Sarah L. Cutrona, MD, MPH

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).

Articles

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

Healthcare Information Technology Interventions to Improve Cardiovascular and Diabetes Medication Adherence

To determine the optimal modes of delivery for interventions to improve adherence to cardiovascular medications.

We conducted systematic searches of English-language, peer-reviewed publications in MEDLINE and EMBASE, 1966 through December 31, 2008. We selected randomized controlled trials of interventions to improve adherence to medications for preventing or treating cardiovascular disease or diabetes. Articles were classified based on mode of delivery of the main intervention as (1) person-independent interventions (mailed, faxed, or hand distributed; or delivered via electronic interface) or (2) person-dependent interventions (nonautomated phone calls, in-person interventions).

We identified 6550 articles. Of these, 168 were reviewed in full and 51 met inclusion criteria. Among person-independent interventions (56% successful), electronic interventions were most successful (67%). Among person-dependent interventions (52% successful), phone calls showed low success rates (38%). In-person interventions at hospital discharge were more effective (67%) than clinic interventions (47%). In-person pharmacist interventions were effective when held in a pharmacy (83% successful), but were less effective in clinics (38%).

 Future medication adherence studies should explore new electronic approaches and in-person interventions at the site of medication distribution. Identifying times of increased patient receptivity to the adherence message such as hospital discharge also will be important.

Interventions designed to improve adherence to cardiovascular medications may require a person to deliver them (in-person counseling, phone calls), or the delivery may be person independent (mailed information, electronic interface). The mode of delivery directly affects cost and long-term viability.

Among person-independent interventions, we found electronic approaches to be most effective at improving adherence. 

For interventions delivered by a person, we found high success rates for in-person interventions delivered at a pharmacy or at the time of hospital discharge.

Nonadherence to essential chronic medication therapy for cardiovascular disease and diabetes is common, leading to substantial morbidity, mortality, and healthcare costs.

Evidence- based efforts to improve adherence are needed. Previous studies have demonstrated the effectiveness of reduced dosing demands and complex, multifactorial interventions,

 but there is little evidence available comparing modes of delivery for adherence interventions.

 Evidence-based information on modes of delivery would enhance the construction of adherence interventions in several domains: the communication channel (eg, written, electronic, phone, in person), the purveyor of information (eg, lay person, pharmacist, physician), and the optimal setting (hospital, home, pharmacy, clinic). The mode of delivery is  closely linked to an intervention’s cost and therefore to its long-term viability. Careful consideration of the comparative efficacy and intensity of different approaches is needed to develop evidence-based strategies.

 We conducted a systematic review of interventions that sought to improve adherence to medications for cardiovascular disease and diabetes, a cardiovascular disease equivalent. We focused on the mechanism of information transfer to patients. By evaluating the effects of (1) the purveyor of information, (2) the channel of information, and (3) the setting of  transfer, we offer to payers, providers, and policy makers additional guidance on the development of adherence interventions.

 We performed a systematic search of articles published in peer-reviewed, healthcare-related journals between 1966 and December 31, 2008. The search was performed using MEDLINE and EMBASE, with the help of a professional librarian. We limited our search to randomized controlled trials.

 We used search terms related to the type of study (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 agents”), and cardiovascular disease and diabetes (myocardial infarction, coronary heart disease, heart failure; hypertension; hyperlipidemia; and 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 the title/abstract review (see the 

 Search terms and parameters were adjusted for both databases while maintaining a common overall architecture. Search results from MEDLINE and EMBASE were combined and screened for duplicate entries.

 Studies were included if they reported the results of randomized controlled trials that examined interventions to improve adherence to medications used for the prevention or treatment of cardiovascular disease or diabetes. Studies were limited to adult subjects (aged ≥18 years) in either the outpatient setting or at the inpatient/ outpatient transition. Data were gathered on outpatient adherence for all patients. Studies were excluded if they described an intervention characterized by regimen simplification (either unit-of-use packaging or changes in dose frequency or formulation), as they could not be placed into the study categorization, and previous studies have demonstrated their effectiveness.

 Studies were excluded if they were written in a language other than English. Those with a duration of less than 24 weeks were excluded because cardiovascular medications typically require long-term adherence.

) were classified by the mode of delivery of the intervention. Person-independent interventions included (1) mailed, faxed, or hand-distributed interventions and (2) interventions delivered via electronic system. Person-dependent interventions included (1) interventions delivered via nonautomated phone calls and (2) in-person interventions (classified based on site of delivery, ie, home, work site, pharmacy, clinic, or hospital). Persondependent interventions were further classified based on the level of training required of the person administering the intervention: (1) trained lay person, (2) nurse, (3) pharmacist, (4) physician, or (5) not specified.

 For those studies that incorporated 2 or more modes of intervention, we assigned categories based on what appeared to be the main mode of intervention. Where categories appeared to be equivalent, we assigned priority in the following order: person dependent (in person, followed by phone) and person independent (electronic system, followed by mailed, faxed, or hand distributed).

 Data were extracted by 2 investigators (SLC, WHS) with disagreements resolved by consensus. We assessed a number of variables related to the organization and outcome of studies, including the study design, setting, characteristics of population studied, the number of participants, characteristics of intervention, methods used to measure medication adherence, clinical outcomes, and medication adherence outcomes. We report confidence intervals (CIs) when available and P values when no CIs were available.

 Our search retrieved a total of 6550 articles. Of these articles, 168 were reviewed in full and 51 met inclusion criteria

). The majority (55%) of interventions were aimed at subjects with hypertension. Other patient populations studied included those with diabetes (8%), coronary artery disease (8%), congestive heart failure (14%), and dyslipidemia (10%); 6% of the studies evaluated patients with a mix of cardiovascular and noncardiovascular diseases.

Person-Independent Interventions: Mailed, Faxed, or Hand-Distributed Material

 in which the main intervention was delivered via mail, fax, or hand distribution of paper or video information; 1 was successful.

 Smith et al followed 907 patients after discharge from a hospital stay for myocardial infarction; 2 letters sent to patients and to primary care providers describing the importance of beta-blocker use yielded improved adherence as measured by pharmacy claims data. Patients receiving mailings were 17% more likely to have 80% of days covered (relative risk = 1.17; 95% CI = 1.02, 1.29). The 2 unsuccessful studies described direct patient mailings of written information on hypertension

 or video information on 1 of 4 medications (2 blood pressure agents, an antihyperlidemic, and transdermal estrogen).

 Takala defined adherent patients as those still under treatment after 2 years,

 and Powell and Edgren studied mean medication possession ratios.

Person-Independent Interventions: Electronic Systems

 that examined a range of electronic interventions, including use of electronic pillboxes with programmable reminders, automated phone calls with interactive components, computer-generated individualized interventions, and home automatic blood pressure monitoring. Overall, this group included more positive studies than the mailed/faxed/hand-delivered group (4 out of 6 showed improved adherence).

 studied 33 people with diabetes whose baseline adherence was less than 80%. This study tested use of electronic pill caps with a time display and programmable beeper compared with electronic pill caps alone. Participants with programmable beeper pill caps showed improved adherence (80% intervention vs 60% control; 

 studied the effect of automated phone calls. Friedman and colleagues followed 299 hypertensive patients, randomizing patients to an interactive computer-based home telemonitoring system versus usual care. Patients called in weekly, shared information by phone regarding automated blood pressure readings and adherence, and received targeted education and motivational counseling (all automated), with responses forwarded to doctors. Adherence, measured by pill count, was not  significantly different between groups in unadjusted analysis; adjustment for age, sex, and baseline adherence yielded significant differences (17.7% adherence in intervention group vs 11.7% in control group; 

 = .03). Piette et al randomized 280 patients to biweekly automated interactive phone calls with structured messages that were adjusted based on patient responses, followed by targeted nurse calls versus usual care. Differences from Friedman et al’s intervention include outgoing calls to patients (Friedman et al required patients to call in) and limited nonautomated phone follow-up. Piette and colleagues found that intervention patients were “substantially less likely to report adherence problems” (based on self-report using modified Morisky scale) (

 = .003, no adherence percentages given) but did not provide baseline adherence (56% reported “any medication problem” at baseline).

 followed 404 adults, describing the impact of a computer program designed to mimic the reasoning and problem-solving of humans based on an integrative model of behavioral change (the transtheoretical model) and incorporating an individualized computer-generated report mailed to patients. They found an improvement in adherence at 18 months (odds ratio = 2.86, 

 studied 217 patients newly diagnosed with hypertension who were randomized in a factorial manner to a computerized decision analysis intervention, mailed video and leaflet, both, or neither. Neither the decision analysis nor the mailed video and leaflet resulted in significant improvement. Marquez-Contreras et al

 examined use of home automatic blood pressure monitoring (along with a phone call with instructions on use) in 250 patients with uncontrolled or newly diagnosed hypertension. At the end of 6 months, 92% of the intervention group had adherence greater than 80% (assessed using electronic pillboxes) compared with 74% of the control group (

 Person-Dependent Interventions: Nonautomated Phone Calls

 Eight studies described the use of nonautomated phone calls to improve adherence. Calls were made by trained lay people,

 Overall, 5 of these studies showed a nonsignificant improvement in adherence, a single study

showed significant improvement but did not clearly define its adherence outcome measure, and 2 showed significant improvement with clearly defined outcomes.

 Neither phone calls by trained lay people nor calls by a nurse yielded significant improvements in adherence. Pharmacist calls showed mixed results. Faulkner et al

 described 12 weeks of weekly phone calls from a pharmacist for patients after cardiac surgery or angioplasty and found a significant improvement in adherence as measured by pharmacy refills (60% adherence for the phone call group vs 27% adherence for the control group at 2 years for lovastatin). Mehos et al19 evaluated the effect of monthly pharmacist phone calls in conjunction with home blood pressure monitoring in a group of hypertensive patients who all received direct clinical services from a pharmacist, and found no significant improvement.

 Three studies did not specify the identity of the caller. Antonicelli et al

described a home telemonitoring intervention managed by a specialized congestive heart failure team that included doctors and nurses, without specifying the actual caller. The study reported significant improvement in adherence (91% in the intervention group vs 46% in the control group, 

<.03), but did not adequately define how adherence was measured. Sclar et al21 used phone calls in which adherence was reinforced and monthly mailings delivered to patients with previously treated and newly diagnosed hypertension, and found statistically significant improvements in both intervention groups (previously treated, 82% adherence intervention vs 48% control; newly treated, 93% vs 52%; 

<.05 for both). Guthrie22 used phone reminders along with mailings to patients with elevated cholesterol and found no significant difference in self-reported pravastatin use.

 Person-Dependent Interventions: In-Person Meetings

 We identified 34 in-person interventions

 (Table 2). These interventions included those conducted in the home,

 Overall, in-person interventions were more likely (56%) than phone calls (38%) to result in significant improvement.

 examined the effect of a trained lay person (a health educator) visiting the home of a patient to provide education to both family members and the patient, and found a significant improvement in self-reported adherence scores. Saunders et al

 also incorporated visits by trained lay persons. They sent reminder letters to patients with hypertension and used patient-recorded blood pressure and medication records, adding field worker home visits after 2 reminder letters went unanswered, but did not find significant improvement. Kirscht et al

 applied a factorial design to multiple educational and behavioral strategies (Table 2) and found a significant improvement in adherence among patients who received nurse-administered home visits aimed at a support person as well as the patient (adherence 65% vs control 55%, 

 examined monthly home visits with or without home blood pressure monitoring (the training of the home visitor was not identified) and found no significant improvement. Both effective in-home interventions incorporated family member involvement, in contrast to the 2 unsuccessful interventions.

 The 3 work site interventions provided hypertension care by a nurse

; improvement was shown only in the study in which a nurse acted with relative autonomy.

 examined the effect of hypertension care administered to 457 patients by a specially trained nurse at the work site. All aspects of blood pressure management were handled directly by the nurse, and the intervention demonstrated improved adherence (68% vs 49% had at least 80% of medications consumed based on pill count).

 that were conducted in pharmacies, all administered by pharmacists. All but 1 study in this group showed success at improving adherence. Although methods of measuring adherence differed between studies, the successful interventions improved adherence by 7% to 27% (Table 2). Interventions in this group were similar, involving an in-person meeting with a pharmacist in which patient-centered medication histories were obtained, medication knowledge was elicited and expanded upon, and disease and lifestyle teaching was conducted, sometimes with accompanying written information. All 6 studies lasted between 6 and 12 months.

 made use of blister packaging in addition to clinical pharmacist meetings and medication education for 159 patients with hypertension, hyperlipidemia, and other diseases, and found 96% adherence at 6 months in intervention patients compared with 69% in controls (

 found an adherence improvement of 11% (95% CI = 5%, 17%) among 314 patients with hypertension over 12 months, but noted that the difference declined to a nonsignificant level after the intervention was discontinued. Blenkinsopp et al

 followed 282 patients with hypertension recruited by community pharmacists over 6 months (63% adherence in intervention group vs 50% in control group, 

 followed 152 patients with congestive heart failure, defining nonadherence as fewer than 80% of days without opening the electronic pill bottle, and found all patients in the intervention group took pills more than 80% of days, whereas 86% in the control group reached that threshold (relative risk = 0.5, 95% CI = 0.4, 0.6). Vrijens et al33 followed 392 patients with hyperlipidemia and found that intervention patients had higher adherence at follow-up (96% vs 89%, 

<.001). The study of patients with coronary artery disease

used a self-reported score to measure adherence (making comparison with other studies difficult) and reported very high baseline and follow-up adherence scores. For this reason, the nonsignificant findings in this study may not be representative of the group of pharmacist interventions.

 that were conducted in a clinic setting. Among in-person clinic interventions conducted by a lay person or nurse,

 50% of studies showed significant improvement over the control group (Table 2). Whereas inperson pharmacist interventions showed high success rates when conducted in a pharmacy, in-person pharmacist interventions in clinics

showed the lowest rate of success among clinic interventions (38% compared with 50% to 67% for other in-clinic interventions). All 3 successful clinic interventions

were carried out at clinics that also had medication-dispensing capabilities. The 5 unsuccessful clinic interventions

 were carried out at primary care offices and neighborhood clinics that did not dispense medications.

 Three in-person clinic interventions were carried out by physicians.

 Although 2 of these studies were successful,

 neither used a rigorous method of assessing adherence. Yilmaz et al

 studied the impact of verbal advice on statin benefits from an “expert physician.” At the end of the study there was an increased likelihood of being on continuous statin therapy (63% intervention vs 46% control), but the authors did not define the outcome or the data source. Avanzini et al

 studied 1771 patients with hypertension, randomized to care by (1) doctors who had the opportunity to educate themselves extensively on hypertension and then designed and implemented hypertension management guidelines or (2) doctors without this experience. At follow-up, 4% of intervention vs 10% of control patients “admitted poor compliance,” but the authors give no further information on how this outcome was calculated. Birtwhistle et al

 randomized patients with hypertension to every-3-month versus every-6-month physician follow-up and found no significant difference in adherence. In-person interventions for patients at the point of hospital discharge

 (67% successful) showed lower success rates than interventions carried out in a pharmacy (83%) but higher success rates than those carried out in a clinic setting (47%). However, although 4 out of 6 studies showed significant improvement in adherence,

 2 of the successful studies had unclear definitions of adherence.

In this review of randomized controlled trials designed to improve adherence to cardiovascular medications, electronic interventions and in-person, pharmacy-based interventions showed the highest success rates.

Sarah L. Cutrona, MD, MPH

Articles

Healthcare Information Technology Interventions to Improve Cardiovascular and Diabetes Medication Adherence

Modes of Delivery for Interventions to Improve Cardiovascular Medication Adherence