Measuring to Improve Medication Reconciliationin a Large Sub.docx

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 medications
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
1553-7250/$-see front matter
© 2017 The Joint Commission. Published by Elsevier Inc. All
rights reserved.
http://dx.doi.org/10.1016/j.jcjq.2017.02.005
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 patients
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.14,15
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), 40
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.pdf.

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.pdf
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 20
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.
2

1
6
E
liza
b
e
th
K
e
rn
,
M
D
,
M
S
,
e
t
a
l
Im
p
ro
vin

g
M
e
d
ica
tio
n
R
e
co
n
cilia
tio
n
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

30
40
50
60
70
80
90
Early Late Post
%
o
f
V
is
it
s
Intervention Time Period
Visits with Attestation of Medication List Reconciled
15
16
17
18
19
20

Baseline Early Late Post
%
o
f
Pa
ti
en
ts
Patients with Medications Missing Dose or Frequency
Test of trend
p < 0.0001
Test of trend
p < 0.0001
1.5
2.0
2.5
3.0
3.5
4.0

4.5
%
o
f
Pa
ti
en
ts
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.
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-
15
35
55
75
95

Early Late Post
%
o
f
V
is
it
s
Visits with Medication List Printed at End of Visit
40
50
60
70
80
Early Post
%
o
f
Pa

ti
en
ts
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.
20
25
30
35

40
45
%
o
f
V
is
it
s
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
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,
[email protected]
njhealth.org.
REFERENCES
1. The Joint Commission. 2017 Comprehensive Accreditation
Manual for Ambulatory Care (E-dition). Oak Brook, IL: Joint
Commission Resources, 2016.
2. Coleman EA, et al. Posthospital medication discrepancies:
prevalence and contributing factors. Arch Intern Med. 2005
Sep 12;165:1842–1847.

3. Andrus MR, Anderson AD. A retrospective review of student
pharmacist medication reconciliation activities in an
outpatient family medicine center. Pharm Pract (Granada).
2015;13:518.
Reconciliation
mailto:[email protected]
http://refhub.elsevier.com/S1553-7250(17)30063-6/sr0010
4. Makaryus AN, Friedman EA. Patients’ understanding of their
treatment plans and diagnosis at discharge. Mayo Clin Proc.
2005;80:991–994.
5. Lee KP, et al. Predictors of completeness of patients’ self-
reported personal medication lists and discrepancies with
clinic medication lists. Ann Pharmacother. 2014;48:168–177.
6. Leonhardt KK, et al. Creating an accurate medication list in
the outpatient setting through a patient-centered approach.
In: Henriksen K, et al., eds. Patient Safety: New Directions
and Alternative Approaches, vol. 3. Performance and Tools.
Rockville, MD: Agency for Healthcare Research and Quality,

2008 Accessed Feb 24, 2017. https://www.ncbi.nlm.nih.gov/
books/NBK43679/.
7. Mueller SK, et al. A toolkit to disseminate best practices in
inpatient medication reconciliation: Multi-Center Medication
Reconciliation Quality Improvement Study (MARQUIS). Jt
Comm J Qual Patient Saf. 2013;39:371–382.
8. Kwan JL, et al. Medication reconciliation during transitions
of care as a patient safety strategy: a systematic review. Ann
Intern Med. 2013 Mar 5;158:397–403.
9. Arundel C, et al. Safe medication reconciliation: an
intervention to improve residents’ medication reconciliation
skills. J Grad Med Educ. 2015;7:407–411.
10. Greenwald JL, et al. Making inpatient medication
reconciliation patient centered, clinically relevant and
implementable: a consensus statement on key principles and
necessary first steps. J Hosp Med. 2010;5:477–485.
11. Nassaralla CL, et al. Implementation of a medication
reconciliation process in an ambulatory internal medicine
clinic. Qual Saf Health Care. 2007;16:9–94.
12. Orrico KB. Sources and types of discrepancies between
electronic medical records and actual outpatient medication
use. J Manag Care Pharm. 2008;14:626–631.
13. Salanitro AH, et al. Rationale and design of the Multicenter
Medication Reconciliation Quality Improvement Study
(MARQUIS). BMC Health Serv Res. 2013 Jun 25;13:230.
14. Bourgeois FT, et al. Adverse drug events in the outpatient
setting: an 11-year national analysis. Pharmacoepidemiol
Drug Saf. 2010;19:901–910.

15. Bedell SE, et al. Discrepancies in the use of medications:
their
extent and predictors in an outpatient practice. Arch Intern
Med. 2000 Jul 24;160:2129–2134.
16. 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.
17. Centers for Medicare & Medicaid Services. Eligible
Professional Meaningful Use Menu Set Measures: Measure
6 of 9: Stage 1 (2014 Definition). Updated May 2014.
Accessed Feb 24, 2017. https://www.cms.gov/Regulations-
and-Guidance/Legislation/EHRIncentivePrograms/
downloads/7_Medication_Reconciliation.pdf.
18. Bernstein L, et al. Medication reconciliation: Harvard
Pilgrim
Health Care’s approach to improving outpatient medication
safety. J Healthc Qual. 2007;29:40–45, 55.
19. Conklin JR, et al. Care transitions service: a pharmacy-
driven
program for medication reconciliation through the continuum
of care. Am J Health Syst Pharm. 2014 May 15;71:802–810.
20. Moro Agud M, et al. Analysis of an electronic medication
reconciliation and information at discharge programme for
frail elderly patients. Int J Clin Pharm. 2016;38:996–1001.
21. Nassaralla CL, et al. Medication reconciliation in
ambulatory
care: attempts at improvement. Qual Saf Health Care.

2009;18:402–407.
22. Lopez-Garcia P, et al. Cross-domain targeted ontology
subsets for annotation: the case of SNOMED CORE and
RxNorm. J Biomed Inform. 2014;47:105–111.
23. Phansalkar S, et al. Towards meaningful medication-related
clinical decision support: recommendations for an initial
implementation. Appl Clin Inform. 2011 Feb 9;2:50–62.
24. Azzi M, et al. Medication safety: an audit of medication
discrepancies in transferring type 2 diabetes mellitus (T2DM)
patients from Australian primary care to tertiary ambulatory
care. Int J Qual Health Care. 2014;26:397–403.
25. Cornish PL, et al. Unintended medication discrepancies at
the time of hospital admission. Arch Intern Med. 2005 Feb
28;165:424–429.
26. Gleason KM, et al. Reconciliation of discrepancies in
medication histories and admission orders of newly
hospitalized patients. Am J Health Syst Pharm. 2004 Aug
15;61:1689–1695.
27. Manley HJ, et al. Drug record discrepancies in an outpa-
tient electronic medical record: frequency, type, and potential
impact on patient care at a hemodialysis center. Pharma-
cotherapy. 2003;23:231–239.
28. Pippins JR, et al. Classifying and predicting errors of
inpatient
medication reconciliation. J Gen Intern Med. 2008;23:1414–
1422.
29. Tam VC, et al. Frequency, type and clinical importance of
medication history errors at admission to hospital: a

systematic review. CMAJ. 2005 Aug 30;173:510–515.
30. Wong JD, et al. Medication reconciliation at hospital
discharge: evaluating discrepancies. Ann Pharmacother.
2008;42:1373–1379.
31. Keogh C, et al. Ambulatory medication reconciliation: using
a collaborative approach to process improvement at an
academic medical center. Jt Comm J Qual Patient Saf.
2016;42:186–194.
32. Schnipper JL, et al. Development of a tool within the
electronic medical record to facilitate medication
reconciliation after hospital discharge. J Am Med Inform
Assoc. 2011 May 1;18:309–313.
33. Zoni AC, et al. The impact of medication reconciliation
program at admission in an internal medicine department.
Eur J Intern Med. 2012;23:696–700.
34. Krist AH, et al. Electronic health record functionality
needed
to better support primary care. J Am Med Inform Assoc.
2014;21:764–771.
35. Marien S, Krug B, Spinewine A. Electronic tools to support
medication reconciliation: a systematic review. J Am Med
Inform Assoc. 2017;24:227–240.
36. National Jewish Health. Medication Reconciliation.
Accessed
Feb 24, 2017. https://www.njhealth.org/medication-
reconciliation.
37. Pevnick JM, Shane R, Schnipper JL. The problem
with medication reconciliation. BMJ Qual Saf. 2016;25:726–

730.
Volume 43, No. 5, May 2017 223
https://www.ncbi.nlm.nih.gov/books/NBK43679/
https://www.ncbi.nlm.nih.gov/books/NBK43679/

https://www.cms.gov/Regulations-and-
Guidance/Legislation/EHRIncentivePrograms/downloads/7_Med
ication_Reconciliation.pdf

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
Running head: Improving medication reconciliation and
education 1
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
Chamberlain College of Nursing NR449 Evidence-
Based PracticeEvidence 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.
1
2
3

4
5
NR449 Evidence Matric Table.docx
Revised10/20/14 ns/cs
1
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: [email protected]
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
http://crossmark.crossref.org/dialog/?doi=10.1177%2F10628606
15574327&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
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
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.
References
1. Wachter RM, Goldman L. The hospitalist movement 5
years later. JAMA. 2002;287:487-494.
2. Jungerwirth R, Wheeler SB, Paul JE. Association of hospi-
talist presence and hospital-level outcome measures among
medicare patients. J Hosp Med. 2014;9:1-6.
3. Goldman L, Pantilat SZ, Whitcomb WF. Passing the clini-
cal baton: 6 principles to guide the hospitalist. Am J Med.
2001;111:36S-39S.
4. Halasyamani L, Kripalani S, Coleman E, et al. Transition
of care for hospitalized elderly patients–development
of a discharge checklist for hospitalists. J Hosp Med.
2006;1:354-360.
5. Kripalani S, Jackson AT, Schnipper JL, Coleman EA.
Promoting effective transitions of care at hospital dis-
charge: a review of key issues for hospitalists. J Hosp Med.
2007;2:314-323.
6. Kripalani S, LeFevre F, Phillips CO, Williams MV,
Basaviah P, Baker DW. Deficits in communication and
information transfer between hospital-based and primary
care physicians: implications for patient safety and conti-

Measuring to Improve Medication Reconciliationin a Large Sub.docx

Measuring to Improve Medication Reconciliationin a Large Sub.docx

Recommended

Recommended

More Related Content

Similar to Measuring to Improve Medication Reconciliationin a Large Sub.docx

Similar to Measuring to Improve Medication Reconciliationin a Large Sub.docx (20)

More from alfredacavx97

More from alfredacavx97 (20)

Recently uploaded

Recently uploaded (20)

Measuring to Improve Medication Reconciliationin a Large Sub.docx