EHR‐Based Medication Support and Nurse‐Led Medication Therapy Management: Rationale and Design for a Three‐Arm Clinic Randomized Trial

Abstract

Patients with chronic conditions often use complex medical regimens. A nurse-led strategy to support medication therapy management incorporated into primary care teams may lead to improved use of medications for disease control. Electronic health record (EHR) tools may offer a lower-cost, less intensive approach to improving medication management.
The Northwestern and Access Community Health Network Medication Education Study is a health center-level cluster-randomized trial being conducted within a network of federally qualified community health centers. Health centers have been enrolled in groups of 3 and randomized to (1) usual care, (2) EHR-based medication management tools alone, or (3) EHR tools plus nurse-led medication therapy management. Patients with uncontrolled hypertension who are prescribed ≥3 medications of any kind are recruited from the centers. EHR tools include a printed medication list to prompt review at each visit and automated plain-language medication information within the after-visit summary to encourage proper medication use. In the nurse-led intervention, patients receive one-on-one counseling about their medication regimens to clarify medication discrepancies and identify drug-related concerns, safety issues, and nonadherence. Nurses also provide follow-up telephone calls following new prescriptions and periodically to perform medication review. The primary study outcome is systolic blood pressure after 1 year. Secondary outcomes include measures of understanding of dosing instructions, discrepancies between patient-reported medications and the medical record, adherence, and intervention costs.
The Northwestern and Access Community Health Network Medication Education Study will assess the effects of 2 approaches to support outpatient medication management among patients with uncontrolled hypertension in federally qualified health center settings.
clinicaltrials.gov. Unique identifier: NCT01578577.

Full text

French, Jennifer King and Michael S. Wolf
Stephen D. Persell, Milton Eder, Elisha Friesema, Corinne Connor, Alfred Rademaker, Dustin D.
Arm Clinic Randomized Trial−and Design for a Three
Led Medication Therapy Management: Rationale−Based Medication Support and Nurse−EHR
Online ISSN: 2047-9980
Dallas, TX 75231
is published by the American Heart Association, 7272 Greenville Avenue,Journal of the American Heart AssociationThe
doi: 10.1161/JAHA.113.000311
2013;2:e000311; originally published October 24, 2013;J Am Heart Assoc.
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EHR-Based Medication Support and Nurse-Led Medication Therapy
Management: Rationale and Design for a Three-Arm Clinic
Randomized Trial
Stephen D. Persell, MD, MPH; Milton Eder, PhD; Elisha Friesema, CCRP; Corinne Connor, RN, BSN, CIC; Alfred Rademaker, PhD;
Dustin D. French, PhD; Jennifer King, MPH; Michael S. Wolf, MA, MPH, PhD
Background-—Patients with chronic conditions often use complex medical regimens. A nurse-led strategy to support medication
therapy management incorporated into primary care teams may lead to improved use of medications for disease control. Electronic
health record (EHR) tools may offer a lower-c ost, less intensive approach to improving medication management.
Methods and Results-—The Northwestern and Access Community Health Network Medication Education Study is a health center–
level cluster-randomized trial being conducted within a network of federally qualified community health centers. Health centers
have been enrolled in groups of 3 and randomized to (1) usual care, (2) EHR-based medication management tools alone, or (3) EHR
tools plus nurse-led medication therapy management. Patients with uncontrolled hypertension who are prescribed ≥3 medications
of any kind are recruited from the centers. EHR tools include a printed medication list to prompt review at each visit and automated
plain-language medication information within the after-visit summary to encourage proper medication use. In the nurse-led
intervention, patients receive one-on-one counseling about their medication regimens to clarify medication discrepancies and
identify drug-related concerns, safety issues, and nona dherence. Nurses also provide follow-up telephone calls following new
prescriptions and period ically to perform medication review. The primary study outcome is systolic blood pressure after 1 year.
Secondary outcomes include measures of understanding of dosing instructions, discrepancies between patient-reported
medications and the medical record, adherence, and intervention costs.
Conclusions-—The Northwestern and Access Community Health Network Medication Education Study will assess the effects of 2
approaches to support outpatient medication management among patients with uncontrolled hypertension in federally qualified
health center settings.
Clinical Trial Registration-—URL: clinicaltrials.gov. Unique identifier: NCT01578577. ( J Am Heart Assoc. 2013;2:e 000311 doi:
10.1161/JAHA.113.000311)
Key Words: adherence • electronic health records • hypertension • medication reconciliation • medication therapy
management • nurse educ ator
P
atients with chronic conditions are asked to use
increasingly complex medical regimens.
1
Long-term
proper adherence is essential to reap the health benefits
demonstrated in clinical trials; however, nonadherence is
widespread and is linked to worse outcomes including
increased mortality.
2
For major chronic illnesses, common
forms of nonadherence include failure to fill new prescrip-
tions,
3
incomplete adherence to medications being used,
4
and
premature discontinuation.
5
Complex drug regimens increase
the risk for medication errors and adverse drug events.
Outpatient adverse drug events are prevalent, and many are
either preventable or ameliorable.
6
Individuals with chronic
illness and the elderly are at greatest risk for unintentional
medication errors, failing to properly self-administer a med-
ication as intended.
6–8
For outpatient care, patients (or their surrogates) are
primarily responsible for executing medication care plans. The
expectations for medication management placed on patients
by the healthcare system are considerable, with patients
expected to perform multiple steps to adhere to their care
From the Division of General Internal Medicine and Geriatrics, Feinberg School
of Medicine, Northwestern University, Chicago, IL (S.D.P., E.F., J.K., M.S.W.);
Access Community Health Network, Chicago, IL (M.E., C.C.); Departments of
Ophthalmology and Center for Healthcare Studies (D.D.F.) and Preventive
Medicine (A.R.), Feinberg School of Medicine, Northwestern University,
Chicago, IL.
Correspondence to: Stephen D. Persell, MD, MPH, Division of General
Internal Medicine and Geriatrics, Feinberg School of Medicine, Northwestern
University, 750 North Lake Shore Drive, 10th Floor, Chicago, IL 60611. E-mail:
spersell@nmff.org
Received July 29, 2013; accepted September 15, 2013.
ª 2013 The Authors. Published on behalf of the American Heart Association,
Inc., by Wiley Blackwell. This is an open access article under the terms of the
Creative Commons Attribution-NonCommercial License, which permits use,
distribution and reproduction in any medium, provided the original work is
properly cited and is not used for commercial purposes.
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plan and gain the benefits of chronic drug therapy. Figure 1
provides a conceptual schema of the tasks involved, how they
relate to outcomes, and how the process can break down.
Ideally, adherence to the care plan begins with an exchange of
information between prescriber and patient followed by their
agreement on an appropriate medication plan. As a result of
these interactions or through information obtained from other
sources, patients acquire the information they will use to
administer medications on an ongoing basis. Information
about prescription medication may come from sources
directly involved in the prescribing and dispensing process
(prescriber/other healthcare team member, prescription,
pharmacist, medication guides, prescription labels, or auxil-
iary labels) or from other sources (references, Internet, family,
friends, advertising).
9
Patients and their healthcare providers
must also recognize safety problems or adverse events when
they occur and modify the medication care plan when
necessary. Patients and providers must also monitor the
efficacy and tolerability of prescribed medications. Changes
to the medication plan are common in response to changing
disease conditions, adverse effects, cost considerations, or
lack of efficacy. Patients are the ones who must successfully
execute these changes. New medicines may be added, others
are discontinued, and dosages may be altered. Errors in any of
these steps could lead to adverse drug events or reduced
efficacy of treatment.
A basic understanding of one’s medication (the indications
for it, how to administer, adverse effects to be aware of) is an
essential prerequisite for a safe, successful execution of a
medication plan. Unfortunately, evidence indicates that
important medication information is delivered to patients in
a haphazard way. Multiple studies show that physicians
frequently do not adequately counsel patients on safe and
appropriate drug use.
10,11
Furthermore, physicians rarely
assess patient comprehension
12
or discuss medication
affordability even though out-of-pocket cost directly influ-
ences adherence.
13
In routine outpatient practice, pharma-
cists seldom provide medication counseling.
10
The information patients do receive about their medications
may not be provided in a manner that supports prescribed use.
Studies indicate that written medication information that
accompanies prescription drugs is difficult to understand for
many patients.
14
Recent studies have repeate dly shown that a
large proportion of patients have difficulty performing routine
medication management tasks, such as correctly interpreting
dosing instructions
8,15
and warning labels on prescribed
medications,
8
accurately and completely self-reporting cur-
rently used medications, and possessing knowledge of the
basic indications for prescribed medicine.
16–18
In several
studies, limited literacy was associated with an increased risk
for these medication-related problems.
7,8,15,17
Discrepancies between the medications patients are taking
and the medications healthcare providers believe the patients
are taking may indicate the presence of ≥1 important
problem: (1) patients may unknowingly be out of agreement
with the medication care plan intended by their providers (eg,
they may have inadvertently not started a medication or failed
to realize that their clinicians intended them to stop using a
medication); (2) patients may be deliberately not adhering;
(3) the healthcare team may have made errors (eg, errors in
documentation or actual errors in care); and (4) the healthcare
team may be unaware of medications a patient obtained
elsewhere. Without systems in place to regularly encourage
Figure 1. Conceptual schema for executing chronic disease med-
ication care plans. A, Optimal planning and execution of medication
care plan for chronic illness care. B, Obstacles to successful
medication care plan planning and execution.
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medication reconciliation, these important problems may go
unaddressed.
Multiple recent studies have shown that medication
discrepancies are highly prevalent. Physicians rarely perform
a comprehensive review of chronic medications
19
and there-
fore may be unaware when patients are not taking essential
medications or using medications that were discontinued.
Problems result in either scenario, as patients become at risk
for potentially dangerous use of medications that were
stopped for medical reasons or duplication of medications
when a substitution from 1 me dication to the next was
intended. Medications listed in patients’ medical records are
frequently discordant with the medications patients report
taking.
17,20–22
Medication discrepancies are particularly com-
mon among patients using multiple medications who have
been recently hospitalized.
23–25
Among adults receiving care at community health centers
in a prior study, patients with low literacy were more likely to
have medication discrepancies for their hypertension medi-
cations than were patients with adequa te literacy.
17
Medica-
tion reconciliation errors have been associated with worse
blood pressure control
16
and can curtail patie nt safety
benefits available through the use of the electronic health
record (EHR). If medications are not recorded within a
patient’s EHR, safety features such as the detection of drug–
drug interactions or allergies will not function properly.
Improving Medication Use Through Medication
Therapy Management
Medication therapy management (MTM) has evolved as a
systematic approach to assist patients with many of the
medication-related problems described above. Formally intro-
duced with the implementation of Medicare Part D, MTM now
serves as a mandate to Part D insurers to provide qualifying
patients with medication assistance. As defined by the
American Pharmacists Association, MTM includes medication
review, assembly of a personal medication record, develop-
ment of patient medication-relate d action plans, clinical
interventions when necessary, and follow-up.
26
The rationale
for this program is to provide Medicare beneficiaries who have
high drug complexity and high drug cost with additional
education and support to improve medication adherence,
improve the detection of medication misuse, and reduce
adverse drug events.
27
Published outcomes from MTM are scant and subject to
important methodological limitations. In the Iowa State Medi-
caid Pharmaceutical Case Management program, MTM reduced
the number of potentially inappropriate medications used, but
the comparison group was not randomly assigned. In this study,
healthcare utilization did not change, and important clinical
outcomes were not examined.
28
Another report indicated that
an MTM intervention increased medication kno wledge but not
adherence, although there was no control group.
29
A Minnesota
Blue Cross Blue Shield (BCBS) study done within medical
practices compared with historical controls suggested that the
MTM program improved the achievement of therapeutic goals
and significantly reduced healthcare costs.
30
We are not aware
of well-controlled trials performed to examine the impact of a
general MTM interve ntion on clinical outcomes. Rigorously
testing viable MTM approaches, particularly among high-risk
groups such as those with limited literacy, is clearly warranted.
In contrast to the limited evidence base for general MTM
interventions, multiple disease-specific interventions that
have employed some features of MTM have shown beneficial
effects. In the case of hypertension, controlled trial s employ-
ing pharmacists to perform several hypertension-related tasks
for the most part have shown beneficial effects on blood
pressure. A meta-analysis of 13 controlled trials revealed
greater reductions in systolic blood pressure in intervention
groups compared with controls (6.9 mm Hg difference).
Interpretation of these studies is somewhat limited because
of their quality and the possi bility of publication bias.
31
Other
recent trials employing pharmacists have also shown bene-
ficial effects in hypertension.
32,33
Studies employing nurses
have been mixed, with studies showing positive effects
34–36
or no effect.
37,38
Similar to hypertension, the findings in
interventions aimed at improving diabetes control using
pharmacists or nurses have generally been favorable but are
similarly heterogeneous.
33,39
These disease-specific studies
generally support the notion that the addition of a pharmacist
or nurse to the larger care delivery team can produce
favorable results. A recent meta-analysis of interventions to
improve adherence among patients with cardiovascular
disease or diabetes also supports this conclusion.
40
However,
it is impossible to apply conclusions from disease-specific
studies (which targeted disease-specific care processes) to
general MTM approaches. Whether non-disease-focused MTM
interventions change important indicators of chronic disease
control is essentially unknown.
A specific limitation to pharmacy-based MTM (as opposed
to MTM delivered by a member of the primary care team) is
that medication review performed separately from patients’
usual source of care may not effectively detect medication
discrepancies, as the third-party pharmacist would not know
firsthand the intent of prescribing clinicians. Also most data
on monitoring and disease control would not be available to
the pharmacy-based provider. This study will test the
effectiveness of MTM provided by a clinician based within a
patient’s primary care practice.
MTM has been conceptualized as a task performed by a
clinician (typically a pharmacist).
26,27
This approach would be
costly if widely applied to the large population of patients with
chronic disease using multiple chronic medications. Under
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Medicare Part D, only individuals with anticipated annual drug
costs of >$4000 would be eligible for reimbursed MTM,
thereby excluding many patients who could potentially benefit
from these services and potentially elim inating systemic
economic gains. However, health information technology
could be leveraged to provide some MTM features at a cost
that is not prohibitive. Contemporary EHRs could serve as a
platform from which to deliver automated tools to assist with
the medication management process for all patients without
requiring additional clinical personnel. Once developed, these
MTM tools could be widely applied at increasingly diminishing
incremental costs.
A substantial portion of medication nonadherence is not a
result of a failure to understand basic medication tasks but
rather of patients’ deliberate choices not to use medications.
Research on health behaviors has identified several important
determinants of whether an individual performs a specific
health behavior: attitudes toward the behavior, the perception
of social norms, and one’s own sense of self-effi cacy in
performing the behavior.
41
Patients may have negative
attitudes or confl icting beliefs toward the efficacy of certain
medications or be influenced by friends or family to not use a
prescribed medicine. The limited interactions between
patients and their providers and currently used print material
may not be sufficient to overcome these negative perceptions
of medications. One’s sense of self-efficacy may be reduced if
dosing requirements are too burdensome or if cost barriers to
obtaining specific medications are not addressed.
13,42
The
approach we have outlined may help patients to consciously
make decisions about their medications that would more
likely promote desired health outcomes. For instance, the
focus on medication list review will ideally help to initiate
provider–patient discussions about medications a patient has
chosen not to use so that providers have additional oppor-
tunities to engage in discussions about nonadherence that
could affect patients’ knowledge, attitud es, and behaviors.
Similarly, providing simplified medication information sheets
should be a more reliable way to give patients salient
information about the purpose of using medications that
could foster more favorable attitudes at a critical time (poin t
of prescribing).
Although potentially cost-prohibitive for many primary care
settings, the inclusion o f a healthcare professional may be
necessary to confirm patients receive the EHR-based MTM
materials, subsequently understand how to safely administer
their current regimens, and continually adhere to their
medicines to achieve optimal health outcomes. A trained
clinician who is specifically tasked with helping patients with
medication management could use these tools while directly
assisting patients with medication list review, information
exchange, and dosing simplification. Although prior MTM
strategies have used both nurses and pharmacists, availability
and cost would likely favor the use of a nurse in primary care
settings.
Feasible and sustainable approaches to help patients
safely execute complicated outpatient medical regimens are
needed. The overall objective of this study is to rigorously
evaluate 2 related approaches to improving patients’ med-
ication self-management in primary care settings. We hypoth-
esized that hypertension patients receiving EHR-based
medication management tools alone or EHR tools plus
nurse-led medication management education, compared with
usual care, would have lower systolic blood pressure, better
understanding of dosing instructions, fewer disc repancies
between patient-reported medications and the medical
record, and better medication adherence.
Methods
Health Center Enrollment
Health centers that are part of the Access Community Health
Network in the greater Chicago, Illinois, area are eligible for
participation if they have completed their implementation of
the common EHR shared by the network (EpicCare, Epic
Systems Corporation, Verona, WI). Study investigators
approached health center leadership, explained the study to
them and requested permission to participate. Because the
print materials are in English, centers with predominantly non-
English-speaking patient populations were not approached by
study investigators. The institutional review board of North-
western University reviewed and approved the study.
Randomization
The flow of health centers and patients is dep icted in
Figure 2. Centers have been enrolled in groups of 3 and
randomized to (1) us ual care, (2) EHR-based medication
management tools alone, or (3) EHR tools plus nurse-led
medication management education. For the first group of
health centers randomized, random numbers were manually
selected in a blind fashion by someone not involved with the
study, and the allocation of the random number to the study
arms was concealed until the randomization was completed.
For each subsequent round of randomization of new centers,
we use a weighted randomization scheme to increase the
likelihood that study arm sizes remained similar. We examined
the existing numbers of patients enrolle d in each of the 3
study arms to date, and estimated numbers of patients
anticipated to be enrolled on the basis of the number of adult
hypertension patients who received care at the new health
centers (identified through queries of the EHR). For each of
the 6 possible random permutations of assignin g 3 health
centers to 3 arms, the sum of the existing sample size and
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anticipated sample size was calculated for each arm. The
standard deviation of this resulting sample size was calculated
across arms. Permutations were weighted by the inverse of
these standard deviations. A permutation was then selected
at random from the weighted set of 6 permutations. This
procedure is more likely to result in similar sample sizes after
the new round than a simple random unweighted sampling
from the 6 permuta tions.
Patient Enrollment
Following health center randomization, individual patients have
been recruited using 2 strategies. We generated lists of adult
patients with hypertension and ≥3 medications prescribed in
the EHR. Access Community Health Network staff have
managed all recruitment contact with patients through this
list. Primary care physicians have been sent emails within the
EHR and have the opportunity to indicate which potentially
eligible patients should not be contacted. Unless a physician
indicates a patient should not be contacted, patients receive
informational letters notifying them about the study. The letter
provides a number for patients to call to opt out of the study
immediately; the letter also informs patients that they will be
called about 10 days after the initial mailing to tell them more
about the study and assess their interest in participating.
Patient recruitment also occurs using institutional review
board–approved flyers and scripts with research assistants
directly approaching patients in each health center. Research
assistants explain the study and assess eligibility of interested
patients in private areas within the health centers. All patients
provide written informed consent before participation.
Eligibility criteria
Patients are eligible if they (1) are ≥18 years, (2) have ≥3
medications of any kind prescribed by their physicians, (3) have
mean blood pressure ≥130 mm Hg systolic or ≥80 mm Hg
diastolic if they have diabetes or ≥140 mm Hg systolic or
≥90 mm Hg diastolic if they do not have diabetes, (4) have a
Mini-Cog Exam
43
score ≥3, (5) are primarily responsible for
administering medication, and (6) do not intend to move or
change their usual source of medical care during the next year.
Patients who are non–English speaking are not eligible.
Interventions
The Electronic Health Record–Based Health Literacy Medi-
cation Therapy Management Intervention strategy leverages
the Epic EHR platform. EHR interventions are implemented at
the health center level and become part of routine work flow
for all adult patients. Earlier versions of these interventions
were field-tested previously among patients with varying
literacy skills.
44
After refinement, final versions of these
materials were reviewed and determined to be appropriate
by the community health center clinical documentation
committee.
Medication list review
As patients arrive, electronic check-in triggers the printing of
their current medication list, accompanied by plain-language
instructions for patients to (1) review medicines; (2 ) strike out
medicines they are not taking; (3) identify if they are taking
the remaining medicines as described by the listed instruction
(yes, no, only as needed); (4) identify any concerns for each
medicine they would like to discuss with their doctors (none,
need re fi ll, side effects, cost, other); and (5) add any medicine
(prescription, over-the-counter, vitamin or other supplement)
that they take regularly that is not included in their medical
record. A sample of this sheet is provided in Figure 3. These
procedures were field-tested among 150 patients at the
Northwestern Medical Faculty Foundation General Medicine
health center. Nearly all patients (91%) in the intervention arm
receive their medicine list at check-in, 85% of those receiving
lists review them with their physicians, and >90% have
discrepancies identified and removed, with an updated list
printed and given to patients at discharge. The 20 physicians
whose patients received this intervention gave unanimously
positive feedback on the value of this process.
Med sheets
We created single-page, plain-language medication informa-
tion sheets with content appropriately sequenced from a
patient’s per spective (drug name, indication, purpose/benefit,
how to take and for how long, when to call your doctor, when
to stop taking and call your doctor, important informati on) and
following other health literacy best practices. A sample sheet
is shown in Figure 4. These sheets are automatically
Figure 2. Health center and participant flow diagram. EHR indicates
electronic health record; MTM, medication therapy management.
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generated for 125 different common chronic disease medi-
cations when prescriptions are ordered or refi lled and are
distributed to patients at checkout along with an after-visit
summary. Lexile analyses on initial information sheets
confirmed that each met a <8th- grade readability standard.
The original content was initially developed by 2 pharmacists,
supported by an environmental scan of existing tools.
Patients, physicians, and health literacy experts reviewed
the material and guided revision. We also provide an
educational folder that details to patients what they should
do before, during, and after a medical encounter.
EHR tools plus nurse-led medication therapy
management
Health centers randomized to this study arm have the same
implementation of EHR tools above. In addition, patients who
enroll in the study receive a medication therapy man agement
intervention delivered by a nurse educator. Nurses receive
training from a physician (S.D.P.) regarding the objectives of
the intervention, the tasks to be accomplished in advance of
and during patient education sessions (listed in Table 1), and
methods of communication and collaboration with the rest of
the primary care team. Du ring the study, nurses performing
medication management consult with patients’ primary care
providers when patient-specific questions arise and with the
study principle investigator to review general approaches.
Before contacting the patient, the nurse educator reviews the
EHR medication list and physician notes to identify potential
medication errors (duplicates, internal discrepancies) and
considers safety monitoring and follow-up with concern for
potential contraindications (eg, renal dysfunction). The nurse
will communicate with the treating physician using EHR-based
email (or directly) to clarify questions and will document the
review in the medical record .
Figure 3. Example of a medication list review sheet.
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Medication counseling sessions can occur in person at a
health center or by phone. The initial session includes assess-
ment of medication comprehension and counseling based on
patient understanding, review of the patient’s medication usage
and medication reconciliation with the list in the medical record,
assistance with regimen dosing consolidation when feasible,
and development of a medication table for complex regimens.
Nurses assess adherence and identify patterns of improper use
and reasons for nonadherence when present. Education about
medication uses teach-back to confirm understanding. Nurses
also assess patients’ knowledge of their chronic conditions,
address misconceptions, and reinforce the role that proper
medication use plays in disease control.
During the intervention year, the nurse attempts to conduct
brief medication education and review with patients around the
time of scheduled office visits. When new chronic disease
medications are prescribed, the nurse telephones patients 4 to
7 days after to determine whether the patient obtained the new
prescription(s), to assess use and comprehension, and to
identify any problems. A nurse proactively telephones patients
who have not returned within 3 months if the patient has an
uncontrolled chronic condition (hypertension, diabetes, or
other chronic condition [eg, heart failure, asthma]) or within
6 months if chronic conditions are contr olled. Nurse-i dentified
medication problems are conveyed to the patient’s primary
physician (using EHR email if not urgent and telephone or page if
Figure 4. Example of a medication information sheet.
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urgent). Other nurse-initiated actions include prescription
refills, directing patients who need renewal of state medical
assistance to appropri ate staff members, and facilitating
appropriate return visits or referrals.
Usual care
Enrolled patients from health centers assigned to usual care
will receive study assessments from research assistants at
the health centers, but there are changes neither to site EHR
functionality nor to health center work flow.
Baseline Measurements
Baseline measures include sociodemographic characteristics,
primary language used at home, chronic health conditions,
current prescription medication usage, the Patient Activation
Measures (13 items).
45
Baseline measurements also include
the Newest Vital Sign, which is a reliable screening tool to
determine risk for limited health literacy.
46
It has a high
sensitivity for predicting limited literacy skills and is strongly
correlated with the short form of the Test of Func tional Health
Literacy in Adults (r=0.61).
46
Outcome Assessments
Trained research assistants conduct outcome assessments 3,
6, and 12 months followi ng enrollment.
Primary Outcome
The primary study outcome is systolic blood pressure.
Analysis will be done according to the intention-to-treat
principle. Researc h assistants will perform standardized
measurements of blood pressures and pulse at baseline and
after 3, 6, and 12 months using an automated device (Omron
HEM-907XL). Patients are seated quietly with their feet and
back supported for 5 minutes before blood pressure is
obtained. Three recordings are to be performed at each visit,
and the mean of the second and third readings is used to
indicate the blood pressure for that visit. Patient positioning,
arm selection, cuff size selection and other techniques follow
the procedures for blood pressure measurement of the
National Health Examination and Nutrition Survey.
47
For the
subgroup with diabetes, measures of disease control for
hemoglobin A1c, and low-density lipoprotein cholesterol are
obtained from patients’ electronic health records. These
Table 1. Nurse-Led Medication Management Tasks
Initial chart review (prior
to patient contact)
Identify potential reconciliation errors (duplicates within drug class, discrepancies between EHR medication list
and clinical notes)
Identify gaps in laboratory monitoring (eg, failure to obtain follow-up labs as instructed by provider, failure to
monitor renal and electrolyte function when indicated in prior year, failure to monitor diabetes or lipids when
indicated)
Check for potential contraindications due to renal dysfunction or drug – drug interactions
Medication counseling
session (general)
Assess medication comprehension and tailor counseling based on patient understanding
Use teach-back
Review patient’s medication usage and perform medication reconciliation with EHR medication list
Assist with regimen dosing consolidation when feasible
Help patients maintain their personal medication record
Assess adherence and identify patterns of improper use
When nonadherence is identified, assess reason(s)
Medication counseling
session (following
medication change)
Determine whether patient obtained new prescription(s), assess use and comprehension, and identify problems
or adverse effects
Educate about new prescriptions (use medication information sheets)
Assess patient understanding of changes using teach-back
Assist with medication-related problem solving (with input from patient’s physician when needed)
Recontact patient Four to 7 days following a new office visit at which medication regimen is changed
Within 3 months of prior contact when uncontrolled chronic condition (eg, hypertension, diabetes, asthma) is present
Within 6 months when controlled chronic condition(s) are present
As clinically indicated or as requested by the patient’s physician
When contacted by patient
EHR indicates electronic health record.
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disease control measures can be scored even when repeat
laboratory measurement is not obtained. For diabetes control,
the quality measures examined are: (1) an HbA1c test (the
most recent measurement recorded within the EHR in the
preceding 365 days was >9.0%, poor diabetes control; (2) an
alternative measure using the threshold of <8.0% (HbA1c
<8.0); and (3) low-density lipoprotein-cholesterol obtained in
the preceding 365 days and <100 mg /dL.
48
Secondary Outcomes
Medication reconciliation
Patients are asked to bring their prescription medications with
them to the 3-, 6-, and 12-month study visits. Patients are
asked to report on the different prescription medications they
are taking and can consult their pill bottles or other aids that
they use to keep track of medications. Patients can identify
additional medications prescribed that they may not have
currently in their possession. We will compare patient-
reported medication re gimens with the medical record
medication list on the s ame date.
We will classify patients into 3 primary categories: (1)
reconciled—patients name the same medications as recorded in
the medical record; (2) reconciliation discrepancies—patients
could name ≥1 more medication, but the list was not in full
agreement with the medical record; and (3) unable to name any
medications—patients provided no recognizable names of
prescription medications listed in their medical record. We have
already used this classification in 2 recent studies.
16,17
We will
apply binary classifications (reconciled versus not reconciled) to
5 groups of medications for each patient: (1) all not-as-needed
prescription medications, (2) antihypertensive medications, (3)
diabetes medications, (4) lipid-lowering medications, and (5) all
prescription medications including as needed.
Medication understanding
Understanding of medication instructions and dosing will be
assessed through a structured questionnaire. Patients will
demonstrate how they take each medication by dose and
frequency. We will apply binary classifi cations (full under-
standing versus not) to the same 5 groups of medications for
each patient. The study team has extensively used these
methods in prior studies to evaluate patients’ proper under-
standing and demonstrated use of multidrug regimens.
49,50
Patients’ knowledge of medication indications will be
assessed by a physician, nurse, or pharmacist who is blinded
to the patient’s study assignment.
18
Medication adherence
Adherence is measured for each prescription medication
using (1) a 4-day assessment of pills taken/pills prescribed
based on patient self-report, (2) a 24-item Patient Medication
Adherence Questionnaire
51
that will assess individual barriers
(eg, cost, adverse effects, salience, stigma, beliefs), and (3)
in-person pill count. The questionnaire developed by Morisky
is also used as a general measure of adherence.
52
Pill count
will be used for antihypertensive, diabetes, and lipid-lowering
medications. Adherence will be treated both continuously
(pills taken/pill prescribed) and dichotomously (yes/no—
≥80% of expected pills absent from pill bottle). As no single
measure may adequately capture a patient’s behavior related
to taking medicine, we use a diverse set of assessments and
will also be able to derive a general factor of adherence using
maximum likelihood estimation.
Health-related quality of life (SF-12)
We will measure whether the interventions influence health-
related quality of life using the SF-12.
53
Power and Sample Size
The sample size for this study was based on the primary
outcome of systolic blood pressure at 12 months. Table 2
shows participants per health center needed to detect a
4-mm Hg difference in SBP for pair-wise comparisons of
intervention groups to usual care. Required sample sizes are
reported for a range of standard deviations, 80% power, 5%
type I error, and intra–health center correlation of 0.001. We
used this intr a–health center correlation based on the very
low correlations observed among patients participating in
another multiclinic care management study (M. Wolf, personal
communication). We will attempt to recruit 140 participants at
each health center in order to have ≥105 complete the
measurement at 1 year (75% retention). A 4–mm Hg differ-
ence in systolic blood pressure was chosen because this
difference has been show n to produce a significant 15%
reduction in major cardiovascular events among patients
randomized to different blood pressure treatment goals.
54
Statistical Analysis
This trial uses a cluster-randomized design in which health
center is the unit of randomization. We will randomize health
centers in groups of 3 to the 3 arms (usual care, EHR tools
only, and EHR tools plus nurse management) and anticipate
randomizing 12 health centers.
We will exami ne for potential confounding factors across
the 3 treatment arms. Variables found to have significant
differences (P<0.05) across treatment groups will be entered
as covariates in the generalized linear mixed models used for
formal analyses. We will control for baseline SBP in formal
analyses.
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SBP at 12 months is the primary outcome. Binary and
continuous variables measure secondary outcomes. We will
use generalized linear mix ed models for analyses of the data.
The 3-category treatment group variable will be the indepen-
dent variable of primary interest and will be modeled as a
fixed effect with the usual care group specified as the
reference group. We will also include fixed effects for any
potential confounding covariates noted in the descriptive
studies. Random effects will be included for each health
center to account for intra–health center correlation among
participants.
The statistical tests of primary interest will pertain to the
fixed main effects comparing EHR tools with usual care and
EHR tools plus nurse management with usual care. The beta
estimates from the generalized linear mixed models will be
deemed statistically significant for P<0.05. Each of these 2
primary comparisons is a specific a priori hypothesis that we
are testing at a=0.05. We will examine outcomes separately
for subgroups defined by participants’ literacy defined as
inadequate (Newest Vital Signs score indicating inadequate
health literacy versus all others). We will test for differences in
intervention effects according to literacy by including a
literacy-intervention interaction term. Statistical significance
for the interaction term (P<0.05) will indicate that intervention
group differences in SBP at 12 months, as well as the
secondary outcomes, vary by literacy level. We do recognize,
however, that we are not formally powered to detect such an
interaction, and so the analyses will be considered explor-
atory. Secondarily, we will compare the fixed main effects
comparing EHR tools with EHR tools plus nurse management.
Further analyses will compare systolic blood pressure
among the study groups using data collected at all postbas-
eline times and using a patient-level repeated-measures linear
model that accounts for clustering by health center. The
group-by-time interaction effect in this model will explore
whether and how intervention effects vary over time. In
particular, we will explore the hypothesis that the nurse-led
intervention will produce more rapid effects on blood pressure
and other outcomes than the EHR-tools-only intervention and
also examine whether intervention effects appear to wane
over time or are sustained.
We will examine loss to follow-up by intervention group. For
the primary outcome, missing data will occur in cases in wich
patients complete ≥1 assessments but not the 12-month
assessment. For this type of missing data, bias assessment
will be done using either an empirical approach or a model-
based approach. The empirical approach will classify patients
as having or not having missing follow-up data and then
compare baseline outcome and mediating variables between
these 2 groups. The model-based approach will try to
determine whether the missing data are missing completely
at random, missing at random, or are nonignorably missing. To
test whether missing data are missing completely at random,
we will use a logistic regression analysis adapted by Ridout.
55
The probability of no response (ie, data missing) is modeled as
a function of previously observed characteristics, and if no
association is found between this binary indicator of no
response and function s of previously observed characteristics,
then the assumption that the data are missing completely at
random may be reasonable. In this case, the mixed model
using all available data will be used for the intention-to-treat
analysis. Further assessment of mis sing data mechanisms
may be done using shared parameter models in which serial
outcome measurements and missing data indicator variables
are jointly modeled under assumptions of different missing
data mechanisms.
56
The shared parameter model approach to
analyze blood pressure over time will be used for the intention-
to-treat analysis if the Rideout test indicates data are not
missing completely at random.
Outcomes for medication reconciliation, medication under-
standing, and medication adherence will be compared
between each of the 3 study groups. We will use generalized
linear mixed models for analyses of the data in a fashion
similar to the primary analysis using the identity link for
continuous data and the logit link for binary data.
Intervention Fidelity
We will use direct observation performed during random
clinical sessions at study sites that receive the EHR-based
interventions to determine the fidelity with which medication
lists were provided to patients at the beginning of their
encounter and medication information print material was
provided at the end of visits. Nurses will log all in-person and
telephone encounters with patients.
Cost Analysis
We will calculate the direct and indirect costs of the 2
components of the intervention (implementation of EHR
Table 2. Participants Required per Clinic to Detect a
4–mm Hg Difference in Systolic Blood Pressure*
Standard Deviation of Systolic
Blood Pressure
Number of Participants per Health
Center Required
14 75
16 100
18 130
*The numbers shown are the number of participants with systolic blood pressure
measured at 12 months for each of 12 health centers needed to detect a 4–mm Hg
difference in SBP for pair-wise comparisons of intervention groups with usual care.
Required sample sizes are reported for the given range of standard deviations, 80%
power, 5% type I error, and intra–health center correlation of 0.001.
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tools and nurse-led medication therapy management) to
inform potential adopters. The primary or direct costs of
the nursing intervention will include the costs of the nurse
time and estimates of additional office resources consumed
(indirect cost). The costs of the EHR tools will include
annual maintenance costs for the system but will not
include development costs for software that will be
available at no additional cost. If these programs lead to
reduced blood pressure, we will conduc t a cost-effective-
ness analysis to compare the 2 components as a ratio of
cost per mean mm Hg systolic blood pressure lowering.
57
Alternatively put, the primary outcome measured, change in
mean systolic blood pressure, will be compared with the
measured intervention costs to obtain a cost per unit
change in systolic blood pressure. We will then test the
sensitivity of costs to different assumptions about indirect
costs and the potential use of less costly staff being
substituted for nurse time, assuming less costly staff could
have the same impact.
Conclusion
The Northwestern and Access Community Health Network
Medication Education Study will test the effects of a higher
intensity intervention based on nurse-led medication therapy
management in combination with EHR-based medication
management tools and a lower-intensity intervention that
uses EHR-based tools only among patients with uncontrolled
hypertension and complex drug regimens. Both interventions
will be delivered within community health centers and help to
inform community practices about methods that may be
helpful to assisting patients in their management of complex
drug regimens.
Acknowledgments
The authors thank Ingrid Guzman, Kendra Julion, Berenice Hernandez,
Darren Kaiser, MS, Josyln Emerson, PharmD, Anand Reddy, Jennifer
Webb, MA, Stacy Bailey, PhD, Daneen Woodard, MD, Jairo Mejia, MD,
Larry Manheim, PhD, and Julie Bonello, RN, for their contributions to
this study. The authors also acknowledge and thank staff members
of participating health centers.
Source of Funding
This work was supported by Award Number R01NR012745
from the National Institute of Nursing Research of the
National Institutes of Health. The content is solely the
responsibility of the authors and does not necessarily
represent the official views of the National Institute of
Nursing Research or the National Institutes of Health.
Disclosures
Dr. Persell has performe d paid consulting for the American
Board of Internal Medicine, the American Medical Association,
and Vimedicus (a health information technology company). Dr.
Wolf has received funds personally from Merck Pharmaceu-
ticals and McNeil Consumer Healthcare for service on
advisory boards and receives funding through Northwestern
University for research grants and contracts from Abbott
Labs, Merck Pharmaceuticals, McNeil Consumer Healthcare,
and UnitedHealthcare. No other authors have any relevant
interests to disclose.
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