationorder(36–23%,p < 0.001).

Figure 2 shows the distribution of unintentional discre-
panciesondischargeprescriptions.Thesewerefurtherstra-
tified into patients’ usual medication discrepancies, i.e.
from BPMH at admission (Figure 3), and new discrepan-
cies, i.e. new changes made in hospital (Figure 4).

When comparing phases 1 and 2, there was a significant
reduction in patients’ usual medication discrepancies on
discharge prescriptions (25.5–1.9%, p < 0.0001). In phase 1, the most frequent discrepancy types on discharge for patients’ usual medication were frequency (52/216), status (48/216) and omission (47/216) compared with errors of omission (5/12) in phase 2.

There was no statistically significant difference in new
discrepancies on discharge prescriptions between phases
1 and 2 (11–12%, p = 0.66). In phase 1, the most frequent
new discrepancy type was newly stopped medication not
listed as ceased (28/93) compared with omission (16/75),
frequency (14/75) and dose (13/75) in phase 2. Omission
was the most common unintentional discharge medication
discrepancy overall (80/396 discrepancies).

Time on Discharge

The median time for medical officers to complete the
medication form inthe patient’s eMRfor the discharge refer-
ral was almost halved between phases 1 and 2 (37 s/

medication order (interquartile range (IQR), 29–48) in
phase1(n = 51),21 s/medicationorder(IQR,11–35)inphase
2 (n = 35) (p < 0.001) and 20 s/medication order (IQR,16–23) in phase 2 medical ward only (n = 11) (p < 0.001)). The num- ber of times the medical officer was contacted to sort out dis- crepancies in the discharge prescription was reduced by almost a quarter (95% in phase 1 to 73% in phase 2, p < 0.001). The median time for the clinical pharmacist to recon- cile the discharge referral was 103 s/medication order (IQR, 54–150) in phase 1 (n = 63) and 81 s/medication order (IQR, 55–136) in phase 2 (n = 46) (p = 0.36).

DISCUSSION

There are many international studies13–17 and several Aus-
tralian studies8–10 describing eDR or medication manage-
ment systems and the effect on discharge medication

0%
10%
20%
30%
40%
50%
60%
70%
80%

90%

100%

P
e

rc
e

n
ta

g
e

Discrepancy Type

Phase 2 (n=87) Phase 1 (n=309)

Figure 2 Distribution oftotal discrepancies ondischargeprescriptions.

Table 2 Discrepancies at discharge

Phase 1
(n = 66

discharges)

Phase 2
(n = 52

discharges)
p Value (phase 1

vs phase 2)
Phase 2 medical ward only

(n = 17 discharges)
p Value (phase 1 vs phase 2

medical wards only)

Discharges with at least one
discrepancy

64 (97%) 34 (65%) <0.0001 14 (82%) 0.056

Number of medication orders for
reconciliation

847 641 211

Mean (±SD) medication orders/
discharge

12.8 (±5.1) 12.3 (±4.6) 12.4 (±4.4)

Medication orders with discrepancy
(%/medication order)

278 (33%) 83 (13%) <0.0001 44 (21%) 0.005

Total discrepancies (%/medication
order)

309 (36%) 87 (14%) <0.0001 48 (23%) <0.001

170 D. S. Lawrence et al.

errors. To the best of our knowledge, this is the first pro-
spective study to describe the impact of pharmacist record-
ing BPMH on the electronic medication form at the time
of admission, on the rate of unintentional discharge medi-
cation discrepancies and time for doctor to complete
the medication form at discharge. Previous studies pub-
lished involve doctors manually typing the medications
into the medication form at the time of discharge. In this
study, the ED pharmacist has already entered the BPMH
at the time of admission on the medication form which is
automatically imported into the discharge referral, and
the doctor on discharge only needs to annotate any changes
or new medication, avoiding duplication of work.

Within this context, the implementation of the change in
medication reconciliation process resulted in a significant
32% absolute reduction in the number of discharges with
at least one unintentional discrepancy and a significant
20% absolute reduction in the proportion of medication
orders atdischarge with anunintentional discrepancy. This
indicates that pharmacists entering BPMH electronically
improves discharge medication accuracy and potential
safety. This reduction in unintentional discrepancies at dis-
charge was due to a significant reduction in patients’ usual
medication discrepancies (i.e. from BPMH at admission),
with no difference in new discrepancies (i.e. medication
changes made in hospital).

The proportion of discharges with at least one discrep-
ancy (97% in phase 1 and 65% in phase 2) is higher than that
of previous studies describing a frequency of 25–80%.5

Marked variation in the frequency reported in published
studies is likely due to methodological differences, types
of discrepancies collected, and inconsistencies in the inter-
pretation of ‘error’ which is a major limitation in previous
trials as well as in this study. The higher frequency can be
explained as we collected discrepancies on more para-
meters than previous local audits6,7 and published
studies,8,10,13,14 including status incorrect (see Figure 1),
formulation/dosage form, and medication incorrectly
listed as ceased. In keeping with previous studies,5 error
of omission was the most commonly reported discrepancy
in this study. Other limitations include the lack of an inde-
pendent/multidisciplinary panel to review reports and
risk of subjectivity in the application of error classification,
low numbers, therefore finding could be due to chance, and
data could only be collected during pharmacy working
hours (08:30–17:00 hours, Monday–Friday).

Another major limitation of the study was the disparity
in methodology followed between phases 1 and 2. Phase 1
collected data across two hospitals and unintentional med-
ication discrepancies on medical ward discharges thereby
involving two-ward pharmacists only, reducing the time
required for training and the chance of inconsistencies in
interpretation of discrepancies. Phase 2 collected data at
one hospital site which was unavoidable owing to phar-
macy staffing issues in the ED and therefore had to include
discharges from medical, surgical, emergency medical unit
and orthopaedic wards to complete the project in a reason-
able time frame. A previous local audit7 found that the
emergency medical unit and orthopaedic ward had the
highest frequency of medicationdiscrepancies ondischarge;
therefore, looking at medical ward only in phase 1 should
not bias the results. Despite this, we analysed the results
of the medical ward discharges in phase 2 separately which
still showed a significant reduction in discrepancies.
The small number of patients in phase 2 medical ward only
(n = 17) may limit the generalisability of our results.

82%

84%

86%

88%

90%

92%

94%

96%

98%

100%
P
e
rc
e
n
ta
g
e
Discrepancy Type

Phase 2 (n=12) Phase 1 (n=216)

Figure 3 Distribution of patient’s usual medication discrepancies on
discharge prescriptions.

0%
10%
20%
30%
40%
50%
60%
70%
80%
90%

100%
P
e
rc
e
n
ta
g
e
Discrepancy Type

Phase 2 (n=75) Phase 1 (n=93)

Figure 4 Distribution of new discrepancies on discharge prescriptions.

Pharmacist-led medication reconciliation 171

The potential clinical significance of the discharge med-
ication discrepancies was not evaluated and is a limitation
of this study. A systematic review on the impact of medica-
tion reconciliation and review on clinical outcomes found
evidence to demonstrate that medication reconciliation
hasthepotentialtoidentifymanymedicationdiscrepancies
and reduce potential harm, but the impact on actual clinical
outcomes, such as reductions in hospital readmissions, is
less clear.5 Of all the medication discrepancies that were
assessed in the systematic review for their reported poten-
tial impact on clinical outcomes, 18.4 –80.5% had the poten-
tial to cause no or minimal harm, 17.9–78.1% had the
potential to cause moderate harm, and 0–24.3% had the
potential to cause severe or life-threatening harm.5 One
study found a significant reduction in the 30-day rate of
hospital readmission (17.8–12.3%, p = 0.042) in high-risk
patients, after implementation of a team-based medication
reconciliationattransitionsofcare,whichcouldyielddirect
cost savings.18

The change in the medication reconciliation process
resulted in a reduction in medical staff workload, shown
by a 22% absolute reduction in the number of telephone
calls to medical officers and a decrease from 37 s/medica-
tion order (IQR, 29–48; n = 51) to 21 s/medication order
(IQR, 11–35; n = 35) in the time taken for the medical officer
tocompletetheelectronicmedicationform.Thelatterresult
has limitations as medical officers were not aware that the
timeline in the patient’s eMR would be used to determine
how long they took to complete the medication form and
they may have been interrupted. Also, for almost a quarter
of the discharges in phase 1 (15/66) and a third of dis-
charges in phase 2 (17/52), we could not determine the time
taken by the medical officers to complete the medication
form on the patient’s eMR as it had been opened on multi-
ple occasions and/or over multiple days. Despite this, a
studyin2011showed similarresults:pharmacytechnicians
entering electronic medication histories on admission
reduced the average time spent by doctors completing
the discharge paperwork from an average of 30 to 23 min
per discharge (p = 0.007).19

The observeddifference intime takenbytheED pharma-
cists to take and record a BPMH when completing a hand-
written National Medication Management Plan and
electronic recording on a medication form in eMR was
found to benon-significant. Pharmacists recordingmedica-
tionhistoriesineMRandreconciling theprintedcopy ofthe
electronic BPMH against the NIMC on admission is a rela-
tively new and slightly different process. Although some
initial delays were expected, they were not observed in this
study. It is expected as the data repository expands over
time and voice activation devices being currently trialled
in our ED have increased the uptake, the electronic process
will, in the long run, be significantly less time-consuming.

The change in process potentially results in more efficient
use of pharmacist time, especially for patients seen by
ED pharmacists who are discharged or transferred to
another hospital, as the electronically recorded BPMH
can be used by another hospital or used locally if the
patient is readmitted at a later date. It also resulted in a
clear and concise electronic medication reconciliation
form that is printed and available at the bedside with
the NIMC, as well as electronically avoiding poor hand-
writing issues.

The difference in time taken for the clinical pharmacist to
reconcile the discharge medication was non-significant
despite the lower rate of medication discrepancies to
resolve and fewer telephone calls to the medical officers
in phase 2.

Although this integrated approach to medication recon-
ciliation has highlighted clear patient safety benefits
while reducing clinician (medical and pharmacy) work-
load there is still a long way to go to reduce unintentional
medication discrepancies at discharge from hospital to
zero. Unfortunately, prescribing on discharge remains a
process with ongoing potential significant risk for patient
harm and pharmacist discharge reconciliation, and collab-
oration with prescribers remains an important safety
measure.

The modified process tested in this study has the poten-
tial to be the basic framework for the introduction of a med-
ication reconciliation component in any future integrated
electronic medication management solution. This study
highlights the importance of enhanced multidisciplinary
focus on medication reconciliation, ongoing education of
the medical officers and better utilisation of eMR to reduce
errors further. As medication reconciliation is a mandatory
criteria for EQuIP20 National Accreditation Survey 4,
National Safety and Quality in Healthcare Standards (crite-
rion 4.6), it is important for organisations to adopt system-
based approaches that allow them to benchmark and mon-
itor the progress of this important safety indicator. Elec-
tronic recording of this information can provide an
opportunity to have access to real-time data related to med-
ication reconciliation as well as to reduce the potential bur-
den of labour-intensive auditing.

ACKNOWLEDGEMENTS

The author thanks contributing pharmacists at the
Northern Beaches Health Service for support with data col-
lection. In addition, the author thanks Jillian Patterson
(Biostatistician, Kolling Institute, Royal North Shore
Hospital, St Leonards) for guidance and assistance with
data analysis.

172 D. S. Lawrence et al.

Competing interests

None declared.

REFERENCES

1 Duguid M. The importance of medication reconciliation for patients
and practitioners. Aust Prescr 2012; 35: 15–19.

2 Australian Pharmaceutical Advisory Council (APAC). Guiding
principles to achieve continuity in medication management. APAC
Secretariat: Canberra; 2005.

3 Australian Commission on Safety and Quality in Health Care.
Medication reconciliation. Available from .
Accessed 19 October 2014.

4 Thompson-Moore N, Liebel MG. Health care system vulnerabilities:
understanding the root causes of patient harm. Am J Health Syst Pharm
2012; 69: 431–6.

5 Lehnbom E, Stewart MJ, Manias E, Westbrook JI. Impact of
medication reconciliation and review on clinical outcomes.
Ann Pharmacother 2014; 48: 1298–312.

6 Fitzgerald C. Local audit. Discharge medication reconciliation: is the
view any better at the far end? Mona Vale: Northern Beaches Health
Service, Mona Vale Hospital; 2011.

7 Sumpter M. Local audit. Northern Beaches Health Service, Manly
Hospital; 2012.

8 Garrett T, McCormack C. Does an electronic discharge referral system
improve the quality of medication prescribing? J Pharm Pract Res 2014;
44: 29–34.

9 Ng C, Welch SA, Luddington J, Bui D, Glasson E, Richardson KL.
Medication reconciliation challenges at discharge from hospital using
anelectronic medicationmanagementsystemandelectronic discharge
summaries. J Pharm Pract Res 2013; 43: 25–8.

10 Callen J, McIntosh J, Li J. Accuracy of medication documentation
in hospital discharge summaries: a retrospective analysis of
medication transcription errors in manual and electronic discharge
summaries. Int J Med Inform 2010; 79: 58–64.

11 Forum AP. Report of the 2013 AMCP partnership forum on electronic
solutions to medication reconciliation and improving transitions of
care. J Manage Care Pharm 2014; 20: 937–47.

12 Burridge N. SHPA standards of practice for the provision of mediation
reconciliation. J Pharm Pract Res 2007; 37: 231–3.

13 Wong JD, Barcar M, Wong CG, Alibhai SM, Huh JH, Cesta A, et al.
Medication reconciliation at hospital discharge: evaluating
discrepancies. Ann Pharmacother 2008; 42: 1373–9.

14 Cornu P, Steurbaut S, Leysen T, Baere ED, Ligneel C, Mets T, et al.
Discrepancies in medication information for the primary care
physician and the geriatric patient at discharge. Ann Pharmacother
2012; 46: 983–90.

15 Stewart AL, Lynch KJ. Identifying discrepancies in electronic medical
records through pharmacist medication reconciliation 2012. J Am
Pharm Assoc 2012; 52: 59–66.

16 Cornu P, Steurbaut S, Leysen T, De Baere E, Ligneel C, Mets T, et al.
Effect of medication reconciliation at hospital admission on
medication discrepancies during hospitalization and at discharge for
geriatric patients. Ann Pharmacother 2012; 46: 484–94.

17 Grimes TC. Medication details documented on hospital
discharge: cross-sectional observational study of factors
associated with mediation non-reconciliation. Br J Clin Pharmacol 2011;
71: 449–57.

18 Anderegg SV, Wilkinson ST, Couldry RJ, Grauer DW, Howser E.
Effects of a hospitalwide pharmacy practice model change on
readmission and return to emergency department rates. Am J Health
Syst Pharm 2014; 71: 1469–79.

19 Sherer AP, Cooper JB. Electronic medication reconciliation: a
new prescription for the medications matching headache. Heart
Lung 2011; 40: 372–3. 7th Annual Conference of the American
Association of Heart Failure Nurses, AAHFN 2011, July-August 2011,
Seattle, WA.

20 The Australian Council on Healthcare Standards. Available from
. Accessed
15 January 2015.

Received: 21 May 2014
Revised version received: 31 January 2015
Accepted: 19 February 2015

Pharmacist-led medication reconciliation 173

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Impact of pharmacist-led medication reconciliation on admission using electronic medical records on accuracy of discharge …

INTRODUCTION
METHODS
Phase 1 (Normal Process)
Phase 2 (Intervention Process)
Data Analysis
RESULTS
Admission Medication Reconciliation
Discharge Medication Reconciliation
Time on Discharge
DISCUSSION
ACKNOWLEDGEMENTS
Competing interests
REFERENCES

Contents lists available at ScienceDirect

Research in Social and Administrative Pharmacy

journal homepage: www.elsevier.com/locate/rsap

Impact of medication reconciliation on health outcomes: An overview of
systematic review

A.B. Guisado-Gila,b,∗, M. Mejías-Truebaa, E.R. Alfaro-Laraa, M. Sánchez-Hidalgob,
N. Ramírez-Duquec, M.D. Santos-Rubiod
a Unidad de Gestión Clínica Farmacia. Hospital Universitario Virgen del Rocío, Sevilla, Spai

b Departamento de Farmacología. Facultad de Farmacia, Universidad de Sevilla, Sevilla, Spain
c Unidad de Gestión Clínica Medicina Interna. Hospital Universitario Virgen del Rocío, Sevilla, Spain
d Unidad de Gestión Clínica Farmacia. Hospital Juan Ramón Jiménez, Huelva, Spain

A R T I C L E I N F O

Keywords:
Systematic revie

Medication reconciliati

Outcome assessment
Evidence-based practice

A B S T R A C T

Background: Recent systematic reviews and meta-analyses suggest that medication reconciliation (MR) is ef-
fective in decreasing the risk of medication discrepancies. Nevertheless, the association between MR and sub-
sequent improved healthcare outcomes is not well established.
Objectives: This systematic review of reviews set out to identify published systematic reviews on the impact of
MR programs on health outcomes and to describe key components of the intervention, the health outcom

assessed and any associations between MR and health outcomes.
Methods: PubMed, EMBASE, Cochrane Library, Cumulative Index to Nursing and Allied Health Literature
(CINAHL) and SCOPUS were searched from inception to May

019. Systematic reviews of all study designs,
populations, intervention providers and settings that measured patient-related outcomes or healthcare utiliza-
tion were considered. Methodological quality was assessed using A Measurement Tool to Assess Systematic Reviews
2 (AMSTAR 2). Two investigators performed study selection, quality assessment and data collection in-
dependently.
Results: Five systematic reviews met the inclusion criteria: 2 were rated as low quality and

as critically low
quality. Reviews included primary studies in different settings (hospitals, the community and residential aged
care facilities) that reported the impact of MR on mortality, length of stay, Emergency Department (ED) visits,
readmissions, physician visits and healthcare utilization. Only one review reported results on mortality.
However, healthcare utilization, which usually included ED visits and readmissions, was communicated in al

reviews. Meta-analyses were conducted in all reviews except one. Medication reconciliation was not consistently
found to be associated with improvements in health outcomes.
Conclusions: Few systematic reviews support the value of MR in achieving good patient-related outcomes and
healthcare utilization improvements. The quality of the systematic reviews was low and the primary studies
included commonly involved additional activities related to MR. There was no clear evidence in favor of in-
tervention in mortality, length of stay, ED visits, unplanned readmissions, physician visits and healthcare uti-
lization.

Introduction

Care transitions are described as changes in care settings. Poor
quality transitions may result in risks to patients’ safety, discontinuity
in care plans and patient dissatisfaction with care.1 Therefore, transi-
tional care interventions encourage positive healthcare goals,2 require
coordination with healthcare professionals in both primary and sec-
ondary care, and provide patients with accessible information on post-

transition.3 In this context, the process of medication reconciliation
should account for any alteration made in the medication taken by
patients, and should ensure that patients or their caregivers have been
made aware of these alterations.

According to the Institute for Healthcare Improvement, medication
reconciliation (MR) refers to the process of identifying the most accu-
rate list of all medications a patient is taking and using this list to
provide correct medications for patients anywhere within the health

https://doi.org/10.101

/j.sapharm.2019.10.011
Received 13 July 2019; Received in revised form 1 October 2019; Accepted 1

October 2019

∗ Corresponding author. Unidad de Gestión Clínica Farmacia. Hospital Universitario Virgen del Rocío, Avenida Manuel Siurot, 41013, Sevilla, Spain.
E-mail address: anab.guisado.sspa@juntadeandalucia.es (A.B. Guisado-Gil).

Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx

1551-

Please cite this article as: A.B. Guisado-Gil, et al., Research in Social and Administrative Pharmacy,
https://doi.org/10.1016/j.sapharm.2019.10.011

http://www.sciencedirect.com/science/journal/15517411

https://www.elsevier.com/locate/rsap

https://doi.org/10.1016/j.sapharm.2019.10.011

mailto:anab.guisado.sspa@juntadeandalucia.es

https://doi.org/10.1016/j.sapharm.2019.10.011

system.4 This review process identifies medication discrepancies. Un-
intended medication discrepancies that represent reconciliation errors
are responsible for more than half the medication errors occurring
during transitions in care, and up to one-third could potentially cause
harm.5

Previous primary research studies have evaluated the effect of MR
on medication discrepancies, patient-related outcomes and healthcare
utilization during care transitions. However, interpreting the evidence
in relation to the impact of MR is a challenge due to the variation in
study designs, interventions and settings. In recent years, a growing
number of systematic reviews and meta-analyses relevant to the impact
of MR on health outcomes have been published. Their results suggest
that MR provided by pharmacists is effective in decreasing the risk of
medication discrepancies.6–

Nevertheless, the association between MR
and the improvement in health outcomes, while plausible, is not well
established. An overview of systematic reviews can play a role in
summarizing existing research or highlighting the absence of evidence,
improving access to specific information and supporting decision-
making by clinicians, policy makers and developers of clinical guide-
lines.9 The Cochrane Collaboration recommends an overview of sys-
tematic reviews to summarize the evidence of existing systematic re-
views that address different outcomes for a single intervention.10

A previous overview of systematic reviews has measured the impact
of MR on health outcomes.11 This overview, conducted by Holte et al.
and published in 2015, considered health-related outcomes (read-
missions, adverse events and unwanted events), but also outcomes re-
lated to the process of performing medication reconciliation (percen-
tage performing medication reconciliation and medication
discrepancies). It included seven moderate-quality systematic reviews
which concluded that MR probably reduces the number of medication
discrepancies, but that the impact on clinical outcomes is unclear.
Neither publication specifically focuses on the impact of MR on patient-
related outcomes and healthcare utilization. Moreover, this overview
did not consider recent systematic reviews of MR.

The objectives of this overview were to identify published sys-
tematic reviews on the impact of MR programs on health outcomes and
to describe key components of the intervention, the health outcomes
assessed and any associations between MR and health outcomes.

Methods

Eligibility criteria

This systematic review was conducted in accordance with the
Preferred Reporting Items for Systematic Reviews and Meta-Analyses
(PRISMA) guidelines.12 The completed PRISMA checklist is included as
Supplementary File 1. Inclusion criteria according to PICOS (Popula-
tion, Intervention, Comparison, Outcome and Study design) for the
systematic review were:

Population: adults and/or pediatric patients experiencing a

transition of care.
Intervention: medication reconciliation. The intervention involved a
healthcare professional and was performed before, during or after a
care transition.
Comparison: a control group that received usual care.
Outcomes (at least one of the following): patient-related outcomes
(mortality) and healthcare utilization (length of stay, unplanned
readmissions, Emergency Department visits and/or primary or sec-
ondary care visits).
Study design: systematic reviews.

We excluded reviews investigating additional interventions not fo-
cused on MR. Those exclusively reporting other outcomes (medication
discrepancies, adverse drug events [ADEs] with potential to cause in-
jury, preventable ADEs, medication adherence and unanticipated in-
creased workload) were also excluded.

Information sources

An electronic literature search was performed using 5 Healthcare
Databases: PubMed, EMBASE, Cochrane Library, Cumulative Index to
Nursing and Allied Health Literature (CINAHL) and SCOPUS, with no
language or publication date restrictions up to 30 September 2018.
Search terms included a mixture of MeSH terms and free text (key-
words, synonyms and word variations) combined with Boolean opera-
tors. Filters were used to limit the results of the search to systematic
reviews (see Table 1 for the complete search strategy). The last full
search was run on 1 May 2019 in order to identify new results.

Study selection

Two independent reviewers (ABGG and MMT) screened the titles
and abstracts of all eligible reviews for possible inclusion, with any
disagreements settled by consensus or with a third reviewer (ERAL).
Where there was uncertainty, full-length publications were evaluated
before a final decision on inclusion was made.

Quality assessment

The quality-assessment tool known as A Measurement Tool to Assess
Systematic Reviews 2 (AMSTAR 2)13 was used to assess the quality and
risk of bias in the studies included. It consists of 16 items whose re-
sponse options were “yes”, “no” or “partial yes”. Items 4, 7, 11, 13 and
15 are considered critical domains. The overall quality can be rated as
high (no or one non-critical weakness), moderate (more than one non-
critical weakness), low (one critical flaw with or without non-critical
weakness), and critically low (more than one critical flaw with or
without non-critical weakness). Two independent reviewers (ABGG and
MMT) conducted the quality assessment, and any disagreements on
quality ratings between reviewers were discussed and a consensus
reached.

Table 1
Complete search strategy for different databases.

Healthcare Databases Search strategy

PubMed (“medication discrepancies” [All Fields] OR “reconciliation discrepancies” [All Fields] OR (“medication reconciliation” [MeSH Terms] OR “medication
reconciliation” [All Fields])) AND ((“impact” [All Fields] OR “health outcomes” [All Fields] OR “Health Impact Assessment” [Mesh] AND systematic
[sb])

)

EMBASE (“health outcomes”/exp OR “health outcomes”) AND (“medication therapy management”/exp OR “medication therapy management”) AND [systematic
review]/lim

Cochrane Library “effect”:ti,ab, kw and “reconciliation”:ti,ab,kw
Limits: Cochrane Reviews

CINAHL AB (medication reconciliation) AND (AB health outcomes OR AB impact or effect or influence or AB outcome or result or consequenc

Type of publication: systematic review

SCOPUS (impact OR effect OR health AND outcome) AND (medication AND reconciliation OR reconciliation AND discrepancies) AND (LIMIT-TO
(DOCTYPE,“re”)) AND (LIMIT-TO (SUBJAREA,“HEAL”))

A.B. Guisado-Gil, et al. Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx

Data collection

Reviewer ABGG independently extracted data and MMT examined
all extraction sheets to ensure their accuracy. We also directly com-
municated with the authors to obtain details not included in the pub-
lished reports. If there were any missing data from a review, we ex-
plicitly stated this. For each systematic review, the following variables
were registered:

– Author and year of publication.
– Aim of systematic review.
– Number of primary studies.
– Design of primary studies: randomized controlled trial (RCT), pro-

spective controlled trial, before-and-after, cohort study, observa-
tional post-intervention, retrospective observational and cross-sec-
tional studies.

– Number of participants.
– Type of participants: adults and/or pediatric patients.
– Settings and transitions of care involved.
– Intervention providers: pharmacists, nurses and/or physicians.
– Key components of intervention.
– Health outcomes: mortality, length of stay, unplanned readmissions,

Emergency Department visits and primary and secondary care visits.
– Other outcomes collected in systematic reviews: medication dis-

crepancies, ADEs with potential to cause injury, preventable ADEs,
medication adherence and unanticipated increased workload.

Both narrative findings and meta-analyses of primary study data
included in the systematic reviews were synthesized. The measures of
association between MR and health outcomes included the risk ratio
(RR) and difference in means (MD), with consistency (I2) reported by
individual reviews and meta-analyses.

Resul

The electronic search revealed 86 citations; 8 were removed using
Mendeley via duplicate checking. Additionally, 56 reviews were ex-
cluded following title and abstract filtering because they did not meet
the eligibility criteria. This left 22 reviews that were potentially re-
levant and retrieved in full-text: 17 were excluded before data extrac-
tion and 5 met the inclusion criteria (Fig. 1). A list of the 17 publica-
tions excluded after full-text evaluation and the reasons for exclusion
are provided in Supplementary File 2.

Quality of the systematic reviews

Table 2 reports on the AMSTAR 2 response option for each domain.
The overall quality of the included studies assessed with AMSTAR 2 was
poor. Of the 5 reviews, none was rated as high or moderate quality, 2
were rated as low quality,15,17 and 3 were critically low quality.7,14,16

All reviews presented similarities with respect to responses in cri-
tical and non-critical domains, except for Lehnbom et al.14 who did not
carry out a meta-analysis. Regarding non-critical flaws, none showed
that they had worked with a written protocol with independent ver-
ification before the review was undertaken (item 2), or documented the
funding sources for each study included in the review (item 10), except
Redmond et al.17 As for critical flaws, none provided a complete list of
potentially relevant studies with justification for the exclusion each one
(item 7). Redmond et al.17 only included a selection of their excluded
articles. In addition, Lehnbom et al.14 did not assess the risk of bias
(RoB) in individual studies or include a discussion of its impact on the
interpretation of the results (item 13) and, together with the other two
critically low quality reviews,7,16 did not perform a sensitivity analysis
to determine publication of bias (item 15).

Characteristics of included systematic reviews and meta-analyses

Full details of the included studies are shown in Table 3. All the
review articles7,14–17 aimed to identify MR interventions and to test
their association with clinical outcomes. One of them14 also evaluated
separately the effectiveness of medication review. They included vari-
able numbers of primary studies: Kwan et al.7 18 studies, Lehnbom
et al.14 40 studies, McNab et al.15 14 studies, Cheema et al.16 18 studies
and Redmond et al.17 25 studies. The 5 reviews cited a combined total
of 87 original research articles: 40 RCTs, 4 prospective controlled trials,
7 before-and-after, 9 cohort studies, 22 observational post-intervention,
3 retrospective observational and 2 cross-sectional studies. In total,
65912 patients were studied, with people recruited from hospitals, from
the community and from residential aged care facilities (RACF). Despite
contacting the authors, McNab et al.15 were unable to provide data on
the number of patients enrolled and the number of participants under
18, in two studies included in their review.

Pharmacists were primarily responsible for delivering the inter-
vention, working closely with other healthcare professionals (physi-
cians and nurses) in some cases.7,14,17 Interventions beyond MR in-
cluded patient counselling,7,14,16,17 creation of post-discharge
medication lists,7,14,16,17 post-discharge communication7,14,17 and
medication review.17 Reviews reported the control group’s intervention
to consist of usual care in the context in which the study took place,
except in two studies18,19 included in Kwan et al.’s publication,7 which
compared two forms of MR.

The reviews included a combined total of 37 primary research
studies that reported the impact of MR on different health outcomes:
mortality,17 length of stay,14,17 Emergency Department visits,14,15,17

readmissions14,15,17 and physician visits,14,15 without restrictions on
follow-up periods. All reviews7,14–17 also studied the impact of MR on
healthcare utilization, a composite variable made up of two or more
health measures. Meta-analysis was conducted except in one case14 if
available data allowed pooling of results, and the variables measured
were homogenous. Additional outcomes identified were: medication
discrepancies,14–17 ADEs with potential to cause injury,7,16 preventable
ADEs,16,17 medication adherence17 and primary care workload.15

Effectiveness of medication reconciliation on health outcomes (see Table 4)

Mortality
One review17 reported mortality with no statistically significant

differences between MR and standard care (RR 0.75, 95% CI 0.27 to
2.08) based on the results of one RCT.

Length of stay
Two reviews14,17 reported no statistically significant differences

between MR in hospitals and standard care regarding length of stay.
Pooled results of 2 RCTs included in the meta-analysis performed by
Redmond et al.17 were MD 0.48 (95% CI -1.04 to 1.99) with some
evidence of heterogeneity between these studies (I2 = 52%; p = 0.15).
Medication reconciliation in RACF demonstrated significantly shorter
hospital stays (p = 0.026) according to one before-and-after study in-
cluded by Lehnbom et al.14

Emergency Department visits
In relation to the hospital setting, Lehnbom et al.14 communicated

no statistically significant differences regarding ED visits within 72 h,
14 days or 30 days after discharge based on the results of one before-
and-after study, whereas Redmond et al.17 included the results of one
small RCT with a moderate risk of bias that reported reduced ED visits
within 30 days after discharge (RR 0.07, 95% CI 0.00 to 1.07). On the
other hand, McNab et al.15 included 2 non-RCTs with no difference
observed between groups, indicating no clear effect of MR on ED visit
rates.

A.B. Guisado-Gil, et al. Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
3

Unplanned readmissions
Two reviews14,17 communicated no statistically significant differ-

ences regarding unplanned rehospitalization rates in the hospital set-
ting. The pooled results of 5 RCTs included in the meta-analysis per-
formed by Redmond et al.17 were RR 0.72 (95% CI 0.44 to 1.18) with a
follow-up range of 5–30 days and some evidence of heterogeneity be-
tween these studies (I2 = 45%; p = 0.12). In the community setting,
Lehnbom et al.14 included one cohort study where the readmissions rate
decreased at 7 (p = 0.01) and 14 days (p = 0.04) but not at 30 days
(p = 0.29) after discharge. McNab et al.15 included 7 studies in the
meta-analysis (4 RCTs and 3 cohort studies), and the pooled RR was
0.91 (95% CI 0.66 to 1.25) with high heterogeneity (I2 = 71%;
p = 0.002). The follow-up period was 30 days, except in 2 RCTs in
which it was 6 months.

Physician visits
One prospective controlled study in the community setting included

by Lehnbom et al.14 found that control patients had a lower rate of
discrepancy resolution and reported no significant trend towards more
planned and unplanned physician visits compared with intervention
patients. One RCT in McNab et al.’s review15 reported a significant
increase in general practitioner visits of 43% (p = 0.002) in the MR
group, while another RCT reported no significant difference at 6
months.

Healthcare utilization
Healthcare utilization after hospital discharges usually included ED

visits and readmissions. In this respect, reported data from 4 RCTs in-
cluded in Cheema et al.’s meta-analysis16 showed no significant

reduction in favor of the MR group, with a pooled RR of 0.78 (95% CI
0.61 to 1.00) and no heterogeneity between the studies (I2 = 0%;
p = 0.54). Four RCTs included in Redmond et al.’s meta-analysis17 re-
ported no certainty of the effect of the intervention (RR 0.78, 95% CI
0.50 to 1.22) and some evidence of heterogeneity (I2 = 48%; p = 0.12).
On the contrary, the pooled results from 3 RCTs from Kwan et al.’s
meta-analysis7 achieved a statistically significant reduction in health-
care usage (RR 0.77, 95% CI 0.63 to 0.95), but heterogeneity was not
assessed. Medication reconciliation by a clinical pharmacist at admis-
sion, and medication review by a clinical pharmacist during hospitali-
zation, also offered no improvements in terms of number of ED visits,
hospital readmissions and mortality rates compared with standard care,
according to one prospective controlled study included by Lehnbom
et al.14 However, in RACF one RCT reported fewer ED visits and
readmissions (p = 0.035) when compared to control patients. McNab
et al.15 communicated the results of 2 articles (1 RCT and 1 before-and-
after study) which measured healthcare utilization as readmissions and
ED or general practitioner visits, with no statistically significant dif-
ferences between groups.

Discussion

This is the first overview of systematic reviews that specifically fo-
cuses on the impact of MR on patient-related outcomes and healthcare
utilization. Two independent reviewers systematically reviewed the
literature with no publication date or language restrictions, and eval-
uated the quality of the systematic reviews using the review instrument
AMSTAR 213 validated for randomized or non-randomized studies of
healthcare interventions, and extracted data from the publications

Fig. 1. Preferred Reporting Items for Systematic Reviews and Meta-Analyses literature search and study selection flowchart.12

A.B. Guisado-Gil, et al. Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
4

T
ab
le
2

Q
ua

it
y

of
sy

st
em

at
ic

re
vi

ew
s

ba
se

d
on

th
e

16
-i
te

m
A

M
ST

A
R

C
he

ck
lis

t.
1
3

It
em

K
w

an
et

.
20

13
7

Le
hn

o
m

et
al

.
20

14
1
4

M
cN

ab
et

al
.

20
18

1
5

C
he

em
a

et
al

.2
01

81
6

R
ed

m
on

d
et

al
.
20

1
7

1.
D

d
th

e
re

se
ar

ch
qu

es
ti

on
s

an
d

in
cl

us
io

n
cr

it
er

ia
fo

r
th

e
re

vi
ew

in
cl

ud
e

th
e

co
m

po
ne

nt
s

of
PI

C
O

Y
es

Y
es
Y
es
Y
es
Y
es

2.
D

id
th

e
re

f
th

e
re
vi
ew

co
nt

ai
n

an
ex

pl
ic

it
st

at
em

en
t

th
at

th
e
re
vi

ew
m

et
ho

s
w

er
e

es
ta

bl
is

he
d

pr
io

r
to

th
e

co
nd

uc
t

of
th

e
re
vi
ew

,
an

,
di

d
th
e
re
po
rt

ju
st

if
y

an
y

si
gn

fi
ca

nt
de

vi
at

io
ns

fr
om

th
e

pr
ot

oc
ol

?
Pa

rt
ia

l
ye

s
Pa

rt
ia
l
ye
s
Pa
rt
ia
l
ye
s
Pa
rt
ia
l
ye

s
Y
es

3.
D

id
th
e
re
vi
ew

au
th

or
s

ex
pl

ai
n

th
ei

r
se

le
ct

io
n

of
th

e
st

ud
y

de
si

gn
s

fo
r

in
cl
us
io

n
in

th
e
re
vi

ew
?

Y
es
Y
es
Y
es
Y
es
Y
es

4.
D

id
th
e
re
vi
ew
au
th
or
s

us
e

a
co

m
pr

eh
en

si
ve

lit
er

at
ur

e
se

ar
ch

st
ra

te
gy

?
Y
es

Y
es
Y
es
Y
es
Y
es

5.
D

id
th
e
re
vi
ew
au
th
or
s

pe
rf

or
m

st
ud

y
se

le
ct
io
n

in
du

pl
ic

at
e?

Y
es
Y
es
Y
es
Y
es
Y
es

6.
D

id
th
e
re
vi
ew
au
th
or
s
pe
rf
or
m

da
ta

ex
tr

ac
ti

on
in

du
pl

ic
at

e?
Y
es

Y
es
Y
es
Y
es
Y
es

7.
D

id
th
e
re
vi
ew
au
th
or
s

pr
ov

id
e

a
lis

t
of

ex
cl

ud
ed

st
ud

ie
s

an
d
ju
st
if
y
th
e
ex
cl
us
io

ns
?

N
o

N
o
N
o
N
o
N
o

8.
D

id
th
e
re
vi
ew
au
th
or
s

de
sc

ri
be

th
e
in
cl
ud
ed
st
ud
ie
s

n
ad

eq
ua

te
de

ta
il?

Y
es
Y
es
Y
es
Y
es
Y
es

9.
D

id
th
e
re
vi
ew
au
th
or
s
us
e

a
sa

ti
sf

ac
to

ry
te

ch
ni

qu
e

fo
r

as
se

ss
in

g
th

e
R

oB
in

n
di

vi
du

al
st

ud
ie

s
th

at
w

er
e
in
cl
ud
ed

in
th

e
re
vi
ew
?
Y
es
N
o
Y
es
Y
es
Y
es

10
.
D

id
th
e
re
vi
ew
au
th
or
s

e
po

rt
th

e
so

ur
ce

s
of

fu
nd

in
g

fo
r
th
e
st
ud
ie
s
in
cl
ud
ed
in
th
e
re
vi
ew

?
N

o
N

o
N
o
N

o
Y
es

11
.I

f
m

et
a-

an
al

ys
is

w
as

pe
rf
or
m

ed
,
di

d
th
e
re
vi
ew
au
th
or
s
us
e

ap
pr

op
ri

at
e

m
et

ho
ds

fo
r
th
e

st
at

is
ti

ca
l
co

m
bi

na
ti

n
of

re
su

lt
s?

Y
es
N
o
m
et

a-
an

al
ys

is
co

nd
uc

te
d

Y
es
Y
es
Y
es

12
.I

f
m
et
a-
an
al
ys
is
w
as
pe
rf
or
m

ed
,d

id
th
e
re
vi
ew
au
th
or
s
as
se

ss
th

e
po

te
nt

ia
l
im

pa
ct

of
R

oB
in
in
di
vi
du
al
st
ud
ie

s
on

th
e
re
su

lt
s

of
th

e
m

et
a-
an
al
ys
is

or
ot

he
r

ev
id

en
ce

sy
nt

he
si

s?
Y
es

N
o
m
et
a-
an
al
ys
is
co
nd
uc
te
d
Y
es
Y
es
Y
es

13
.D

id
th
e
re
vi
ew
au
th
or
s

ac
co

un
tf

or
R

oB
in
in
di
vi
du
al
st
ud
ie
s
w

he
n

in
te

rp
re

ti
ng

/d
is

cu
ss

in
g
th
e
re
su
lt
s
of
th
e
re
vi
ew
?
Y
es
N
o
Y
es
Y
es
Y
es

14
.D

id
th
e
re
vi
ew
au
th
or
s
pr
ov
id
e
a
sa
ti
sf
ac
to

ry
ex

pl
an

at
io

n
fo

r,
an

d
di

sc
us

si
on

of
,a

ny
he

te
ro

ge
ne

it
y

ob
se

rv
ed

in
th
e
re

su
lt
s

of
th
e
re
vi
ew
?
Y
es
Y
es
Y
es
Y
es
Y
es

15
.I

f
th

ey
pe

rf
or

m
ed

qu
an

ti
ta

ti
ve

sy
nt
he
si

s,
di

d
th
e
re
vi
ew
au
th
or
s

ca
rr

y
ou

t
an

ad
eq

ua
te

in
ve

st
ig

at
io
n
of

pu
bl

ic
at
io
n

bi
as

(s
m

al
l
st

ud
y
bi
as

)
an

d
di
sc
us

s
it

s
lik

el
y

im
pa

ct
on

th
e
re
su
lt
s
of
th
e
re
vi
ew
?
N
o
N
o
m
et
a-
an
al
ys
is
co
nd

uc
te

d
Y
es

N
o
Y
es

16
.D

id
th
e
re
vi
ew
au
th
or
s

re
po

rt
an

y
po

te
nt

ia
l
so

ur
ce
s
of

co
nfl

ic
t

of
in

te
re

st
,i

nc
lu

di
ng

an
y
fu
nd
in
g

th
ey

re
ce

iv
ed

fo
r
co
nd

uc
ti

ng
th

e
re
vi
ew
?
Y
es
Y
es
Y
es
Y
es
Y
es
Q
ua

lit
y

le
ve

l
C
ri

ti
ca

lly
lo

w
C
ri

ti
ca
lly
lo

w
Lo

w
C
ri
ti
ca
lly
lo
w
Lo
w

a A
M

ST
A

R
=

A
ss

es
sm

en
t

of
M

ul
ti

pl
e

Sy
st

em
at

ic
R

ev
ie

w
s.

PI
C
O

=
Po

pu
la

ti
on

,
In

te
rv

en
ti

on
,

C
om

pa
ri

so
n,

O
ut

co
m

.
R

oB
=

ri
sk

of
bi

as
.

A.B. Guisado-Gil, et al. Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
5

T
ab
le
3

Su
m

m
ar

y
of

ch
ar

ac
te

ri
st

ic
s

of
se

le
ct

ed
sy

st
em
at
ic
re
vi
ew
s
an
d
m
et
a-
an
al
ys

es
.

A
ut

ho
r,

ye
ar

A
im

N
um

be
r

an
d
de
si

gn
of

st
ud
ie
s
N
um
be
r
an
d

ty
pe

of
pa

rt
ic

ip
an

Se
tt

in
g

(t
ra

ns
it

io
n

po
in

t)
In

te
rv
en
ti

on
pr

ov
id

er
In

te
rv
en
ti

on
H

ea
lt
h

ou
tc

om
es

O
th

er
ou

tc
om

es
K
w
an
et
al
.

20
13

7
To

su
m

m
ar

iz
e

ev
id
en
ce

on
th

e
eff

ec
ti

ve
ne

ss
of

ho
sp

it
al

-b
as

ed
M

R
in

te
rv
en
ti
on
s

5
R

C
Ts

4
be

fo
re

-a
nd

-a
ft

er
9

po
st

–
in

te
rv
en
ti
on

39
17

0
ad

ul
ts

29
5

pe
di

at
ri

c
pa

ti
en

H
os

pi
ta

l
(a

m
is

si
on
,
di

sc
ha

rg
e

ho
m

e,
in

–
ho

sp
it

al
tr

an
sf

er
)

Ph
ar

m
ac

is
ts

A
ls

o
ph

ys
ic

ia
ns

an
d

nu
rs

M
R

Pa
ti

en
t

co
un

se
lli

ng
C
re

at
io
n
of
po
st

–
di

sc
ha
rg
e
m
ed
ic
at
io
n

lis
ts

Po
st

-d
is

ch
ar

e
co

m
m

un
ic

at
io
n

H
ea

lt
hc

ar
e

ut
ili

za
ti

on
(E

m
er

ge
nc

y
D

ep
ar

tm
en

t
vi

si
ts

an
d

re
ad

m
is
si
on

w
it

hi
n

30
da

ys
)

C
lin

ic
al

ly
si

gn
ifi

ca
nt

un
in

te
nd

d
m

ed
ic

at
io
n
di

sc
re

pa
nc

ie
s

(e
qu

l
to

A
D

it
h

po
te

nt
ia

l
to

ca
us

e
in

ju
ry

)
Le
hn

bo
m

et
al
.
20
14
1
4

To
ev

al
ua

te
ho

w
eff

ec
ti

ve
M

R
an

d
m
ed
ic
at
io

n
re

vi
ew
ar
e

in
id

en
ti

fy
in

g
an

d
re

ct
if
yi

ng
ha

rm
fu

l
di

sc
re
pa
nc
ie
s
an
d
m
ed
ic
at

io
n-

re
la

te
d

pr
ob

le
m

s,
an

d
to

as
se
ss
th

ei
r

im
pa
ct
on

cl
in

ic
al
ou
tc
om
es
5
R
C
Ts

4
pr

os
pe

ct
iv

e
co

nt
ro

le
d

2
be

fo
re
-a
nd
-a
ft

er
3

co
ho

rt
21

po
st
–
in
te
rv
en
ti

on
3

re
tr

os
pe
ct
iv

e
ob

se
rv

at
io

na
l

2
cr

os
s-

se
ct

io
na

19
15

8
ad

ul
ts
H
os
pi
ta
l
(a

dm
is

si
on
,
di
sc
ha
rg
e
ho
m
e,
in
–
ho
sp
it
al
tr
an
sf
er
)
C
om

m
un

it
y

(d
is

ch
ar

ge
fr

om
ho

sp
it

al
to

ho
m

e)
R

A
C
F

(a
dm

is
si

on
fr

om
ho
sp
it

al
)

Ph
ar
m
ac
is
ts
A
ls
o
ph
ys
ic
ia
ns
an
d
nu
rs
es
M
R
Pa
ti
en
t
co
un
se
lli
ng
C
re
at
io
n
of
po
st
–
di
sc
ha
rg
e
m
ed
ic
at
io
n
lis
ts
Po
st
-d
is
ch
ar

ge
co

m
m
un
ic
at
io
n

Em
er

ge
nc
y
D
ep
ar
tm
en
t
vi
si
ts

R
ea

dm
is
si
on

s
Le

ng
th

of
st

ay
Ph

ys
ic

ia
n

vi
si

ts
H

ea
lt
hc

ar
e
ut
ili
za
ti
on
(E
m
er
ge
nc
y
D
ep
ar
tm
en
t
vi
si
ts

,
re

ad
m

is
si
on
s
an
d

m
or

ta
lit

M
ed

ic
at
io
n

di
sc

re
pa

nc
ie

s
M
cN
ab
et
al
.
20
18
1
5

To
de

te
rm

in
e

th
e

eff
ec

ti
ve

ne
ss

of
M
R
in

ov
er

al
l
di

sc
re
pa
nc

y
id

en
ti
fi
ca
ti
on
an
d

re
so

lu
ti

on
,
th

e
cl

in
ic

al
re

le
va

nc
e

of
re

so
lv

ed
di

sc
re
pa
nc
ie
s
an
d

he
al

th
ca

re
ut

ili
za

ti
on
5
R
C
Ts

3
be

fo
re
-a
nd
-a
ft

er
6

co
ho
rt

36
42

ad
ul

ts
an

d
pe

di
at

ri
c

pa
ti

en
ts

C
om
m
un
it
y
(d
is
ch
ar
ge
fr
om
ho
sp
it
al
to
ho
m

e,
re

si
de

nt
ia

l
un

it
or

nu
rs
in
g
ho
m
e)
Ph
ar
m
ac
is
ts
M
R
Em
er
ge
nc
y
D
ep
ar
tm
en
t
vi
si
ts
R
ea
dm
is
si
on

s
Pr

im
ar

y
an

d
se

co
nd

ar
y

ca
re

co
ns

ul
ta

ti
on
s
M
ed
ic
at
io
n
di
sc
re
pa
nc
ie
s
Pr
im
ar

y
ca

re
w

or
kl

oa
d

C
he
em
a
et
al
.
20

18
1
6

To
up

da
te

th
e

ex
is

ti
ng

ev
al

ua
ti

on
of

th
e
im
pa

ct
of

ph
ar

m
ac

is
t-

le
d
m
ed
ic
at
io
n

re
co

nc
ili

at
io

n
on

he
al
th
ca

re
ou

tc
om
es

18
R

C
Ts

60
38

ad
ul
ts
H

os
pi

ta
l
(a

dm
is
si
on
,
di
sc
ha
rg
e
ho
m
e,
in
–
ho
sp
it
al
tr
an
sf
er
)
Ph
ar
m
ac
is
ts
M
R
Pa
ti
en
t
co
un
se
lli
ng
C
re
at
io
n
of
po
st
–
di
sc
ha
rg
e
m
ed
ic
at
io
n
lis
ts
H
ea
lt
hc
ar
e
ut
ili
za
ti

on
(d

ru
g-

re
la
te
d
Em
er
ge
nc
y
D
ep
ar
tm
en
t
vi
si
ts
an
d
re
ad
m
is
si
on

M
ed
ic
at
io
n
di
sc
re
pa
nc
ie

s
A

D
Es

w
it

h
po

te
nt

ia
l
to

ca
us
e
in
ju
ry

Pr
ev

en
ta

bl
e

A
D
Es
R
ed
m
on
d
et
al
.
20
18
1
7

To
as

se
ss

th
e
eff
ec
t
of
M
R

on
m

ed
ic
at
io
n
di
sc
re
pa
nc

ie
s,

pa
ti

en
t-

re
la
te
d
ou
tc
om
es
an
d
he
al
th
ca
re
ut
ili
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A.B. Guisado-Gil, et al. Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
6

included. Medication reconciliation is proposed to avoid possible
medication errors, and consequently harm to patients, however, our
findings show that, compared with usual care, the intervention does not
achieve a clear improvement for patient-related outcomes and health-
care utilization.

The reviews included were low15,17 and critically low7,14,16 in
quality according to AMSTAR 213. The main results in critical and non-
critical domains were consistent with Wolfe et al.20 Overall, the most
frequent methodological shortcomings were: rarely providing evidence
that the authors had worked with a written and registered protocol with
independent verification; not providing a list of excluded studies and
justification of exclusions; infrequently assessing the likelihood of
publication bias, and neglecting to state the sources of funding of each
primary study. A list of excluded studies and justification (item 7; cri-
tical domain) was necessary to qualify publications as moderate quality.
However, it is currently rare when publishing systematic reviews in
journals to include this information beyond the study selection flow-
chart.

Of the 5 systematic reviews included, all conducted further meta-
analyses except Lehnbom et al.14 For the meta-analysis, authors used a
random effects model. This model, as opposed to a fixed-effects model,
is advised in the case of statistical heterogeneity in studies.10 Ad-
ditionally, two reviews7,16 used a fixed model to validate their results.

The results of this review are in line with other authors21,22 who
communicated that hospital-based care is the most commonly studied
point of transition, follow by primary care and long-term care settings.
Regarding intervention providers, the findings were consistent with
Mueller et al.6 whose review found that the majority of, and most
successful, interventions relied heavily on pharmacists. Growing evi-
dence shows that medication lists obtained by pharmacists contain
significantly fewer errors than those obtained through the usual means.
In this sense, a new law in California, which came into effect on 1
January 2019, requires pharmacy staff at hospitals with more than 100
beds to obtain an accurate medication list for each newly admitted
high-risk patient.23

Most studies compared the intervention group with usual care, but it
is not always clear what usual care involved. For ethical reasons, most
studies failed to evaluate MR versus “no medication reconciliation”,
thus limiting their ability to detect a significant difference between
groups. In addition, all reviews except McNab et al.15 included articles
that bundled MR with other interventions aimed at improving transi-
tions of care, but the specific effect of MR in these multifaceted inter-
ventions is not found. In this overview, MR does not achieve a clear
improvement in health outcomes. Only one review17 based on a single
publication reported results on mortality. However, the impact of MR

on healthcare utilization was communicated in all reviews.7,14–17

Emergency Department visits and readmissions were other results with
a follow-up period no longer than 30 days in most studies. In contrast, a
trial of MR with no additional discharge interventions that used a
longer follow-up (12 months) reported a significant reduction in ED
visits and readmissions.24 This suggests the convenience of future re-
search to consider a time point beyond the traditional 30-day mark.

The main strength of our study is that it was not limited to specific
study designs, population, intervention providers, settings or health
outcomes measured, and was intended to provide an outline of current
evidence related to MR. The results summarize the effectiveness of the
intervention on mortality, length of stay, ED visits, unplanned read-
missions, physician visits and healthcare utilization. The main limita-
tion is that the low number of systematic reviews included did not make
it possible to exclude reviews with insufficient quality, and moderate
and high quality systematic reviews in the literature that met the in-
clusion criteria were not found. In addition, because of evidence of
substantial heterogeneity in study designs, settings, intervention com-
ponents and health outcomes, statistical pooling of all primary studies
that measured health outcomes was not always possible. Even those in
which a meta-analysis was carried out, the value of I2 showed a non-
negligible level of heterogeneity.

While all reviews in our overview addressed the impact of MR on
patient-related outcomes and healthcare utilization, a limitation com-
monly admitted by the authors of the reviews included is that few
primary research articles specifically focused on these results. These
were often listed as secondary or composite outcomes, and the studies
were not powered to detect a significant difference between groups.
This means that it is difficult to draw definitive conclusions from meta-
analysis, other than to say that there is currently no firm evidence that
MR improves health outcomes. Further research is needed that includes
more studies that are robust and of adequate sample size to test the
impact of MR on health outcomes.

Conclusion

Few systematic reviews support the value of MR in achieving good
patient-related outcomes and healthcare utilization improvements. The
quality of the systematic reviews were low and the primary studies
included, mostly RCTs, often involved additional activities related to
MR. There was no clear evidence in favor of the intervention in mor-
tality, length of stay, ED visits, unplanned readmissions, physician visits
and healthcare utilization, the latter being the most frequently com-
municated clinical outcome.

Funding

This research did not receive any specific grant from funding
agencies in the public, commercial, or not-for-profit sectors.

Declaration of competing interest

None.

Acknowledgements

We would like to thank Fundación Andaluza Beturia para la
Investigación en Salud (FABIS) who provided critical review and support
in writing this review.

Appendix A. Supplementary data

Supplementary data to this article can be found online at https://
doi.org/10.1016/j.sapharm.2019.10.011.

Table 4
Association between medication reconciliation and health outcomes.

Health outcomes Setting Number of
systematic
reviews

Number of
primary
studies

Association

Mortality Hospital 117 1 ND
Length of stay Hospital 214,17 3 ND

RACF 114 1 +
Emergency

Department
visits

Hospital 214,17 2 ND
Community 115 2 ND

Unplanned
readmissions

Hospital 214,17 6 ND
Community 214, 15 7 ND

Physician visits Community 214, 15 3 ‒/ND
Healthcare

utilization
Hospital 47, 14,16,17 10 +/ND
Community 115 2 ND
RACF 114 1 +

RACF = Residential aged care facilities. ND=No statistically significant dif-
ferences (p > 0.05). + = Statistically significant differences in favor of
medication reconciliation (p < 0.05). ‒ = Statistically significant differences against medication reconciliation (p < 0.05).

A.B. Guisado-Gil, et al. Research in Social and Administrative Pharmacy xxx (xxxx) xxx–xxx
7

https://doi.org/10.1016/j.sapharm.2019.10.011

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Impact of medication reconciliation on health outcomes: An overview of systematic reviews

Introduction
Methods
Eligibility criteria
Information sources
Study selection
Quality assessment
Data collection
Results
Quality of the systematic reviews
Characteristics of included systematic reviews and meta-analyses
Effectiveness of medication reconciliation on health outcomes (see Table 4)
Mortality
Length of stay
Emergency Department visits
Unplanned readmissions
Physician visits
Healthcare utilization

Discussion
Conclusion
Funding
mk:H1_21
Acknowledgements
Supplementary data
References

American Journal of Medical Quality
2016, Vol. 31(4) 315 –322
© The Author(s) 2015
Reprints and permissions:
sagepub.com/journalsPermissions.nav
DOI: 10.1177/1062860615574327
ajmq.sagepub.com

Article

Hospitalized patients in the United States are increasingly
being cared for by physicians other than their primary
care physicians (PCPs).1 In 2010, more than 80% of US
hospitals with ≥200 beds had hospitalist programs.2 As a
result, the importance of communication between hospi-
tal providers and PCPs to prevent medical errors and
improve quality of care has come to the forefront.3-6
Hospital systems are, for the most part, not optimized to
provide efficient transfer of this vital information, and
communication between physicians caring for hospital-
ized patients and PCPs is often suboptimal.7-9

For patients with complex medical problems, the hospi-
tal discharge period is particularly prone to errors.5
Medications may have been discontinued or added or may
have had dosing changes during a hospitalization, fre-
quently leading to errors. Medical errors are common in the
early postdischarge period,10 and adverse events occur in
about 20% of patients post discharge, most often because of
medications.11,12 Medication errors and adverse drug events
(ADEs) are frequently caused by hospital system factors,13
such as ineffective communication between caregivers.11

Almost half of discharged patients have unexplained medi-
cation discrepancies, heightening ADE risk.14 Medication
reconciliation is a Joint Commission National Patient
Safety Goal and a core measure of Stage 2 meaningful
use.15 However, hospitals and electronic medical record
(EMR) vendors have struggled to meet this mandate.16,17

Prior research has studied interventions to decrease
medication errors at hospital discharge and to improve
patient outcomes.18 Some interventions used medication
reconciliation performed by pharmacists, with medication
errors being variably affected by these interventions.19,20
Computerized medication reconciliation tools have been
developed21 and have shown promise as a means to
decrease medication errors, but effects on patient outcomes

574327AJMXXX10.1177/1062860615574327American Journal of Medical QualitySmith et al
research-article2015

1University of Pittsburgh, Pittsburgh, PA
2Weill Cornell Medical College, New York, NY

Corresponding Author:
Kenneth J. Smith, MD, MS, Department of Medicine, University of
Pittsburgh, 200 Meyran Ave, Suite 200, Pittsburgh, PA 15232.
Email: smithkj2@upmc.edu

Automated Communication Tools
and Computer-Based Medication
Reconciliation to Decrease Hospital
Discharge Medication Errors

Kenneth J. Smith, MD, MS1, Steven M. Handler, MD, PhD1,
Wishwa N. Kapoor, MD, MPH1, G. Daniel Martich, MD1,
Vivek K. Reddy, MD1, and Sunday Clark, ScD, MPH2

Abstract
This study sought to determine the effects of automated primary care physician (PCP) communication and patient
safety tools, including computerized discharge medication reconciliation, on discharge medication errors and
posthospitalization patient outcomes, using a pre-post quasi-experimental study design, in hospitalized medical patients
with ≥2 comorbidities and ≥5 chronic medications, at a single center. The primary outcome was discharge medication
errors, compared before and after rollout of these tools. Secondary outcomes were 30-day rehospitalization, emergency
department visit, and PCP follow-up visit rates. This study found that discharge medication errors were lower post
intervention (odds ratio = 0.57; 95% confidence interval = 0.44-0.74; P < .001). Clinically important errors, with the potential for serious or life-threatening harm, and 30-day patient outcomes were not significantly different between study periods. Thus, automated health system–based communication and patient safety tools, including computerized discharge medication reconciliation, decreased hospital discharge medication errors in medically complex patients.

Keywords
medication error, medication reconciliation, hospital discharge, communication tools

mailto:smithkj2@upmc.edu

http://crossmark.crossref.org/dialog/?doi=10.1177%2F1062860615574327&domain=pdf&date_stamp=2015-03-09

316 American Journal of Medical Quality 31(4)

are unclear.22,23 This study examines a health care system’s
implementation of a broader set of automated PCP com-
munication tools, including computerized medication rec-
onciliation, and its impact on discharge medication errors.

Methods

A pre-post quasi-experimental study of a series of sys-
tem-wide automated communication and patient safety
tools was performed within the University of Pittsburgh
Medical Center (UPMC) system, which in 2010 operated
20 hospitals throughout Western Pennsylvania. Data were
collected for patients hospitalized at UPMC Presbyterian,
UPMC’s major academic hospital.

The University of Pittsburgh Institutional Review
Board approved a waiver of informed consent/HIPAA
(Health Insurance Portability and Accountability Act)
authorization to access, record, and use protected patient
health information/patient medical record information.
This study is registered at ClinicalTrials.gov, Identifier:
NCT01397253.

The preintervention period for this study was April 1,
2009, through October 7, 2010. The end date was chosen
based on the first of the new automated PCP communica-
tion initiatives, rolled out on October 8, 2010. Assisted by
an expert PCP panel, using the modified Delphi technique
to seek consensus on information items PCPs want to
receive,24 other initiatives were sequentially rolled out to
improve notifications about admission, critical illness
occurrence, test results, and discharge communication
(see Figure 1). The UPMC Office of Physician Relations
sent notifications by secure e-mail or fax, using the PCPs’
preferred method. The Office of Physician Relations

maintained addresses and phone numbers to ensure
timely delivery notification while managing and correct-
ing any process failures. These efforts culminated in a
mandatory EMR-based discharge medication reconcilia-
tion procedure, with reports given to patients and sent to
PCPs. This procedure, implemented in Cerner PowerChart
(Cerner, Kansas City, Missouri), UPMC’s inpatient EMR,
was launched on August 22, 2011; this began the postint-
ervention period, which ended on December 31, 2012. At
hospital discharge, physicians used this tool to reconcile
discharge medications against medication histories
obtained on hospital admission by hospital personnel; use
was required to order discharge medications and to dis-
charge patients. In the preintervention period, a paper-
based nonmandatory discharge medication reconciliation
process was in place, similarly reconciling against medi-
cation histories obtained by hospital personnel; its effec-
tiveness was unclear.

Patients were included if they were admitted to gen-
eral medicine, geriatrics, or cardiology inpatient services;
were ≥18 years of age; were discharged home; were med-
ically complex (≥2 comorbid conditions present, defined
using the Elixhauser comorbidity system25); were pre-
scribed ≥5 preadmission medications (a measure of poly-
pharmacy); and had outpatient care provided by PCPs
who (1) use the UPMC Epic ambulatory care EMR (Epic
Systems, Madison, Wisconsin) and (2) admitted ≥5
patients to UPMC Presbyterian in the year preceding the
study. The Epic ambulatory EMR is used by approxi-
mately 90% of UPMC outpatient providers. Patients were
excluded if they were admitted to critical care units,
admitted from skilled nursing facilities, diagnosed with
dementia, or were organ transplant recipients; exclusions

Figure 1. Intervention elements.

Hospital admission notifications to primary care physicians (PCPs) with contacts for communication

PCP notification of patient transfer to critical care units

Mandatory computer-assisted discharge medication reconciliation

PCP notifications at a patient’s hospital discharge
Current problem list
Advance directive information
Vaccination history
Reconciled medication list
Major tests and procedures
Test results pending
Planned follow-up
Patient discharge instructions
Patient information material/education received
Hospital contacts for communication
Discharge summary

Smith et al 317

were based on the expectation that study patients would
be admitted from and discharged to a community setting
in which they would resume care with their PCP. All
medically complex patients identified and meeting inclu-
sion/exclusion criteria were included in analyses.

Medication errors were identified using a 2-stage pro-
cess.26,27 For the purposes of the study, this process was
performed retrospectively after a patient’s hospital dis-
charge and, thus, was entirely separate from procedures
performed during the hospitalization by hospital person-
nel during all phases of this study. In the first stage of the
study-based process, trained research personnel created a
case summary of each patient’s medications, which
included preadmission medications, medications prior to
discharge, and discharge medications. This case medica-
tion summary was created by examining ambulatory
EMR data on a patient’s current medications at the last
PCP encounter before hospitalization. This retrospec-
tively constructed list, intended to be a gold standard rep-
resentation of prehospital medication use, was not
connected to the medication history obtained by hospital
personnel at the time of admission. Hospital medications
and discharge medications were included in the study-
based medication case summary using hospital EMR data
post discharge. Discharge medications were those listed,
after medication reconciliation, in discharge medication
instructions given to the patient and sent to the PCP.
Discrepancies in medication regimens were identified by
comparing the preadmission medication list, hospital
medications, and discharge medications. Any differences
between the study-based preadmission medication case
summary and discharge medications were considered
medication variances. Hospital personnel, when obtain-
ing the medication history, had access to the outpatient
EMR throughout all study periods.

During the second stage of the study-based medication
error identification process, 2 hospital-based clinical phar-
macists independently reviewed those study-based medi-
cation variance summaries, using methods described
previously.27 Both pharmacists had previous experience
and concurrent activity in clinical medication review and
received refresher training in error classification. They
reviewed the EMR to identify the need for changes from
the patient’s preadmission medication case record.
Medication variances deemed medically necessary were
not considered medication errors. Variances not consid-
ered changes required by the patient’s clinical status were
classified as medication errors. The pharmacists then
independently classified medication errors, via the schema
of Pippins et al,27 as clinically important if there was the
potential to cause death, permanent or temporary disabil-
ity, prolonged hospital stay, readmission, or additional
treatment or monitoring to protect the patient from harm;
by this schema,27 these were serious or life-threatening

potential ADEs. All disagreements between pharmacists
were resolved by consensus during periodic face-to-face
meetings, supplemented by telephone and electronic com-
munication. The pharmacists could not be blinded because
of their use of the entire EMR in their reviews and the
time-based nature of the intervention. Data for secondary
outcomes (30-day readmission, emergency department
visits, and follow-up PCP visits) were obtained through
EMR review. Patients with >1 hospitalization during a
study period were eligible for inclusion only during their
first hospitalization but could be included once each dur-
ing the preintervention and postintervention periods.

All comparisons were performed using Kruskal-
Wallis and χ2 tests. To control for potential confounders,
multivariable logistic regression was performed. Factors
were included in the multivariable mixed-effects model
if they were significantly associated with the outcome
variable (unintended medication variances) at P < .20 or considered potentially clinically significant. A P < .20 was chosen because more traditional levels (eg, P < .05) can, in multivariable models, fail to identify the follow- ing: (1) variables known to be important or (2) collec- tions of variables that, considered together, are significant predictors when they are not significant individually.28 Because they could contribute to both study periods and because of multiple medications per individual, patients were included in the mixed-effects model as a random effect, and individual patient characteristics were included as fixed effects. Pre hoc power and sample size calculations showed that detection of a 10% absolute reduction in discharge medication errors (primary out- come) from an estimated baseline of 41% at α = .05 and 90% power required enrollment of 381 participants dur- ing each period (n = 762 over the entire study). This study planned enrollment of 500 patients in each period to increase power to detect differences in 30-day rehos- pitalization, emergency department visits, and PCP fol- low-up visits (secondary outcomes), with 80% power to detect 6% absolute reductions.

Changes in clinical responsibilities prevented all cases
from being reviewed by both pharmacists. As a result, the
primary analysis includes only cases reviewed by both
pharmacists to ensure consensus regarding medication
variances. A sensitivity analysis including all cases also
was performed, whether reviewed by one or both phar-
macists. In addition, a post hoc secondary analysis was
performed that examined possible associations of sex,
race, and hospital length of stay with medication errors.

Results

Data on 835 patient hospitalizations were obtained, 443
pre intervention and 392 post intervention. Of these, 560
(67%) had discharge medication variances reviewed by

318 American Journal of Medical Quality 31(4)

both pharmacists (317 pre intervention, 243 post interven-
tion); these patients are included in the primary analysis,
the remainder are included in a sensitivity analysis. It was

found that 28 patients were in both pre and post cohorts.
Age, sex, and race did not differ between study periods
(Table 1). Postintervention patients were significantly

Table 1. Characteristics and Outcomes of Participants, by Study Period.

Pre intervention, n = 317 Post intervention, n = 243 P Value

Demographic characteristics
Age (years), median (IQR) 63 (53-76) 63 (54-73) .43
Sex (%) .20
Male 139 (44) 93 (38)
Female 178 (56) 150 (62)
Race (%) .44
White 216 (68) 151 (62)
Black 96 (30) 86 (35)
Native American/Alaskan Native 1 (0.3) 1 (0.4)
Asian 3 (1) 4 (2)
Hispanic 1 (0.3) 0 (0)
Missing 0 (0) 1 (0.4)
Insurance (%) <.001 Private 96 (30) 193 (79) Public 215 (68) 50 (21) Uninsured 4 (1) 0 (0) No documentation 2 (1) 0 (0)

Clinical characteristics
Number of comorbidities (%) <.001 0 9 (3) 4 (2) 1 62 (20) 75 (31) 2 118 (37) 106 (44) 3 83 (26) 47 (19) 4 32 (10) 10 (4) 5 12 (4) 1 (0.4) 6 1 (0.3) 0 (0) Modified Elixhauser comorbidity index, median (IQR) 5 (3-11) 3 (0-5) <.001 Hospital length of stay (days), median (IQR) 3 (2-4) 2 (2-4) .54 Number of medications, median (IQR) 11 (8-15) 8 (6-10) <.001 Number of medications (%) <.001 5-9 107 (34) 165 (68) 10-14 126 (40) 61 (25) 15-19 62 (20) 14 (6) 20-24 15 (5) 3 (1) 25-29 6 (2) 0 (0) 30 1 (0.3) 0 (0)

Medication variance
Medication variance (%) <.001 None 1836 (53) 1650 (58) Medically indicated variance 1009 (29) 814 (29) Medication error 645 (18) 359 (13) Clinically important medication error 9 (1.4) 11 (3.1) .10

30-Day follow-up
Readmission (%) 58 (18) 41 (17) .74
Emergency department visit (%) 81 (26) 49 (20) .16
Attended PCP follow-up appointment (%) 148 (47) 109 (45) .04
Died (%) 0 (0) 0 (0) —

Abbreviations: IQR, interquartile range; PCP, primary care provider.

Smith et al 319

more likely to have employer/commercial insurance.
Modified Elixhauser comorbidity index scores29 and med-
ications per patient were slightly lower post intervention.

Fewer medication errors occurred during the postin-
tervention period. Clinically important medication errors
did not differ between study periods. Although there was
a small but statistically significant decrease in PCP fol-
low-up visits post intervention, no differences were
observed in hospital readmissions or emergency depart-
ment visits.

Differences in medication errors remained statistically
significant on multivariable analysis adjusting for age,
sex, insurance, comorbidity, and number of medications
(Table 2).

A sensitivity analysis, including cases only reviewed
by a single pharmacist (totaling 835 hospitalizations; 443
pre intervention, and 392 post intervention), showed
results not materially different from the primary analysis,
with the fully adjusted multivariable mixed-effects model
showing a reduction in medication errors post interven-
tion (odds ratio [OR] = 0.52; 95% confidence interval
[CI] = 0.42-0.66; P < .001). After adjustment, no signifi- cant differences were seen in clinically significant medi- cation errors or in 30-day patient outcomes.

In post hoc secondary analyses to assess associations
between medication errors and sex, race, and hospital
length of stay, race was not associated with medication
errors (data not shown). However, women were more
likely to have medication errors (OR = 1.40; 95% CI =
1.11-1.75) after adjustment for age, insurance, comor-
bidity, and number of medications, and longer hospital
stays were associated with fewer discharge medication
errors (first quartile: reference; second quartile: OR =
0.91, 95% CI = 0.68-1.21; third quartile: OR = 0.56,
95% CI = 0.41-0.76; fourth quartile: OR = 0.60, 95% CI
= 0.45-0.82) in the fully adjusted model. Stratifying by
study period did not materially change results (data not
shown).

Discussion

This study examined the impact of automated health sys-
tem–based interventions on patient care quality and safety,
in the context of a PCP’s patient being admitted to the

hospital, cared for by another physician, and discharged
back to the PCP’s care. Statistically significant decreases in
medication errors were seen when comparing preinterven-
tion and postintervention periods. Clinically significant
medication errors with potential for serious or life-threaten-
ing consequences were rare and no different between study
periods. After adjustment, 30-day patient care outcomes for
rehospitalization and emergency department visits were not
significantly different between study periods.

The intervention included automated communications
to notify PCPs of their patients’ admission, discharge, and
critical care transfers during a hospitalization and to pro-
vide PCPs with important information on follow-up care at
discharge. This information includes studies whose results
were pending and reports from a mandatory computerized
medication reconciliation process. Unfortunately, individ-
ual intervention component effectiveness cannot be mea-
sured. Because this study did not measure the effects of
automated hospital communications on hospital/PCP inter-
actions, it could be argued that the EMR-based mandatory
discharge medication reconciliation was the key compo-
nent in decreasing medication errors, with PCP communi-
cation unlikely to affect this outcome. If so, demonstration
that software-based medication reconciliation successfully
reduced medication errors is still a valuable finding and
consistent with prior studies.22,23 A conference convened to
discuss challenges facing medication reconciliation,
including myriad tracking systems, unclear responsibili-
ties, and systems development needs, has made recom-
mendations to help resolve them.17 On the other hand,
communication between hospitalists and PCPs is a recent
focus of research and guidelines, with hopes that electronic
communication tools will improve patient care quality and
outcomes4-6,30 and lead to information exchange between
both parties, rather than passive information transfer from
hospital to PCP.31 In theory, highly developed 2-way elec-
tronic communication systems between hospitals and
PCPs, with access to EMR data and direct communication
links to hospital caregivers, could allow PCPs the option of
participating more directly in their patients’ hospital care at
a distance, providing virtual continuity of care through
electronic means and, through this interaction, avoiding
transition of care miscommunications that could lead to
medical errors.

Table 2. Multivariable Mixed-Effects Model of Intervention Effects on Unintended Medication Variances (Medication Errors).

Odds Ratio 95% Confidence Interval P Value

Unadjusted 0.63 0.51-0.77 <.001 Adjusted for age, sex, and insurance 0.54 0.43-0.69 <.001 Adjusted for age, sex, insurance, and comorbidity score 0.52 0.41-0.67 <.001 Adjusted for age, sex, insurance, comorbidity score, and number of

medications
0.57 0.44-0.74 <.001

320 American Journal of Medical Quality 31(4)

In this study, comparisons were made between pread-
mission medication lists that were created retrospectively
by research personnel based on ambulatory EMR data
and discharge medications. Thus, the effectiveness of the
entire hospital medication transition reconciliation and
prescribing process was tested en bloc, noting uncor-
rected medication errors occurring from preadmission
medications onward through the hospitalization, based on
discrepancies between lists. Ambulatory EMR use to
construct prehospitalization medication lists could be
criticized if long intervals between PCP visits and hospi-
talizations were seen, with new medications possibly
added by non-PCP physicians in the interim but not noted
in the EMR. However, the medication summaries were
identically obtained throughout all study periods; thus,
differences attributable to this effect should cancel out
between preintervention and postintervention periods.
Finally, the study-based reviewing pharmacists were not
blinded, a potential limitation, because they needed
access to the entire EMR for their determinations.

No differences were found in clinically important
medication errors or in patient outcomes. Interestingly,
clinically important medication error rates in this study
were lower than those typically reported.27 It is not clear
why. A common definition was used for errors,27 as was a
well-described format for finding them.26,27 The study-
based medication case record was obtained independently
from the clinical medication history. Two trained clinical
pharmacists examined each case record and, for the pri-
mary analysis, reached consensus on medication error
classification. In the study institution, a paper-based med-
ication reconciliation process had been in place before
this intervention, possibly diluting its effect. More recent
studies found serious potential ADE rates at hospital dis-
charge, from 0.01 to 0.21 per patient32; the present study
found rates of 0.03 and 0.05 per patient in preintervention
and postintervention, respectively. In addition, 30-day
outcomes could have been underestimated if visits
occurred at non-UPMC facilities because outcomes were
ascertained using UPMC EMR data, a study limitation.
However, study participants were patients of PCPs who
use the UPMC EMR, likely mitigating this effect.

Post hoc secondary analyses found associations of
errors with female sex and hospital length of stay. Greater
medication error risk in women has been reported previ-
ously33; its mechanism is unclear. Medication error risk
decreased with longer hospital length of stay, a finding
not described elsewhere. Although requiring confirma-
tion, it raises several possibilities. Medication errors are
commonly made at hospital admission32; longer hospital-
izations may provide more opportunities for error correc-
tion. Patients with shorter stays may be perceived as less
sick, and less vigilance could result. Finally, patients with
in-hospital ADEs have longer lengths of stay.34 ADEs

could trigger greater attention to medications and fewer
errors at discharge.

There are limitations in quasi-experimental study
designs.35 A nonrandomized study could insufficiently
control for important confounding variables. This study
controlled for variables where significant differences
were found between study groups, but unmeasured con-
founders could still affect results. Secular trends toward
decreasing discharge medication errors also could explain
the study results. However, a gap of less than 11 months
between study periods makes this less likely. Introduction
of the intervention represented a historical event that
could have changed physician attitudes and affected
results. On the other hand, randomized trials of medical
informatics interventions are often difficult to perform
within a single facility because of barriers to selective
rollout of interventions.35 Contamination effects, wherein
personnel learning a new intervention could apply it to all
patients regardless of randomized group, also could
occur.

Thus, a multicenter randomized trial of the study insti-
tution’s automated tools would need to be performed to
definitively demonstrate benefit. A multicenter random-
ized trial of best practices to improve medication recon-
ciliation at 6 US hospitals is ongoing. This effort, the
Multicenter Medication Reconciliation Quality
Improvement Study (MARQUIS), will assess multiple
interventions, including medication reconciliation soft-
ware, to specifically address obtaining a “best medication
history” from hospitalized patients and using multiple
processes to ensure that all necessary medications are
taken post discharge.32

In conclusion, implementation of automated health
system–based tools, including computerized discharge
medication reconciliation, decreased hospital discharge
medication errors in medically complex patients.
Definitive assessment of these tools will await future
multicenter trials.

Declaration of Conflicting Interests

The authors declared the following potential conflicts of interest
with respect to the research, authorship, and/or publication of
this article: All authors are or have been employees of UPMC
and/or the University of Pittsburgh. There are no other conflicts
of interest.

Funding

The authors disclosed receipt of the following financial support
for the research, authorship, and/or publication of this article:
This study was supported by the Agency for Healthcare
Research and Quality (R18HS18151, R01HS018721,
K12HS019461), which had no role in the study design, collec-
tion, analysis, interpretation, or drafting of the manuscript or in
the decision to submit the manuscript for publication. The

Smith et al 321

content is solely the responsibility of the authors and does not
represent the official views of the Agency for Healthcare
Research and Quality.

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6. Kripalani S, LeFevre F, Phillips CO, Williams MV,
Basaviah P, Baker DW. Deficits in communication and
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nuity of care. JAMA. 2007;297:831-841.

7. Auerbach AD, Nelson EA, Lindenauer PK, Pantilat SZ,
Katz PP, Wachter RM. Physician attitudes toward and
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8. Pantilat SZ, Lindenauer PK, Katz PP, Wachter RM.
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9. Roy CL, Poon EG, Karson AS, et al. Patient safety con-
cerns arising from test results that return after hospital dis-
charge. Ann Intern Med. 2005;143:121-128.

10. Moore C, Wisnivesky J, Williams S, McGinn T. Medical
errors related to discontinuity of care from an inpatient to
an outpatient setting. J Gen Intern Med. 2003;18:646-651.

11. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW.
The incidence and severity of adverse events affecting
patients after discharge from the hospital. Ann Intern Med.
2003;138:161-167.

12. Forster AJ, Murff HJ, Peterson JF, Gandhi TK, Bates DW.
Adverse drug events occurring following hospital dis-
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13. Greenwald JL, Denham C, Jack B. The hospital discharge: a
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14. Schnipper JL, Kirwin JL, Cotugno MC, et al. Role
of pharmacist counseling in preventing adverse drug
events after hospitalization. Arch Intern Med. 2006;166:
565-571.

15. Centers for Medicare & Medicaid Services. Stage 2 Eligible
hospital and critical access hospital meaningful use core mea-
sures: measure 11 of 16. http://www.cms.gov/Regulations-and-
Guidance/Legislation/EHRIncentivePrograms/downloads/
Stage2_HospitalCore_11_MedicationReconciliation .
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16. Simmons JC. Getting patients right drugs at right time no
easy task. http://www.acponline.org/journals/news/oct06/
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17. Greenwald JL, Halasyamani L, Greene J, et al. Making
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2010;5:477-485.

18. Hesselink G, Schoonhoven L, Barach P, et al. Improving
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Lavf58.12.100

Chamberlain College of Nursing NR449 Evidence-Based Practice

Evidence Matrix Table

Article

Reference

Purpose

Hypothesis

Study Question

Variables

Independent(I)

Dependent(D)

Study Design

Sample

Size and Selection

Data Collection

Methods

Major Findings

1
(sample not a real article)

Smith, Lewis (2013),
What should I eat? A focus for those living with diabetes. Journal of Nursing Education, 1 (4) 111-112.

How do educational support groups effect dietary modifications in patients with diabetes?

D-Dietary modifications
I-Education

Qualitative

N- 18
Convenience sample-selected from local support group in Pittsburgh, PA

Focus Groups

Support and education improved compliance with dietary modifications.

NR449 Evidence Matric Table x Revised10/20/14 ns/cs

Running head: Improving medication reconciliation and education

Improving medication reconciliation and education 2

Improving Hospital Discharged through Medication Reconciliation and Education

Carmen Mustata

Chamberlain Collage of Nursing

NR – 449: Evidence Base Practice

January 2020

Clinical Question

Problem

Every year, several deaths have been connected to medication errors. This paper is going to address what is the effectiveness of an improved hospital discharge through medication reconciliation and education. It will also evaluate the effectiveness of an improved medication reconciliation and education and the risk for not improving it which decreases medication errors and promote patient safety.

Significance of problem

Kern, E., Dingae, M. B., Langmack, E. L., Juarez, C., Cott, G., & Meadows, S. K. (2017), states that across 18 months improved medication reconciliation increased from 9.8% to 91.3%. This improvement of medication reconciliation have led to medication that list missing dose/frequency to decrease form 18.1% to 15.8%. Also patient who have duplicate medication to decrease from 4.0% to 2.6%. The article identifies other aspect that contribute to improve medication reconciliation and education by requiring the organization to obtain the patient’s medication information at admission, and update when the patient’s medications change.

Kreckman J, Wasey W, Wise S, et al (2018), brought out that the healthcare team verifies with patient and their families and even contacting their pharmacies and providers to reconcile the patient’s hospital medication at admission and within 24 hours of discharge. This implementation prevented errors and early recovery if an error occurred. The percentage of improving medication conciliation decreased from 33.9% to 18.7% at the hospital admission and at discharge from 22.9% to 5.0%. With all these results, by improving the medication reconciliation and education prevented a lot of medication errors and also help patient to safer transition.

Purpose

The purpose of this assignment is to evaluate the effectiveness of improving medication reconciliation and education at hospital discharge.

Evidence Matrix Tool

In this paper matrix table was used and discussed two evidence based practice articles. The first evidence based article that was used is Kern, E., et al (2017), the purpose is to determine if medication reconciliation in a large subspecialty outpatient practice improved. The variables whereby the independent (I) is the improve medication reconciliation whiles the dependent (D) is the medication errors. The study design for this article is interview, and quantitative. The sample size and selection is N-75,000 adult outpatient in a National Jewish Health (NJH) hospital and data collection method is focus group in Denver. The major findings for this article is implementing measures to improve performance and quality of medication reconciliation from Electronic Health Records (EHR) over a periods of time. The article addresses potential safety concerns by ensuring when the medication is added, changed, or discontinued and needs to be evaluate. The health care team goal was to improve medication reconciliation by setting up a system that measures and validate electronic measures in daily work because NJH lacked measures of attestation that medication reconciliation is done. Concerning patient education, based on EHR documentation they were able to find out if patient had been offered a “Medication Safety Facts” handout to know if there have been any patient education regarding the medication prescribed.

The second evidence based article that was used is Kreckman J, Wasey W, Wise S, et al (2018), the purpose is to improve medication reconciliation at hospital admission, discharge and ambulatory care through a transition of care team. The variables where the independent (I) is improving medication reconciliation and dependent (D) is medication errors. The study design for this article quantitative, interview patients, and qualitative. The sample size and selection is N-70 patients in a tertiary-care facility in Illinois and the data collected method used is focus group. The major findings for this article is reduction in medication errors at admission, discharge, and follow-up by improving medication reconciliation. Furthermore, to improve medication reconciliation they formed a group called transition of care team which includes registered nurses to help improve medication errors. This group engaged with everyone involved with patient care. With this it allowed for additional investigation resources and preventing errors.

Conclusion

The major findings are the resources used establish reliability and validity. With both articles support the need to improve medication reconciliation whether at admission, discharge, or follow-up. The articles provide results that shows how using care team and electronic health records to improve medication reconciliation. Both articles set up strategies that are useful for healthcare workers regarding on how to improve medication reconciliation and education to reduce medication errors during transition of care.

References

Kern, E., Dingae, M. B., Langmack, E. L., Juarez, C., Cott, G., & Meadows, S. K. (2017). Measuring to Improve Medication Reconciliation in a Large Subspecialty Outpatient Practice. The Joint Commission Journal on Quality and Patient Safety, 43(5), 212–223. doi: 10.1016/j.jcjq.2017.02.005

Kreckman, J., Wasey, W., Wise, S., Stevens, T., Millburg, L., & Jaeger, C. (2018). Improving medication reconciliation at hospital admission, discharge and ambulatory care through a transition of care team. BMJ open quality, 7(2), e000281. doi:10.1136/bmjoq-2017-000281

Measuring to Improve Medication Reconciliation
in a Large Subspecialty Outpatient Practice
Elizabeth Kern, MD, MS; Meg B. Dingae, MHSA; Esther L. Langmack, MD; Candace Juarez, MT; Gary Cott, MD;
Sarah K. Meadows, MS

Background: To assess performance in medication reconciliation (med rec)—the process of comparing and reconciling
patients’ medication lists at clinical transition points—and demonstrate improvement in an outpatient setting, sustainable
and valid measures are needed.

Methods: An interdisciplinary team at National Jewish Health (Denver) attempted to improve med rec in an ambulatory
practice serving patients with respiratory and related diseases. Interventions, which were aimed at physicians, nurses (RNs),
and medical assistants, involved changes in practice and changes in documentation in the electronic health record (EHR).
New measures designed to assess med rec performance, and to validate the measures, were derived from EHR data.

Results: Across 18 months, electronic attestation that med rec was completed at clinic visits increased from 9.8% to 91.3%
(p < 0.0001). Consistent with this improvement, patients with medication lists missing dose/frequency for at least one prescription- type medication decreased from 18.1% to 15.8% (p < 0.0001). Patients with duplicate albuterol inhalers on their list decreased from 4.0% to 2.6% (p < 0.0001). Percentages of patients increased for printing of the medication list at the visit (18.7% to 94.0%; p < 0.0001) and receipt of the printed medication list at the visit (52.3% to 67.0%; p = 0.0074). Documentation that patient education handouts were offered increased initially then declined to an overall poor performance of 32.4% of clinic visits. Investigation of this result revealed poor buy-in and a highly redundant process.

Conclusion: Deriving measures reflecting performance and quality of med rec from EHR data is feasible and sustainable
over the time periods necessary to demonstrate change. Concurrent, complementary measures may be used to support the
validity of summary measures.

Medication reconciliation (med rec) is the process of sys-tematically and comprehensively reviewing the
medications a patient is taking, to ensure that medication

added, changed, or discontinued are evaluated for poten-
tial safety concerns. One of the three current Joint
Commission National Patient Safety Goals (NPSGs) on med-
ication safety (Goal 3), concerns medication reconciliation,
which ambulatory care organizations have been expected to
perform since 2005. The current version of the goal
(NPSG.03.06.01), effective July 1, 2011, stipulates that am-
bulatory care organizations maintain and communicate
accurate patient medication information.1 One require-
ment is that the organization obtain the patient’s medication
information at the beginning of an episode of care, with the
information to be updated when the patient’s medications
change. Ideally, med rec should occur at each transition of
care or handoff, as reflected in Joint Commission Provi-
sion of Care, Treatment, and Services (PC) Standard
PC.02.02.01, which addresses the coordination of informa-
tion during transitions, including medications and medication
reconciliation.1 PC.02.03.01 addresses patient education on
safe medication use.1

For outpatient care, then, each clinic visit represents a tran-
sition during which med rec should be performed.
Impediments to med rec may be attributed to both pa-
tients and providers, who are partners in the process.2 On
the patient side, inaccuracies and incompleteness of self-
reported medication lists are common.3–5 Med rec may be
improved by training patients to maintain personal written
medication lists or to bring all medications to visits.6 Such
interventions aim to retrieve the most accurate patient-
reported medication list, leading to the accepted standard
of the “best possible medication history” (BPMH) as the basis
for reconciliation.7,8 On the provider side, lack of educa-
tion regarding med rec, and lack of understanding of roles
and responsibilities, impede effective med rec.9,10 Provid-
ers’ failure to update the list in the medical record occurs
frequently.11,12

Variability and complexity in work flow among health care
settings precludes prescriptive solutions for the med rec
process. However, measures resulting from the med rec process
may be used to reflect how well med rec is performed.7,13
For example, properly reconciled medication lists should not
contain duplicate medications, and listed prescription-type
medications should include both the dose and frequency. Se-
lected measures should be fundamental to the med rec process,
unlikely to change in importance, and easily captured with
existing institutional resources. Ideally, measurement of med
rec in the outpatient setting should not impede clinic work

The Joint Commission Journal on Quality and Patient Safety 2017; 43:212–223

http://dx.doi.org/10.1016/j.jcjq.2017.02.005

flow or require extra resources. To improve med rec, mea-
sures should reflect the providers responsible for the med rec
process within clinical microsystems and enable evaluation
across time.

National Jewish Health (NJH) is a hospital and outpa-
tient health care system focused on the care of patients with
respiratory, cardiac, immune, and related disorders. Most clin-
ical care occurs in the outpatient setting. The majority of
patients have multiple, comorbid conditions, requiring care
by several specialists within the system, as well as outside pro-
viders. Polypharmacy is common. Half of the adult patien

at NJH have more than nine medications listed in the elec-
tronic health record (EHR), excluding pharmacy supplies,
durable medical equipment, and oxygen. As the risk for drug
interactions and discrepancies among medication lists in-
creases with the number of medications taken, med rec in
the outpatient setting is a key element of patient safety.14,

An interdisciplinary team at NJH convened in 2013 to
examine the med rec process. Within the many clinical
microsystems of NJH, they found little standardization of
processes to document that medication lists were recon-
ciled at clinic visits. NJH lacked a measure of attestation that
med rec had been done. There was no system to show pro-
vider accountability for the process. Finally, there were no
measures of the general quality of medication lists.

The long-term goal was to improve med rec at NJH. The
first aim was to standardize the med rec process within the
NJH health care system, according to NPSG.03.06.011 The
second aim was to set up a practical and sustainable system
of measurement of med rec, by validating electronic mea-
sures of med rec captured in daily work flow from the EHR.

METHODS

The study was judged to be exempt from oversight by the
NJH Institutional Review Board.

Setting

The project was carried out on the main campus of NJH,
a tertiary care and academic medical center in Denver. NJH
provides approximately 75,000 adult outpatient visits an-
nually. Approximately 130 physicians, 117 nurses (RNs),

medical assistants (MAs), and 5 pharmacists staff the out-
patient subspecialty clinics in pulmonary medicine, allergy
and immunology, sleep medicine, cardiology, gastroenter-
ology, infectious diseases, rheumatology, oncology,
endocrinology, nephrology, environmental and occupation-
al health, pediatrics, otorhinolaryngology, and
neuropsychology. The MA staff turns over frequently and
most were hired within the two to three years prior to the
start of the project. No more than six mid-level practitio-
ners were working in adult clinics during the time frame of
the project.

Typically, 1–30 (median of 5) physicians, 2–4 RNs, and
2–4 MAs work together in small subspecialty clinic teams.

Each team has its own leaders and patient care work flow.
All clinics use a single EHR system (Allscripts, Chicago). The
EHR has a medication list module that automatically records
and sends electronic prescriptions. Medications prescribed
by health care providers outside of NJH can be added to
the list, with strength, route, dose, and frequency. Over-
the-counter medications and dietary supplements can be listed,
as well. The list in the patient’s EHR is considered to be
the patient’s official medication list for the NJH system.

Interventions

An interdisciplinary project team consisted of representa-
tives from the following departments: medicine, pediatrics,
pharmacy, administration, information services, nursing,
patient quality and safety, and NJH’s continuing medical
education (CME) office. The project team used the Medi-
cations at Transitions and Clinical Handoffs (MATCH)
framework as a guide for process improvement.16 Figure 1
illustrates the overall strategy used to assess needs, plan in-
terventions, and develop measures for med rec. Sidebar 1
shows the main resources used in designing the interventions.

Flowcharts of the existing med rec processes in various
clinical microsystems were mapped according to input from
physicians and clinic staff. The information was compiled
to identify med rec gaps, barriers to process change, and gaps
in documentation. A putative improved process, based on
NPSG.03.06.01, was piloted in two adult clinics (cardiol-
ogy and gastroenterology). Observations from the pilot were
used to develop broader implementation, educational, and
training strategies for the rest of NJH’s clinics. Interven-
tions focused on clinical team education, behavioral change,
and documentation change. The project team met with each
subspecialty clinic group to customize med rec for the clin-
ic’s preferred work flow while ensuring that performance
targets could be met.

Standardizing the Med Rec Process

The interventions were rolled out in an 18-month period
(Table 1). MAs were identified as the frontline agents for
med rec because they have the first contact with patients at
clinic visits. In all clinics, the med rec process was standard-
ized to require that MAs print the EHR medication lists prior
to patient visits. The patient’s medication history from home
(obtained by interview) was used to reconcile the printed
list. Changes were transcribed to the EHR medication list.
Because of concerns about potential errors, entry-level MAs
were prohibited from deleting medications directly from the
EHR list or adding free-text dose and/or frequency descrip-
tions. Rather, they were trained to annotate the printed list
regarding these types of changes. The annotated list was then
passed to the RN or physician to make the final reconcili-
ation with the EHR medication list. MAs were required to
document that the medication safety handout was offered
to patients (or refused by patients) at every clinic visit. This
handout explained how med rec promotes patient safety and

Volume 43, No. 5, May 2017 213

suggests ways that patients can assist with the process (for
example, bringing in all pill bottles and inhalers).

Training

A series of five interactive, online teaching and testing modules
was created and implemented for MAs. The modules covered
how to record outside medications in the EHR list, add or
change dose and frequency for these medications using struc-
tured entry, and annotate the printed lists for the presence

of duplicate/equivalent albuterol inhalers or discontinued
medications. Duplicate albuterol inhalers were targeted
because preliminary data showed a high percentage of this
error, and pulmonary medicine is a core service of NJH.
Modules included education on types of respiratory inhal-
ers and patient interviewing skills. Posters illustrating different
inhalers were created to help MAs determine which inhal-
ers patients were using. MAs were required to successfully
complete all five online modules.

Overall Strategy

Figure 1: The overall strategy to improve the process of MR began with a preintervention phase of process and work-
flow assessment for MR throughout the adult clinics. Multidimensional measures to reflect the MR process, which could
be obtained from the EHR database, were explored. Interventions to improve MR and MR documentation were designed
for physicians, nurses (RNs), and medical assistants and implemented over 18 months. Eventually, the EHR measures were
validated by comparing the direction of change among related measures, across time. EHR, electronic health record.

Sidebar 1. Resources for Improving Medication Reconciliation (MR) in Ambulatory Care

Resources for Health Care Professionals
► Joint Commission National Patient Safety Goals (NPSG)1

• Contains NPSG.03.06.01, “Maintain and communicate accurate medication information.” Describes the rationale and elements
of performance for medication reconciliation.

► Medications at Transitions and Clinical Handoffs (MATCH) Toolkit2

• Step-by-step guidelines and practical tips for designing and implementing medication reconciliation in a variety of care settings,
from the Agency for Healthcare Research and Quality (AHRQ).

Resources for Patients
► “Your Medicine: Be Smart. Be Safe.”3

• Patient brochure from AHRQ with simple tips on medication safety. Includes a wallet card for listing medications and other health
information. Spanish version available.

Examples of instructional videos and other materials used in the National Jewish Health Medication Project can be found at its website.4

References
1. The Joint Commission. 2017 Comprehensive Accreditation Manual for Ambulatory Care (E-dition). Oak Brook, IL: Joint Commission

Resources, 2016.
2. Gleason K., et al. Island Peer Review Organization. Medications at Transitions and Clinical Handoffs (MATCH) Toolkit for Medication

Reconciliation. AHRQ Publication No. 11(12)-0059. Rockville, MD: Agency for Healthcare Research and Quality, 2012.
3. Agency for Healthcare Research and Quality. Your Medicine: Be Smart. Be Safe. AHRQ Publication No. 11-0049-A. Apr 2011. Ac-

cessed Feb 24, 2017. https://archive.ahrq.gov/patients-consumers/diagnosis-treatment/treatments/safemeds/yourmeds .
4. National Jewish Health. Medication Reconciliation. Accessed Feb 24, 2017. https://www.njhealth.org/medication-reconciliation.

214 Elizabeth Kern, MD, MS, et al Improving Medication Reconciliation

https://archive.ahrq.gov/patients-consumers/diagnosis-treatment/treatments/safemeds/yourmeds

https://www.njhealth.org/medication-reconciliation

Physicians and RNs were encouraged, but not required,
to complete a short online module about their roles and re-
sponsibilities in the med rec process. Small signs were posted
on computer workstations reminding physicians to recon-
cile and print the medication list. Exam room signs were
posted reminding patients to leave the visit with a printed
medication list. In most clinic areas, physicians were ex-
pected to review the annotated, printed medication list from
the MA, correct the EHR list as needed, attest that med rec
was completed, and deliver a final, reconciled, printed list
to the patient. Live educational interventions included MA,
RN, and pharmacist in-service trainings and Medicine Grand
Rounds.

Professional Incentives

To incentivize multidisciplinary engagement, the Office of
Professional Education at NJH made it possible for physi-
cians, RNs, and pharmacists to earn continuing education
credits for their participation. Physicians could earn 20 AMA
PRA Category 1 Credits™ for participation in the perfor-
mance improvement CME initiative, as well as

Maintenance of Certification (MOC) Part IV points from
the American Board of Internal Medicine (ABIM). Nurses

could earn continuing education units (CEU). Pharma-
cists could earn continuing pharmacy education (CPE) credit
for attending a live education session about med rec. MAs
could receive certificates of participation for attending live
trainings and completing the online med rec modules. For
MAs, participation certificates are necessary for career ad-
vancement within NJH.

Measures

To measure the med rec process, indicators of medication
list quality and the med rec process were developed from EHR
data. Table 2 shows details about the operational defini-
tions for the measures.

Process Documentation

The method for electronic documentation for the med rec
process changed during the project. In the early interven-
tion period, electronic attestation of med rec attestation
required two computer mouse clicks. In the late interven-
tion period, a yellow, highlighted button, requiring only one
mouse click, was added to the command bar at the top of
the EHR medication list, in accordance with the EHR ven-
dor’s need to certify for Meaningful Use.17 Likewise, the

Table 1. Time Line of Interventions to Improve Medication Reconciliation (MR) Process and Documentation

Interventions

Q3 Q4 Q1 Q2 Q3 Q4 Q1

2013 2013 2014 2014 2014 2014 2015

In-service training for MAs—MR process X
Baseline performance report to physicians, Oct. 2013 X
In-service training for MAs—MR resources X
In-service training for nurses—MR process, expectations X
In-service training for MAs—Duplicate inhalers X
MR process and work flow—Cardiology division X
MR process and work flow—Gastroenterology division X
Medicine Grand Rounds—MR process and expectations X
Midpoint performance report to physicians, Feb. 2014 X
MR process and work flow—Allergy/immunology division X
MR process and work flow—Cystic fibrosis group X
MR process and work flow—Rheumatology division X
MR process and work flow—Infectious diseases division X
MR process and work flow—Oncology division X
Signs for patients in clinic rooms: Reminder to get printed list X
Computer workstation cards: Reminder to print medication list X
New “button” in EHR to reconcile medication list X
New “button” in EHR to print medication list X
Video recording for MAs—MR interviewing skills X
MR process and work flow—Interstitial lung diseases group X
MR process and work flow—Pulmonary division X
MR process and work flow—Otorhinolaryngology group X
MR process and work flow—Occupational health division X
Online education launch for MAs—5 modules X
Midpoint performance report to physicians, Sep. 2014 X
Live CPE–certified training for pharmacists X
“Medication Safety Facts ”handout updated for patients X
Final performance report to physicians, Mar. 2015 X

Q, quarter; MR, medication reconciliation; MA, medical assistant; EHR, electronic health record; CPE, continuing pharmacy education.

Volume 43, No. 5, May 2017 215

Table 2. Measures Developed for Medication Reconciliation Improvement

Description How Documented Comment

Attestation that medication reconciliation was done “Button” click in the EHR chart done on or after the scheduled
day/time of the visit

Usually the clinic physician attests, but could be a nurse or MA.

Patients with one or more prescription medications
lacking a dose or frequency on the medication list

“Snapshot” samples of medication lists, per unique patient,
queried from the EHR database

The pharmacy module in the EHR indicates strength and route
within the name of the selected medications. Dose and
frequency are added. Medications prescribed outside NJH can
be recorded as “history.”

Patients with duplicates of albuterol inhalers on the
medication list

“Snapshot” samples of medication lists, per unique patient,
queried from the EHR database

The programming algorithm queried for prespecified names of
equivalent brands of albuterol inhalers.

Medication list was printed after the start of the
scheduled clinic visit.

Print command from the EHR chart, within specified time frame Allowance was made for printing up to one hour before the
scheduled visit time because some patients arrived early or
clinic times were shifted ad hoc.

Patients reporting the printed medication list was
given to them at the end of the visit.

Patient sample survey Ad hoc survey as patients left the clinic: not documented in EHR

Attestation that the medication safety handout was
offered (and/or declined) at the visit

“Button” click in the EHR chart The documentation was to be made after the initial medication
reconciliation, prior to the face-to-face with the provider.

Qualifying clinic visit Initially restricted to follow-up visits in adult clinic, but eventually
added new patient visits. Counted only the most recent (last)
visit per unique patient within the sampling time period.

Excluded visits for ancillary services such as lab, nutrition,
radiology, and visits for procedures.

Attribution to the clinic physician Scheduled physician provider for the qualifying visit Initially excluded mid-level providers as an accountable
provider, with intent to eventually include them.

EHR, electronic health record; MA, medical assistant; NJH, National Jewish Health.

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two-click system required to print the medication list was
switched to a one-click system. Again, the change was
prompted by a Meaningful Use requirement because the med-
ication list became a component of a clinical summary
document required at the end of each visit.

The unit of measurement was clinic visits. An individ-
ual patient could be sampled more than once, if he or she
had multiple visits within the sampling time frame. The at-
testation and print measures were attributed to the physician
provider for each visit. Although it was possible to obtain a
100% sample of clinic visits, it became onerous to compile
the weekly reports. Therefore we chose to report a two- to
three-week consecutive sample of clinic visits, once per quarter.
This sampling was sufficient to include all physician pro-
viders in the measures.

Quality Indicators

To validate med rec attestation, we reasoned that the quality
of the medication lists should change in accordance with the
attestation measure. Therefore, we concurrently measured
(1) the percentage of patients who had at least one
prescription-type medication missing dose or frequency on
their medication list, and (2) the percentage of patients with
duplicate albuterol inhalers on their medication list. The unit
of measurement for the two validation measures was the
unique patient, measured once within each month by a “snap-
shot” of his or her medication list. If a patient had more than
one visit within the month, the medication list at the final
visit for the reporting month was used. The patient samples
were independent of each other; individual patients were not
followed longitudinally.

Patient Education

This measure was derived from a single computer click entered
by the MA, documenting that the handout on the impor-
tance of med rec to patient safety had been offered. The unit
of measurement was clinic visits. The patient education
measure was attributed to the MA group as a whole.

Patients’ Receipt of Reconciled Medication List

To validate that the printed medication list actually made
it into the hands of patients, we performed a manual survey
of patients leaving the clinic to determine the percentage who
had received a printed list. Patients’ receipt of the recon-
ciled medication list is the final part of the med rec process,
and we lacked EHR methodology to document this event.

Qualifying Visits for Medication Reconciliation

To verify that the patient showed up for the visit, we re-
quired that a systolic blood pressure measurement be recorded
on the day of the visit. Individual physician queries about
the accuracy of their own data led to “spot checks” that re-
vealed that our measures for attestation and print were not
properly captured if the patient arrived early for the visit.
Report parameters were adjusted to account for this situation.

Analysis

The approach to the analysis of the project was an inter-
rupted time series design, without a concurrent control group.
The samples were compiled within four sequential report-
ing periods for the project: baseline (quarters 1–2, 2013);
early intervention period (quarters 3–4, 2013); late inter-
vention period (quarters 1–4, 2014); and postintervention
period (quarters 1–2, 2015).

Baseline data were not available for med rec attestation
and medication list printing because the documentation for
the measures did not exist prior to the project. The manual
patient survey of patients leaving the clinic with a printed
medication list in hand was performed twice: once during
the baseline period, and once during the postintervention
period. During 2014 reporting of medication lists lacking
dose/frequency for one or more prescription-type medica-
tions was dropped, and resumed in 2015. This was due to
an erroneous communication from the intervention team to
the information technology (IT) team to suspend report-
ing for this measure. Retrospective data were not retrievable
because these data are collected as a snapshot in time. Data
from adult follow-up clinic visits are reported. Pediatric clinic
visit data were not included in this analysis because the process
flow and timing of interventions were substantially differ-
ent from those of the adult clinics.

The results for each measure were calculated as numer-
ator divided by denominator, multiplied by 100 to get a
percentage; that is, the percentage of visits during which an
electronic med rec attestation was completed, or the per-
centage of patients whose medication list contained duplicate
or equivalent albuterol inhalers.

The 95% confidence intervals (CIs) around the sample
percentages were calculated according to the normal ap-
proximation to the binomial distribution. The chi-square test
of trend in binomial proportions was applied across the se-
quential time periods sampled. A trend line was constructed
using the method of least squares applied to the sample means
at each time period. Tests for significance were two-sided,
and alpha < 0.05 was considered significant. The analytic software was SAS 9.3 (SAS Institute Inc., Cary, North Caro- lina) and Excel (Microsoft Corp., Redmond, Washington).

RESULTS
Process Documentation

Measures of med rec process documented in the EHR in-
creased during the course of the initiative (Figure 2). Electronic
attestation that the medication list had been reconciled in-
creased from 9.8% (95% CI: 8.4.%–11.2%) of patients in
the early period to 91.3% (95% CI: 90.5%–92.1%) in the
postintervention period (test of trend, p < 0.0001). Notably, the new attestation button was added to the EHR in March 2014 (late intervention period) to meet Meaningful Use criteria17 and facilitate electronic attestation. The percent- ages of patients whose medication list was printed at the end

Volume 43, No. 5, May 2017 217

of the visit increased from 18.7% (95% CI: 17.8%–
19.6%) in the early period to 94.0% (95% CI: 93.8%–
94.2%) in the postintervention period (test of trend,
p < 0.0001) (Figure 3).

Quality Indicators

Patients with medication lists missing dose or frequency for
at least one medication prescribed by an outside provider
decreased from 18.1% (95% CI: 17.5%–18.7%) at base-
line to 15.8% (95% CI: 15.3%–16.3%) in the
postintervention period (test of trend, p < 0.0001). Pa- tients with duplicate or equivalent albuterol inhalers on their

medication list decreased from 4.0% (95% CI: 3.7%–
4.3%) at baseline to 2.6% (95% CI: 2.4%–2.8%) in the
postintervention period (test of trend, p < 0.0001).

Patient Education

The measure of patient education on medication safety
(Figure 4) was based on EHR documentation as to whether
or not the patient had been offered a “Medication Safety
Facts” handout. Although the test of trend was significant
overall in a positive direction (p < 0.0001), the results varied widely over time, with values in the postintervention period (32.4%; 95% CI: 31.8%–33.0%) that were actually

0
10
20

Early Late Post

%
o

f
V

is
it

Intervention Time Period

Visits with Attestation of Medication List Reconciled

Baseline Early Late Post

%
o

f
Pa

ti
en

ts
Intervention Time Period

Patients with Medications Missing Dose or Frequency

Test of trend
p < 0.0001

1.5

2.0

2.5

3.0

3.5

4.0

4.5

Baseline Early Late Post
%
o
f
Pa
ti
en
ts
Intervention Time Period

Patients with Duplicate Albuterol Inhalers on
Medication List

Test of trend
p < 0.0001

Figure 2: The three measures were chosen to indicate that the medication list was reconciled at the visit. The unit of mea-
surement for attestation was clinic visits. The unit of measurement for duplicate/equivalent albuterol inhalers, or prescription-
type medications lacking a dose/frequency, was unique patients’ medication lists, counted at a single (last) clinic visit during
the month. Each data point is represented by the rectangles. The vertical lines represent the upper and lower limits of the
95% confidence intervals. The dashed line is a trend line, indicating whether the measure increased or decreased across
time. Baseline period, Quarters 1–2, 2013; Early period, Quarters 3–4, 2013; Late period, Quarters 1–4, 2014; Post period,
Quarters 1–2, 2015. Data were not available for all measures in all periods. See “Analysis” section.

218 Elizabeth Kern, MD, MS, et al Improving Medication Reconciliation

slightly lower than at baseline (35.0%; 95% CI:
34.3%–35.7%).

Patients’ Receipt of Reconciled Medication List

Measures indicating the medication list was printed and
handed to the patient improved (Figure 3). The percent-
ages of patients surveyed who reported that they received

their printed medication list at the end of the visit in-
creased, from 52.3% in the early period to 67.0% in the
postintervention period (p = 0.0074).

Professional Incentives

Of the 89 adult medicine physicians who participated, 39
claimed CME credit and 17 completed attestation of their par-

Early Late Post
%
o
f
V
is
it
s
Intervention Time Period

Visits with Medication List Printed at End of Visit

40
50
60
70
80

Early Post

%
o
f
Pa
ti
en
ts

Intervention Time Period

Patients Reporting Medication List Received at End of
Visit

Test of trend
p < 0.0001

Chi-square
p = 0.0074

Figure 3: The two measures were chosen to indicate that the medication list was printed and given to the patient during
or following the clinic visit. The unit of measurement was clinic visits. Each data point is represented by the rectangles.
The vertical lines represent the upper and lower limits of the 95% confidence intervals. The dashed line is a trend line,
indicating whether the measure increased or decreased across time. Baseline period, Quarters 1–2, 2013; Early period,
Quarters 3–4, 2013; Late period, Quarters 1–4, 2014; Post period, Quarters 1–2, 2015. Data were not available for all mea-
sures in all periods. See “Analysis” section.

30
35
40

Baseline Early Late Post
%
o
f
V
is
it
s
Intervention Time Period

Visits with Patient Education Offered

Test of trend
p < 0.0001

Figure 4: The measure was chosen to indicate that the MA documented in the EHR whether or not he or she offered the
educational handout “Medication Safety Facts” to the patient. MAs were trained to offer the handout and document if
the handout was accepted or refused by the patient at every qualifying visit during which medication lists were recon-
ciled. The unit of measurement was clinic visits. Each data point is represented by the rectangles. The vertical lines represent
the upper and lower limits of the 95% confidence intervals. The dashed line is a trend line, indicating whether the measure
increased or decreased across time. Baseline period, Quarters 1–2, 2013; Early period, Quarters 3–4, 2013; Late period,
Quarters 1–4, 2014; Post period, Quarters 1–2, 2015. MA, medical assistant; EHR, electronic health record.

Volume 43, No. 5, May 2017 219

ticipation for ABIM MOC Part IV points. Fifty-six RNs
received nursing CEUs, and 49 MAs obtained participation
certificates for completing all of their online educational
modules. Two of 5 pharmacists attending a live med rec ed-
ucation session claimed CPE credit.

DISCUSSION

We used a multipronged, evidence-based approach to improve
our med rec process across a large outpatient practice pro-
viding care for patients with respiratory disease and associated
conditions.7,13,16 To stimulate engagement, we incorporat-
ed MOC and continuing professional education credits as
part of the intervention.

Following the intervention, we found that electronically
captured measures of med rec attestation and printing of rec-
onciled medication lists improved across time. The percentage
of patients leaving the clinic with a printed, and presum-
ably reconciled, medication list also increased. These
improvements align with The Joint Commission’s medica-
tion reconciliation requirements under Goal 3, which stipulate
that health care organizations define the types of medica-
tion information to be collected; resolve discrepancies between
the patient-provided information regarding his or medica-
tions lists and provider lists (including elimination of
duplicated medications); and provide patients with written,
reconciled lists at the end of an encounter.

Concurrently, two measures of the quality of the medi-
cation lists improved after the intervention: (1) The percentage
of patients with one or more prescription medications in the
medication list lacking dose or frequency information de-
clined, and (2) the percentage of patients with listed duplicate
or equivalent albuterol inhalers declined. In contrast, elec-
tronic documentation of patient-directed handouts on the
goals of med rec improved only slightly and showed pro-
nounced variability. Almost half of participating physicians
claimed CME credits and/or MOC credits for participat-
ing in the project. Sizable numbers of nurses claimed CEU
credits, and the large majority of MAs employed at our fa-
cility completed training in med rec useful for advancement.

The improvement in medication list quality measures, con-
current with improvements in documentation of attestation,
supports the premise that med rec truly improved in prac-
tice. Similarly, the increased percentage of patients leaving
the clinic with a printed medication list supports the premise
that the observed increase in the electronic commands to
print the medication list reflected real practice improve-
ment. Despite training, the documentation of patient
education handouts offered by MAs failed to show mean-
ingful improvement. Because many patients are seen in NJH
clinics for multiple visits within each month, and the defined
process is to offer the educational handout at each clinic visit
(or document that the patient declined to accept it), the
process is redundant. Some MAs stopped offering the handout
because patients complained they already had multiple copies.

Using educational handouts may be excessive in an outpa-
tient setting, when patients have multiple visits within short
periods of time.

Interventions that have successfully improved the med rec
process have frequently used hospital-based pharmacists, fo-
cusing on medication safety as patients move between
inpatient and outpatient care.7,8,18,19 In contrast, we trained
and incentivized the clinical team of physicians, nurses, and
MAs to perform med rec at outpatient visits. In our facili-
ty, the role of pharmacists in clinical interactions, such as
med rec, is limited. It should be noted, however, that NJH
has dedicated teams of nurses and physicians that continu-
ously reconcile high-risk medications prescribed by our
providers, including warfarin, insulin, and immunosuppres-
sive agents. These activities are outside the scope of the med
rec project reported here.

As in our project, other investigators have measured de-
ficiencies in the quality of medication lists to assess
improvement following an intervention.9,20,21 Direct com-
parisons with our results are problematic because of variability
in the types of medications assessed and differences in the
unit of measurement (for example, unique patients’ lists, or
all medications across all patients). Arundel et al. found that
even after physician-directed training, 12% of patients’ dis-
charge medication lists contained duplicate medications of
any type,9 in contrast to our finding that fewer than 3% of
patients’ lists contained duplicate albuterol inhalers follow-
ing our intervention. Our method of using a computer
algorithm to identify the occurrence of duplicate albuterol
inhalers could be expanded to include other commonly du-
plicated medications by using standardized vocabularies that
describe drugs by, for example, therapeutic class, subclass,
and form of delivery.22,23 When operationalized, electronic
surveillance is advantageous in that it can continuously
monitor medication lists across time, as opposed to cross-
sectional observations requiring trained observers.

Moro Agud et al. found that incomplete documentation
of medication dose, frequency, or route was the most fre-
quent error in medication lists among an outpatient
population of elderly patients with polypharmacy.20 However,
the unit of measurement was defined as incomplete docu-
mentation of dose or frequency among all medications for
all patients, rather than one or more instances of incom-
plete documentation per unique patient’s list, as in our study.
The difference in methodology hinders direct compari-
sons, but we similarly found that lack of dose or frequency
documentation was more common than the listing of du-
plicate medications.

As opposed to our method of examining deficiencies in
a single medication list recorded in the medical chart, many
studies have examined discrepancies between home-based
medication histories and medical chart–based medication
lists.2,8,12,15,24–30 Common discrepancies are failure to list in
the medical chart medications taken at home and failure to
remove medications no longer taken.3,24 Measuring such

220 Elizabeth Kern, MD, MS, et al Improving Medication Reconciliation

discrepancies is not possible when examining only the single
list in the EHR, as in our study. A technology-based tool
to measure discrepancies becomes possible only when there
are two structured medication lists to compare.31–33 Such ca-
pabilities are not yet commonplace in EHR systems, but there
is demand that EHR systems support Meaningful Use goals
with improved functionality.34 A recent systematic review
found 18 reports of electronic tools developed to support
med rec. Of these, half were able to identify medication
discrepancies.35

In our study, the absolute changes in our measures for med-
ication list “deficiencies” (lack of dose or frequency, and listing
of duplicate albuterol inhalers) were much smaller than the
changes in attestation and printing of medication lists. In
part, this is expected because the use of electronic attesta-
tion was not required and providers were generally unaware
of it prior to our intervention. Therefore the opportunity
for improvement was large. Conversely, the absolute per-
centage of patients with medication list deficiencies at baseline
was less than 20%, with a smaller opportunity for improve-
ment. An additional factor may be that attestation for med
rec completion does not correlate precisely with medica-
tion list deficiencies; for example, a patient unable to report
dose or frequency of medications taken at home, despite best
efforts to obtain the BPMH, will cause the attestation measure
and the quality measure to diverge.

We observed discrepancies between the electronic measure
for printing of medication lists and the manual patient survey.
At baseline, the electronic measure showed that medica-
tion lists were printed at fewer than 20% of clinic visits, yet
more than 50% of patients sampled as they left the clinic
reported they were given a print copy of their medication
list. Investigation revealed that physicians frequently gave pa-
tients the initial, printed list from the MAs interview, with
handwritten changes. While this practice saves paper, it does
not meet the Meaningful Use criterion that a printed clinic
summary containing the reconciled medication list is given
to the patient at the end of the visit.36 Further, it does not
ensure that medication changes are appropriately reflected
in the EHR–based list. Therefore, physicians were edu-
cated to print the electronically edited, reconciled list from
the EHR a second time, at the end of the visit.

Limitations

Our study lacked a control group because our objective was
to standardize the med rec process across the entire medical
facility. Baseline assessment of our med rec process was not
possible for several measures because implementing the
measure was part of the intervention. Therefore, we are limited
in inferring causality between our interventions and im-
provement in the med rec process in the period following
the intervention. We inadvertently lost interim data on the
measure for lack of dose and frequency of prescription type
medications. This error highlights the need for team-
approved communication to the IT team at all times. Our

project is limited by being a single-center study in an am-
bulatory setting and may not generalize to other types of
health care settings. Measuring discrepancies such as medi-
cations missing or extraneous medications included on EHR
medication lists is beyond the scope of our study and our
measures.

Implications for Improvement Practices

The quality of the med rec process should be measurable
to assess improvement. Although the Allscripts EHR is capable
of reporting an electronic attestation of med rec and the print-
ing of the medication list at the time of a visit, we further
required that the attestation and print commands had to occur
at the time of, or shortly after, the scheduled visit time to
“count” as properly done. In addition, we sought to support
these measures with concurrent measures of the quality of
the medication lists. Although the quality measures we used
are not sufficiently comprehensive to uncover all errors within
a medication list, they function as a proxy for providers’ at-
tention to the med rec process.

Lack of standards for documenting med rec within dif-
ferent EHR systems may impede efforts to measure the med
rec process. Keogh et al. augmented the global electronic at-
testation of med rec with a system to record med rec at each
visit for each medication previously prescribed by an indi-
vidual provider.31 The specificity and provider-accountability
of the measures helped drive performance improvement.
However, switching to a different EHR system halted the
measurement until the data entry capability was added to
the new system.

In contrast, the measures we devised to assess the quality
of the medication lists are not EHR system–specific since
they do not depend on innovative data entry. Most EHR
systems have a queryable relational database system to extract
structured encounter and medication data from the EHR.
Such a database can be used to design reports examining speci-
fied deficiencies in the medication lists, using operational
definitions of clinic encounters, providers, and types of medi-
cations to be assessed.

Greenwald et al. called for methods to proactively iden-
tify patients at risk for poor reconciliation and medication-
related adverse events.10 Data derived from EHR medication
lists, such as we used in our quality measures, could be used
to develop computer-based algorithms identifying high-
risk patients (for example, elderly patients, patients with high
numbers of medications) or patients with deficiencies in their
medication lists (for example, medications lacking dose and
frequency). Supplemental med rec interventions and patient
education might be directed to these patients.

Periodically reporting med rec measures to individual pro-
viders stimulated engagement. When reporting started,
physicians were surprisingly invested in the med rec process,
and helped us to redesign our measures to more closely co-
incide with clinic work flow. We found that clinic workspace
logistics, such as location and maintenance of printers, made

Volume 43, No. 5, May 2017 221

it difficult to comply with the NPSG.03.06.01 require-
ment to hand reconciled lists to patients. Requiring a
redundant process, such as offering educational handouts to
patients at every encounter, was considered wasteful and led
to variable performance quality. Regarding the rollout and
monitoring of the project, we found that accurate commu-
nication from the performance team to the IT team is vital:
We lost data due to a single instance of miscommunica-
tion and failure to monitor the measure in real time.
Designating a single source for communication, and close
monitoring of the reports, are necessary on the perfor-
mance team side.

Next Steps

Interventions to improve med rec require thought and effort
beyond the basics of NPSG.03.06.011 and are frequently ex-
pensive to implement.37 Our aim was to design, implement,
and validate measures that could sustain provider engage-
ment and performance.

We currently report two measures to individual clini-
cians on a quarterly basis (attestation of med rec per clinic
visit, printing of the medication lists per clinic visit). Phy-
sician performance is rewarded with financial incentives,
starting in 2016. We continue to collect data for our sup-
porting quality measures. Since the conclusion of the
intervention phase, the definitions we originally used for the
measures have changed. We broadened the definition of ac-
countable providers to include mid-level providers, and now
include new, as well as follow-up visits. Thus, the denom-
inator has expanded. We plan to report the measures for
medication list quality to individual physicians and mid-
level providers in 2017. We plan to target other commonly
duplicated medications, such as proton pump inhibitors and
antihypertensive agents, for “clean-up” on the EHR–based
lists. For patient education, we eliminated paper handouts
and will instead have the MAs ask each patient, “Do you
keep an up-to-date Medication List?” Electronic documen-
tation of possible answers include “yes” or “no, but patient
knows it is recommended.”

Toward the end of the project, medication lists became
viewable on NJH’s patient portal, a secure online resource
displaying selected parts of the EHR chart for individual pa-
tients. By December 2016, 18,272 patients (48%) had portal
access. Physicians now have the option to include the rec-
onciled medication list as an addendum to their clinic note,
which is faxed to referring health care providers. The ad-
dendum eliminates the need to dictate the medication list
into the body of the note. Currently, about 50% of dic-
tated notes use the reconciled list as an addendum.

It is common for pharmacy benefits managers to require
substitutions in brand and classes of prescribed medica-
tion. Such changes typically occur between visits. Because
the Allscripts EHR uses e-prescribe software, newly pre-
scribed medications automatically display name, route, dose,
and frequency. However, changes could create a duplica-

tion error if the original medication is not deleted. Our clinic
providers have been educated to update the medication list
to reflect between-visit changes, as part of a continuous med
rec process. At this point, med rec attestation between visits
is not required but simply encouraged.

To sustain the med rec process, newly hired MAs are re-
quired to complete the med rec online modules during their
orientation. MAs will be required to repeat the modules every
three years. Training of new MAs continues via the Web-
based training we created for the intervention, and new
physicians and nurses receive individual training from our
quality staff. Additional resources, including a video round-
table discussion and MA training materials, may be found
online.36

CONCLUSION

Our study showed that deriving electronic measures that
reflect the quality of clinicians’ performance of med rec is
feasible and that such measures are sustainable over the time
periods necessary to demonstrate change. Electronic mea-
sures of med rec performance may be validated by concurrent,
complementary indicators of medication list quality that
change in the same direction, across time.

Funding. This study was funded by an independent educational grant from
GlaxoSmithKline (Grant Request Reference # 007732).
Acknowledgments. The authors thank Joy Zimmer and Ken Gonzales,
of the Information Service and Technology Department at National Jewish
Health, for their expertise and help in designing and implementing the
measures of medication reconciliation. They also thank Mandy Comeau,
of the Office of Professional Education at National Jewish Health, for overall
assistance in coordinating the project.
Conflicts of Interest. The authors report no conflicts of interest.

Elizabeth Kern, MD, MS, is Director, Health Outcomes, and Associate
Professor, Department of Medicine, National Jewish Health, Denver. Meg
B. Dingae, MHSA, formerly Manager, Educational Grants and Collabo-
rations, Office of Professional Education, National Jewish Health, is Strategy
and Corporate Development Consultant, Colorado Permanente Medical
Group, Denver. Esther L. Langmack, MD, formerly Medical Director, Office
of Professional Education, and Associate Professor, Department of Med-
icine, is Medical Director, Education, Aegis Creative Communications,
Lakewood, Colorado. Candace Juarez, MT, is Quality Improvement Co-
ordinator, Department of Clinical Affairs; Gary Cott, MD, is Executive Vice
President, Clinical Affairs; and Sarah K. Meadows, MS, is Manager, Ac-
creditation and Programs, Office of Professional Education, National Jewish
Health. Please address correspondence to Elizabeth Kern, kerne@
njhealth.org.

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Measuring to Improve Medication Reconciliation in a Large Subspecialty Outpatient Practice

Methods
Setting
Interventions
Standardizing the Med Rec Process
Training
Professional Incentives
Measures
Process Documentation
Quality Indicators
Patient Education
Patients’ Receipt of Reconciled Medication List
Qualifying Visits for Medication Reconciliation
Analysis
Results
Process Documentation
Quality Indicators
Patient Education
Patients’ Receipt of Reconciled Medication List
Professional Incentives
Discussion
Limitations
Implications for Improvement Practices
Next Steps
Conclusion
References

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