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Supplements Special Issue: Health Information Technology — Guest Editors: Sachin H. Jain, MD, MBA; and David B
The Road to Electronic Health Records Is Paved With Operations
Amir Dan Rubin, MBA, MHSA; and Virginia A. McFerran, MA
Finding Cancer at Home
Katlyn L. Nemani, BA
Alternative Measures of Electronic Health Record Adoption Among Hospitals
Fredric E. Blavin, MS; Melinda J. Beeuwkes Buntin, PhD; and Charles P. Friedman, PhD
Physician ePortfolio: The Missing Piece for Linking Performance With Improvement
Nancy L. Davis, PhD; Lloyd Myers, RPh; and Zachary E. Myers
Using Electronic Prescribing Transaction Data to Estimate Electronic Health Record Adoption
Emily Ruth Maxson, BS; Melinda J. Beeuwkes Buntin, PhD; and Farzad Mostashari, MD, ScM
Understanding Meaningful Outcomes
Daniel C. Armijo, MHSA; Eric J. Lammers, MPP; and Dean G. Smith, PhD
Electronic Health Record Feedback to Improve Antibiotic Prescribing for Acute Respiratory Infections
Jeffrey A. Linder, MD, MPH; Jeffrey L. Schnipper, MD, MPH; Ruslana Tsurikova, Msc, MA; D. Tony Yu, MD, MPH; Lynn A. Volk, MHS; Andrea J. Melnikas, MPH; Matvey B. Palchuk, MD, MS; Maya Olsha-Yehiav, MS
Review of Veterans Health Administration Telemedicine Interventions
Robert D. Hill, PhD; Marilyn K. Luptak, PhD, MSW; Randall W. Rupper, MD, MPH; Byron Bair, MD; Cherie Peterson, RN, MS; Nancy Dailey, MSN, RN-BC; and Bret L. Hicken, PhD, MSPH
Achieving Meaningful Use: A Health System Perspective
Cynthia L. Bero, MPH; and Thomas H. Lee, MD
Health Information Technology Is Leading Multisector Health System Transformation
Sachin H. Jain, MD, MBA; and David Blumenthal, MD, MPP
Health Information Technology and Health System Redesign-The Quality Chasm Revisited
Reed V. Tuckson, MD; Deneen Vojta, MD; and Andrew M. Slavitt, MBA
Health Information Technology and the Medical School Curriculum
Marc M. Triola, MD; Erica Friedman, MD; Christopher Cimino, MD; Enid M. Geyer, MLS, MBA; Jo Wiederhorn, MSW; and Crystal Mainiero
Congressional Intent for the HITECH Act
Pete Stark
Optimizing Health Information Technology's Role in Enabling Comparative Effectiveness Research
Amol S. Navathe, MD, PhD; and Patrick H. Conway, MD, MSc
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Healthcare Information Technology Interventions to Improve Cardiovascular and Diabetes Medication Adherence
Alexander S. Misono, BA; Sarah L. Cutrona, MD, MPH; Niteesh K. Choudhry, MD, PhD; Michael A. Fischer, MD, MS; Margaret R. Stedman, PhD; Joshua N. Liberman, PhD; Troyen A. Brennan, MD, JD; Sachin H. Ja
Electronic Health Record Adoption and Quality Improvement in US Hospitals
Spencer S. Jones, PhD; John L. Adams, PhD; Eric C. Schneider, MD; Jeanne S. Ringel, PhD; and Elizabeth A. McGlynn, PhD
A Health Plan Prescription for Health Information Technology
Newt Gingrich, PhD, MA; and Malik Hasan, MD
HITECH Lays the Foundation for More Ambitious Outcomes-Based Reimbursement
John Glaser, PhD
Increasing Consumerism in Healthcare Through Intelligent Information Technology
Seth B. Cohen, MBA, MPA; Kurt D. Grote, MD; Wayne E. Pietraszek, MBA; and Francois Laflamme, MBA
Electronic Health Records: Potential to Transform Medical Education
Bryant A. Adibe, BS; and Sachin H. Jain, MD, MBA
Smart Health Community: The Hidden Value of Health Information Exchange
James N. Ciriello, MS; and Nalin Kulatilaka, PhD, MS
Effects of Documentation-Based Decision Support on Chronic Disease Management
Jeffrey L. Schnipper, MD, MPH; Jeffrey A. Linder, MD, MPH; Matvey B. Palchuk, MD, MS; D. Tony Yu, MD; Kerry E. McColgan, BA; Lynn A. Volk, MHS; Ruslana Tsurikova, MA; Andrea J. Melnikas, BA; Jonathan

Healthcare Information Technology Interventions to Improve Cardiovascular and Diabetes Medication Adherence

Alexander S. Misono, BA; Sarah L. Cutrona, MD, MPH; Niteesh K. Choudhry, MD, PhD; Michael A. Fischer, MD, MS; Margaret R. Stedman, PhD; Joshua N. Liberman, PhD; Troyen A. Brennan, MD, JD; Sachin H. Ja

Despite increasing availability of healthcare information technology, a literature review showed few clinical data on medication adherence interventions using this technology.

Objective: To determine the efficacy of healthcare information technology (HIT) interventions in improving adherence.


Study Design: Systematic search of randomized controlled trials of HIT interventions to improve medication adherence in cardiovascular disease or diabetes.


Methods: Interventions were classified as 1-way patient reminder systems, 2-way interactive systems, and systems to enhance patient–provider interaction. Studies were subclassified into those with and without real-time provider feedback. Cohen’s d effect sizes were calculated to assess each intervention’s magnitude of effectiveness.


Results: We identified 7190 articles, only 13 of which met inclusion criteria. The majority of included studies (54%, 7 studies) showed a very small ES. The effect size was small in 15%, large in 8%, and was not amenable to calculation in the remainder. Reminder systems were consistently effective, showing the largest effect sizes in this review. Education/counseling HIT systems were less successful, as was the addition of realtime adherence feedback to healthcare providers. Interactive systems were rudimentary and not integrated into electronic health records; they exhibited very small effect sizes. Studies aiming to improve patient–provider communication also had very small effect sizes.


Conclusions: There is a paucity of data about HIT’s efficacy in improving adherence to medications for cardiovascular disease and diabetes, although simple patient reminder systems appear effective. Future studies should focus on more sophisticated interactive interventions that expand the functionality and capabilities of HIT and better engage patients in care.


(Am J Manag Care. 2010;16(12 Spec No.):SP82-SP92)

We performed a systematic review to assess the efficacy of healthcare information technology (HIT) interventions for improving patients' medication adherence.


  • There is a striking paucity of clinical data despite increasing availability of HIT.


  • Existing HIT adherence interventions are promising, with simple patient reminder systems providing the best evidence for use.


  • The tested interactive systems (eg, education and counseling via interactive computer interface) were rudimentary and showed limited benefit.
Nonadherence to essential medications is an important public health problem.1,2 Patients commonly do not adhere to chronic medication therapy, leading to substantial morbidity, mortality, and excess healthcare costs.3-6 Despite the significant impact of nonadherence on health, solutions are elusive. Meta-analyses evaluating interventions to improve adherence have consistently supported the use of resource-intensive, multifactorial approaches with multiple elements delivered over time, such as self-management plans, reinforcement, or rewards.7,8 In an effort to identify more cost-effective solutions, researchers and clinicians have begun exploring the role of healthcare information technology (HIT) in medication adherence interventions.

There are numerous potential applications for HIT in a medication adherence intervention. Electronic pharmacy data may allow for identification of nonadherence and facilitate data delivery to prescribers and pharmacists.9 Electronic systems might inexpensively remind patients and providers about refills. Interactive electronic systems may be used to educate  patients about appropriate medication use, and widespread online connectivity should accommodate more sophisticated monitoring, interaction, and communication.

Although rigorous evaluation of the effectiveness of HIT adherence interventions is essential, little systematic assessment has been done to date. We conducted a systematic review of HIT interventions designed to improve medication adherence in cardiovascular disease and diabetes. Our goal was to assess the state of evidence in this field, identify interventions that were successful, and ascertain specific features of interventions that seem to be most associated with success.


We performed a systematic search of peer-reviewed journals between 1966 and 2010 using MEDLINE and EMBASE. We limited our search to randomized controlled trials.

Our search terms related to the type of study (ie, randomized controlled trial); adherence (ie, adherence OR compliance OR medication adherence OR treatment adherence); prescription drugs (ie, drug OR medication OR antihypertensive OR antihyperlipidemic OR hypoglycemic); and cardiovascular disease and diabetes (ie, myocardial infarction, coronary heart disease, heart failure, hypertension, hyperlipidemia, OR diabetes). Articles with at least 1 search term in 3 of the main categories (study type AND adherence AND either drug OR disease) met criteria for review.

Search terms and parameters were adjusted for both databases (MEDLINE and EMBASE) while maintaining a common overall architecture. Search results were then screened for duplicate entries, which were removed.

Study Selection

Studies were included if they reported results of randomized controlled trials studying interventions to improve adherence to medications used for prevention or treatment of diabetes or cardiovascular disease, the greatest source of mortality in the United States.10 We included only randomized trials in order to promote interventions based on the  highest quality of evidence. Studies were limited to adult subjects (aged ≥18 years). Of these interventions, we included only interventions with any electronic component. Examples included the identification of patients with electronic tracking of adherence, electronic reminders to take medication, or electronically enhanced communication with patients or providers. Non-English studies were excluded.

Study Classification

After exclusions, 13 articles (Figure) were classified into 2 groups. The first group described the type of interaction with the patient: 1-way patient reminder systems, 2-way interactive systems, or systems to enhance patient–provider interaction. We selected these categories to assess how to best deliver interventions to patients, whether by simple patient reminders, engagement of patients with an electronic system, or enhancement of communication with the provider. Studies of reminder systems featured interventions providing audio and/ or visual reminders of medication dosing. Interactive systems included computer-based tools aimed at patient education, counseling, and/or promoting favorable patient behaviors. Systems interacted with patients either immediately or via delayed feedback (eg, customized reading material).

The second group described the type of physician engagement. These types of interventions included those in which no real-time adherence information was passed on to providers and those that incorporated real-time feedback to providers. We included this categorization in an attempt to assess the incremental value of delivering additional feedback to the provider. In a third type of intervention, providers (or in 1 case, research assistants) directly interacted with patients as part of the intervention.

Data Extraction

Data were extracted by 3 investigators (ASM, SLC, WHS), with disagreements resolved by consensus. We assessed a number of variables related to the organization and outcome of studies including study design, setting, characteristics of population studied, number of participants, mean age (or age range) of participants, characteristics of the intervention, methods used to measure medication adherence, and medication adherence outcomes. Confidence intervals (CIs) were reported where they were available and P values where no CIs were available.

We identified those randomized controlled trials where means and standard deviations for medication adherence outcomes were presented. For these studies, we computed Cohen’s d effect size (ES) statistics, which can be calculated for outcomes that are either binary (eg, survey responses or predefined adherence cutoffs) or continuous (eg, proportion of days covered).11,12 The ESs compare the difference in effect between the study groups divided by the standard deviation of this difference. When standard deviations  were not reported, we derived them from the P value or t test statistic.

Using standard methods, we considered an ES of less than 0.2 to be very small, an ES of 0.2 to less than 0.5 to be small, an ES of 0.5 to less than 0.8 to be medium, and an ES of 0.8 or greater to be large. We assumed that the estimated Cohen’s d statistics were independent of scale, sample size, and the standard deviation of the outcome studied.


Thirteen studies met criteria for our literature review (Table). Five studies used 1-way patient reminder systems, of which only 1 incorporated provider feedback. Six studies examined 2-way interactive systems, of which 3 included provider feedback. The remaining 2 studies were designed to test systems to enhance patient–provider interaction. Five studies used patient’s self-reported adherence as an end point, whereas 8 studies used pill count, pill cap monitoring, or some other quantitative measure to determine adherence. A total of 11 studies included patients with cardiovascular disease, and 2 studies were conducted with diabetic patients. Seven studies had a very small ES, 2 studies had a small ES, no studies had a medium ES, and 1 study had a large ES. There were 3 studies where the ES could not be determined from the published data.

One-Way Patient Reminder Systems Without Real-Time Provider Feedback

Four studies featuring reminder systems without provider feedback were identifi ed in our search. Studies varied in length from 12 weeks to 1 year. Only 1 study used a self-reported adherence measure. The ES could be calculated for 2 of the 4 studies, resulting in a very small effect and a large effect.

Christensen et al studied the impact of an electronic reminder device on adherence in hypertensive patients taking telmisartan.13 Patients with “untreated or ineffectively treated hypertension” were recruited from private practice or hospital ambulatory centers in Poland; 1577 patients were given the monitoring device, and 784 patients began using it. A total of 135 patients did not return the device, and 251 patients were excluded from analysis because they did not answer survey questions appropriately or investigators had “doubts about the authenticity of electronic monitoring data.” The intervention was an electronic blister card device, which recorded dispensing and provided audiovisual medication  reminders. The study was a crossover design with 6 months per arm. At 6 months, before crossover, patients’ self-reported compliance was 90.6% in the intervention group and 85.1% in the control group (P = .072). The ES for this intervention was very small (ES = 0.06; 95% CI, 0.01-0.12).

McKenney et al followed 70 adult patients on antihypertensive medication in an ambulatory patient population from a retirement community or a primary care center.14 The intervention was a pill cap displaying the last time and day when the container was opened, as well as an audiovisual alarm reminder. The alarm sounded when a dose was due, and the digital face fl ashed if the alarm was ignored. The study was conducted in two 12-week phases. In each phase, the electronic cap was compared with standard vials. After  phase 1, the intervention arm had adherence of 95.1% versus 78% in the control arm (P = .0002). After phase 2, the electronic cap arm had adherence of 94.6% versus 79% in the control arm (P = .003). The ES for the cap intervention was large (ES = 0.85; 95% CI, 0.14-1.56).

Mengden et al studied 44 patients with uncontrolled hypertension from a hospital outpatient department in Germany.15 After a 4-week run-in period to identify patients with uncontrolled hypertension, patients had 1 antihypertensive substituted by candesartan/hydrochlorothiazide (16/12.5 mg). Adherence was tracked using a Medication Event Monitoring System (MEMS) cap, which was placed on each participant’s medication bottles. Patients were randomized to receive hypertension teaching and an interactive MEMS with visual reminders or usual care with noninteractive MEMS for monitoring. Adherence was calculated from MEMS data as the percentage of prescribed doses taken. Although adherence in patients with uncontrolled hypertension dropped signifi cantly during the run-in (P <.001), it rebounded to excellent levels after drug substitution. At 12 weeks, patient adherence was remarkably high in all study arms: the intervention arm had adherence of 99.7% compared with 97.7% in the control arm and 97.8% in patients whose hypertension was considered to be controlled after the run-in period (no statistically significant differences). The ES could not be calculated. However, the high levels of adherence in control patients in this larger study are not representative of most patients.

Santschi et al compared 2 different electronic reminder systems in 25 patients with hypertension taking irbesartan being followed at 1 of 2 outpatient clinics in Switzerland.16 This crossover study compared the MEMS system with the Intelligent Drug Administration System, a blister pack–based medication tracking and reminder system. Each crossover period lasted 2 months. Adherence was calculated as the percentage of doses taken. At the end of the study, median adherence was 100% for each device, with no statistically significant difference. The ES could not be calculated.

One-Way Patient Reminder Systems With Real-Time Provider Feedback

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