Margaret R. Stedman, PhD

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

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

 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.

 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.

 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.

 Patients commonly do not adhere to chronic medication therapy, leading to substantial morbidity, mortality, and excess healthcare costs.

 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.

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.

 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.

 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.

 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.

) 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 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).

 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 (

). 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.

 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.

 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.

 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.

 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

 One study fit the criteria for a reminder system with provider feedback. Rosen et al used interactive MEMS as part of a cue dose training regimen.17 A total of 33 type 2 diabetes patients receiving metformin were recruited from a primary care clinic at the Veterans Administration Connecticut Healthcare System. The intervention included interactive MEMS with audiovisual reminders to take medication. Data from the interactive MEMS were collected every 4 weeks and shared with healthcare providers. Study coordinators contacted providers and urged them to discuss the MEMS findings with their patients. Control patients were monitored with noninteractive MEMS. The intervention lasted 16 weeks with a 12-week follow-up period. Adherence was defined as a percentage of prescribed doses taken. At baseline, adherence was approximately 60% in both groups. At 16 weeks, adherence in the intervention arm was 80% versus 60% in the control (P = .017). The ES was small (ES = 0.43; 95% CI, -0.27-1.14). Adherence was noted to fall after the intervention period concluded. Investigators also surveyed patients and doctors about the experience of discussing MEMS data. Both parties generally found the discussions to be moderately helpful and comfortable.

 Two-Way Interactive Systems Without Real-Time Provider Feedback

 We found 3 articles describing interactive systems without provider feedback. All 3 systems sought to educate/counsel patients and influence adherence behavior either via realtime interaction or customized feedback reports. Of the 3 studies, 2 used self-reported adherence measures. The ESs for all 3 studies were very small.

 Stacy et al recruited 497 of 1219 commercially insured subjects from a large health plan who had been newly prescribed a statin.

 Case and control patients received at least 1 interactive voice recognition (IVR) call providing behavioral advice. Patients randomized to the intervention received tailored behavioral support and print material and 2 additional tailored IVR calls. Tailored print material provided personalized advice based on patients’ responses during the IVR call. More than 25% of patients participated in all 3 calls; 42% of patients participated in 2 of 3 calls. Patients in the control arm received 1 nontailored IVR call and generic print materials. The adherence end point was calculated as percentage of pills taken by using insurance claims data, with an 80% adherence threshold. At 6 months, 47.0% of the intervention and 38.9% of the control patients, respectively, were adherent to at least 80% of their medications. The ES was very small (ES = 0.08; 95% CI, 0.01-0.17).

 Johnson et al studied the impact of an individualized transtheoretical model expert system intervention on 404 adults with hyperlipidemia prescribed a cholesterol-lowering medication.

 The transtheoretical model explains and predicts how and when individuals change behaviors with stages classified as precontemplation, contemplation,  preparation, action, and maintenance. The expert system was built on normative databases to mimic reasoning and problem solving of human experts, and generated individualized reports for patients relevant to their current stage of change. Adherence in both the intervention and control arms was assessed by the system at 6-month intervals over 18 months on a 5-point Likert scale. The odds ratios of improved adherence in the intervention group compared with the control group were 2.03 (P >.05) at 6 months, 3.67 (P <.05) at 12 months, and 2.86 (P <. 05) at 18 months. The ES at the conclusion of the study was very small (ES = 0.18; 95% CI, -0.08-0.45).

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

Margaret R. Stedman, PhD

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