The American Journal of Managed Care
Special Issue: Health Information Technology
Volume 20
Issue SP 17

Improving Adherence to Cardiovascular Disease Medications With Information Technology

Improving adherence to long-term medication therapy remains a challenge. Health information technology interventions that leverage electronic medical records are promising, low-cost approaches for increasing adherence.


Evaluate the utility of 2 electronic medical record (EMR)-linked, automated phone reminder interventions for improving adherence to cardiovascular disease medications.

Study Design

A 1-year, parallel arm, pragmatic clinical trial in which 21,752 adults were randomized to receive either usual care (UC) or 1 of 2 interventions in the form of interactive voice recognition calls—regular (IVR) or enhanced (IVR+). The interventions used automated phone reminders to increase adherence to cardiovascular disease medications. The primary outcome was medication adherence; blood pressure and lipid levels were secondary outcomes.


The study took place in 3 large health maintenance organizations. We enrolled participants who were 40 years or older, had diabetes mellitus or atherosclerotic cardiovascular disease, and were suboptimally adherent. IVR participants received automated phone calls when they were due or overdue for a refill. IVR+ participants received these phone calls, plus personalized reminder letters, live outreach calls, EMR-based feedback to their primary care providers, and additional mailed materials.


Both interventions significantly increased adherence to statins and angiotensin-converting enzyme inhibitors/angiotensin receptor blockers (ACEIs/ARBs) compared with UC (1.6 to 3.7 percentage points). Adherence to ACEIs/ARBs was also significantly higher for IVR+ relative to IVR participants. These differences persisted across subgroups. Among statin users, IVR+ participants had significantly lower low-density lipoprotein (LDL) levels at follow-up compared with UC (Δ = —1.5; 95% CI, –2.7 to –0.2 mg/dL); this effect was seen mainly in those with baseline LDL levels >100 mg/dL (Δ = –3.6; 95% CI, –5.9 to –1.3 mg/dL).


Technology-based tools, in conjunction with an EMR, can improve adherence to chronic disease medications and measured cardiovascular disease risk factors.

Am J Manag Care. 2014;20(11 Spec No. 17):SP502-SP510PATIENT (Promoting Adherence to Improve Effectiveness of Cardiovasular Disease Therapies) was a pragmatic clinical trial designed to improve adherence to cardiovascular disease medications using a low-cost, electronic medical record linked telephone reminder intervention. Using broad eligibility criteria, we enrolled 21,752 adult members of a health maintenance organization in a randomized trial to evaluate whether 2 phone reminder interventions, compared with usual care, could improve adherence to statins, angiotensin-converting enzyme inhibitors, and angiotensin receptor blockers.

  • We saw small but statistically significant improvements in adherence.
  • Among statin users, intervention participants had significantly reduced followup lipid levels and improved lipid control compared with usual care.
  • The public health impact of these changes, applied across large populations, is uncertain.

Nonadherence to chronic cardiovascular disease (CVD) therapy is well-documented and contributes to increased CVD risk and morbidity.1,2 Low adherence is often the broken link between new therapies and improved health outcomes,3 and is a target for reducing healthcare costs.4,5

The most effective adherence interventions include both educational and behavioral strategies6; however, these can be costly. Further, most interventions thus far have enrolled select patient populations, limiting generalizability. Recently, research has focused on using health information technologies (HITs) to develop low-cost interventions for large populations.7,8

We recently reported on a trial to improve adherence to inhaled corticosteroids in 8517 adult health plan members with asthma.9 That study used automated telephone reminder calls linked with an electronic medical record (EMR). It found a small (2 percentage point) but statistically significant improvement over 18 months in the intent-to-treat analysis, and an increase of 6 percentage points in adherence and decreased asthma symptoms among patients who took the calls. Derose and colleagues10 tested automated reminder calls followed by mailed letters to increase adherence among 5216 adults who received a new statin prescription. The intervention improved fill rates over the next 25 days by 16 percentage points. These and other studies11-14 suggest that HIT/EMR-based reminder interventions offer a promising population-based approach to promoting adherence.

We present the main outcomes for PATIENT (Promoting Adherence to Improve Effectiveness of Cardiovasular Disease Therapies), a pragmatic trial involving members of a health maintenance organization that evaluated the effectiveness of 2 EMR-linked, automated reminder interventions, compared with usual care (UC), in increasing adherence to cardiovascular medications.


Additional methods, details, and results are included in the eAppendix, available at

Study Design

PATIENT was a paralel arm, pragmatic clinical trial in which 21,752 adults were randomized to receive either UC or 1 of 2 interventions designed to increase adherence to statins, angiotensin-converting enzyme inhibitors (ACEIs), and angiotensin receptor blockers (ARBs). The study was funded as a CHOICE (Clinical and Health Outcomes Initiative in Comparative Effectiveness) grant15 by the Agency for Healthcare Research and Quality, and had a mandate to carry out comparative effectiveness research in large, “real-world” populations and to assess treatment effects overall and in relevant subgroups.

Assuming a standard deviation of 0.28 (ie, 28 percentage points), the study had 95% power to detect deltas of 0.025 (2.5 percentage points) in adherence to statins and 0.029 (2.9 percentage points) to ACEIs/ARBs for each active intervention arm relative to UC for the cohort as a whole. Subgroup power is shown in the eAppendix A.

Research Setting

Participants were members of one of 3 regions of the Kaiser Permanente (KP) health plan—Northwest (KPNW), Hawaii (KPH), and Georgia (KPG)—which collectively serve about 944,000 individuals. The Institutional Review Boards of each region approved the study and waived informed consent. An external Data and Safety Monitoring Board and local clinician advisory boards at each site approved the study protocol and monitored the study for safety and data quality.

Participant Selection and Randomization

Using each region’s EMR, we identified participants 40 years and older with diabetes mellitus and/or cardiovascular disease (CVD), suboptimally (<90%) adherent to a statin or ACEI/ARB during the previous 12 months, and due or overdue for a refill. We excluded only individuals with medical conditions that might contraindicate the use of these medications, such as medication allergies, liver failure, cirrhosis, rhabdomyolysis, end-stage renal disease, chronic kidney disease (see eAppendix Table A1 for complete list) and those on KP’s “do not contact” list.

Within each region, we randomly assigned a sample of eligible members to the 3 primary study arms (usual care and 2 intervention arms) in a 1:1:1 ratio at the study outset and repeated this process for previously ineligible members who subsequently met eligibility criteria over the following 5 months. Computer-generated randomization assignments were stratified by region and blocked to assure balance across treatment arms. Neither participants nor providers were blinded to treatment assignment. Study enrollment began in December 2011 and continued through May 2012. Intervention and outcome assessment continued through November 2012.

Study Interventions

UC participants had access to the full range of usual services, including each region’s normal education and care management outreach efforts to encourage statin and ACEI/ARB use.

Interactive Voice Recognition (IVR) Calls. IVR participants received automated phone calls when they were due or overdue for a refill. The calls used speech-recognition technology to educate patients about their medications and help them refill prescriptions (we created separate “refill” and “tardy” calls). The flow of each call was determined by participants’ responses; each call lasted 2 to 3 minutes. At randomization, IVR participants received a pamphlet explaining these calls.

Both call types offered a transfer to KP’s automated pharmacy refill line. The tardy call also offered a transfer to a live pharmacist. With permission, obtained at the first successful call contact, the program left detailed messages on answering machines or with another household member. Enhanced IVR (IVR+). In addition to IVR calls, participants in the IVR+ arm received a personalized reminder letter if they were 60 to 89 days overdue and a live outreach call if they were ≥90 days overdue, as well as EMR-based feedback to their primary care provider. IVR+ participants received additional materials, including a personalized health report with their latest BP and cholesterol levels, a pill organizer, and bimonthly mailings (Table A2 in eAppendix).

Study Measurements

Medication Adherence. We used a modified version of the Proportion of Days Covered (PDC),16 defined from pharmacy dispensing records, for our primary measure. Because we were measuring adherence to chronic medications patients were known to be taking at randomization, we modified the PDC (mPDC) to include the whole follow-up period as the denominator time frame rather than time from first dispensing.17 We accounted for medication on hand at randomization and ignored any medication remaining at the end of follow-up. We computed mPDCs separately for statins and ACEI/ARBs. To simplify enrollment logistics, we defined eligibility at baseline using the simpler Medication Possession Ratio (MPR), which we computed by dividing total days’ dispensed supply by 365 and capping at 1.

Other EMR-Based Data. We used the EMR to capture age, race, gender, healthcare utilization for diabetes and CVD, and BP and lipid levels. Consistent with the Healthcare Effectiveness Data and Information Set reporting guidelines,18,19 we defined BP control as systolic BP (SBP)/diastolic BP <140/90 mm Hg and lipid control as an lowdensity lipoprotein (LDL) <100 mg/dL. Pre- and post BP measurements were available for 91.6% of ACEI/ARB users, while pre- and post LDL measurements were available for 84.2% of statin users; missing values were ignored.

Statistical Analysis

We used an intention-to-treat analysis to compare primary and secondary outcomes between intervention and UC participants. All adherence analyses were conducted separately for users of statins and users of ACEIs/ARBs. We compared each intervention against UC using an α-level of 0.025. We then compared the IVR and IVR+ interventions against each other at an α-level of 0.05 only if either of these initial contrasts was statistically significant, thus assuring a trialwide α-level of 0.05. We used a similar adjustment procedure for all secondary analyses of treatment effects.

The primary analytic model compared post intervention adherence between intervention and UC participants using a general linear model that adjusted for site, gender, age (40-60 years, 61-70 years, 71+ years), number of baseline medications (1-5, 6-10, 11-15, 16+), comorbid diabetes/CVD status, and baseline adherence (≤.4, .4-.75, >.75 for statins; ≤.5, .5-.75, >.75 for ACEIs/ARBs) as fixed main effects. We assessed follow-up from randomization to end-of-study or loss of health plan coverage, whichever came first; baseline refers to the 12 months prior to randomization.

In prespecified secondary analyses, we added interaction terms to our models to estimate subgroup-specific treatment effects and to test for treatment by subgroup interactions. We used similar analytic models to assess the impact of the interventions on BP and LDL-cholesterol levels as continuous variables. We used logistic regression for analyses of BP control and LDL-cholesterol control. All analyses were conducted using SAS version 9.220 or Stata version 11.2.21


Of the 45,051 individuals who met inclusion criteria, we excluded 13.7% due to medical contraindications and another 6.2% for administrative reasons (Figure A1 in eAppendix). From the remaining 36,115 individuals, we randomly selected 25,323 for study inclusion and randomized those people into one of the main study arms (n = 21,752) or to one of the 2 ancillary treatment arms (n = 3571; see eAppendix). Of the former group, 16,380 qualified for statin calls at randomization and are included in the statin analyses; 13,036 qualified for the ACEI/ARB analyses.

Comparison of Intervention and UC Groups

Baseline characteristics of the intervention and UC groups for the pooled statin and ACEI/ARB analysis samples were very similar (Table 1). Among individuals included in the statin analysis, the mean baseline MPR was 0.51. For ACEI/ARB users, mean baseline MPR was 0.53.

Participant Follow-Up and Intervention Process Data

Mean duration of follow-up was 9.6 months and did not vary by treatment arm (Table A3 in eAppendix). IVR participants received, on average, 3.7 call attempts, including 2.4 direct connects or detailed messages; IVR+ participants received an average of 10.1 contact attempts, including 3.3 call attempts (2.2 resulting in direct connects or detailed messages), 5.9 educational mailings, 0.6 reminder letters, and 0.3 live pharmacy outreach call attempts.

Statin Adherence

While statin adherence increased significantly for both IVR and IVR+ participants compared with UC, adherence did not differ significantly between IVR and IVR+ (Table 2). On average, adherence among IVR participants was 2.2 percentage points higher than for UC (95% CI, 1.1-3.4), while the difference was 3.0 (95% CI, 1.9-4.2) percentage points for IVR+. These differences generally persisted in subgroups defined by gender, age, number of baseline medications, and baseline adherence; however, we saw little or no effect among individuals whose baseline adherence was greater than 0.75 or among those with comorbid diabetes and CVD (Table A4 in eAppendix). The intervention effects were present in all sites and differed significantly by site.

Both the IVR and IVR+ arms also significantly increased the proportion of individuals with good adherence (>0.80), with odds ratios (ORs) of 1.16 and 1.14 for IVR+ and IVR versus UC, respectively (Table 2). The same patterns were observed for those with mid-level baseline adherence (0.40-0.70) and for those with baseline adherence >0.75, although only the former were statistically significant.

ACEI/ARB Adherence

The results for ACEI/ARB adherence were similar to those for statin adherence (Table 2). Compared with UC, ACEI/ARB adherence also increased significantly for both IVR (1.6 percentage points) and IVR+ (3.7 percentage points) participants, within this case, the difference between IVR+ and IVR also statistically significant. As with the statin analyses, these patterns generally persisted across subgroups, and we found no evidence of an intervention effect among those with baseline adherence above 0.75 and among those with both diabetes and CVD (Table A6 in eAppendix). Unlike in the statin analysis, the interventions’ effects on ACEI/ARB use were very similar across regions.

Both interventions also resulted in significantly higher levels of “good” ACEI/ARB adherence (>0.80) compared with UC, with ORs (95% CIs) of 1.21 (1.10-1.32) and 1.12 (1.02-1.23) for IVR+ and IVR versus UC, respectively (Table 2). The difference between IVR+ and IVR was not statistically significant.

Impact on Lipids and Blood Pressure

Among statin users, we observed a statistically significant reduction in LDL-cholesterol levels among IVR+ relative to UC participants (mean difference = —1.5; 95% CI, –2.7 to –0.2 mg/dL: Table 3). These differences appeared to vary by baseline LDL level, with the greatest reductions occurring in those whose initial LDL levels were above 100 mg/dL (mean difference = —3.6; 95% CI, –5.9 to –1.3 mg/dL) and no indication of an intervention effect in those with baseline LDL level below 80 mg/dL. The corresponding interaction test, however, was not significant. These reductions in LDL levels were reflected in improved LDL control for IVR+ versus UC, with significant increases (P = .015) in those whose initial LDL levels were above 100 mg/dL (mean difference = 1.21; 95% CI 1.04-1.42) and borderline significant increases (P = .058) overall [mean difference = 1.10, 95% CI, 1.00-1.22).

We observed no evidence of any impact on SBP or overall BP control among ACEI/ARB users, either overall or in subgroups defined by initial SBP levels (Table 3).

Participant Safety

During the 1-year follow-up period (Table A9 in eAppendix), 427 study participants (2.0%) died; 0.3% of participants were hospitalized for conditions potentially related to ACEI/ARB use; and 0.02% were hospitalized for conditions potentially related to statin use. These patterns were similar for the 3 intervention arms.


The PATIENT trial demonstrated that a low-cost EMR-based intervention, utilizing HIT tools, can improve adherence among patients with diabetes and/or CVD as part of a population-based disease management strategy, and extends our previous work showing similar improvements among patients with asthma.9

Although the improvements were statistically significant across subgroups, the overall effect was small (1.6-3.7 percentage points). This may, however, still have important public health implications. For instance, a 2 mm Hg drop in blood pressure, on a population basis, translates into long-term cardiovascular risk reduction.22 Unfortunately, little is known about the public health impact of small changes in medication adherence.

Of note in this regard, we observed a statistically significant reduction in LDL-cholesterol levels among IVR+ participants who were taking statins. This effect was most pronounced among those with poorly controlled LDL at baseline, for whom the IVR+ and IVR interventions resulted in LDL reductions of 3.6 and 1.7 mg/dL versus UC, respectively. A recent meta-analysis of 26 randomized trials suggests that each 39 mg/dL decrease in LDL leads to annual reductions in all-cause mortality and major vascular events of 10% and 21%, respectively.23 Assuming a sustained effect, the 3.6 mg/dL reduction in LDL we observed for IVR+ would be associated with a nearly 1% annual reduction in mortality and 2% reduction in major vascular events. Therefore, while modest, these LDL reductions could have meaningful public health impact. We did not observe significant improvements in BP control, despite increases in ACEI/ARB adherence similar to those we observed with statins. This may reflect a different adherence threshold for clinical impact or the complexity of BP control.24

Reminder interventions show promise for improving adherence with CVD medications.10,25-28 The PATIENT intervention also showed an impact on lipid levels. Together these studies reinforce the value of IVR/EMR strategies to support adherence among new and established users of statins.

The sustainability of such strategies, however, is likely dependent on patient perceptions of the usefulness versus intrusiveness of the calls. To assess this, we conducted qualitative, semi-structured follow-up interviews with 49 study participants. Most (70%) indicated they appreciated the calls, while only 8% said they were annoyed by them. In addition, 70% reported listening to at least 1 call in its entirely, though 22% reported hanging up on subsequent calls. Only 6% described the calls as “not useful,” while 43% reported that the calls made them feel cared for and supported by the health plan. Close to 60% reported that the calls prompted them to check the status of their medication and take follow-up action. And while intervention “fatigue” might certainly e a barrier to continued efficacy, 94% reported that the service should continue for all health plan members. However, as patients increasingly rely on diverse communication technologies, including email and texting, effective reminder interventions will need to be flexible, adaptive, and personally tailored to match patients’ preferences.

This study’s strengths are a large, real-world patient population, a randomized design, and near-complete participant primary and secondary outcome data derived directly from the EMR. Also, in this trial we show evidence that change in EMR-derived pharmacy dispensing is associated with change in CVD risk factors, supporting the validity of this approach. Limitations should also be noted: a substantial number of participants were never reached by phone, thus diluting delivery and potentially the effectiveness of the IVR intervention. Indeed, the IVR+ intervention was designed largely in recognition of this limitation, although the incremental effect of the added IVR+ components was also small. Post hoc analyses suggested much more substantial effects for those participants who actually received 2 or more calls (Tables A5, A7, A8 in eAppendix); however, the PATIENT intervention model was by design relatively passive and “light-touch.” More actively engaging patients in their own self-care and adherence might have increased the impact of the reminder intervention. Finally, constraints imposed by the funding agency precluded a longer follow-up.

Barriers to adherence can include cost, low health literacy, depression, patient-provider communication, and health beliefs.29 The PATIENT intervention was not designed to address these complex barriers, but to overcome simpler barriers. Our results support the benefit of such programs in improving adherence and provide preliminary evidence for clinical impact. Future interventions that combine HIT-based systems, perhaps with strategies customized to patient preference and more tailored clinical support, offer a promising next step.


The authors would like to acknowledge and thank Data and Safety Monitoring Board members Beverly Green, MD, MPH (Group Health Research Institute); Tom Greene, PhD (University of Utah); and Michael Ho, MD, PhD (VA Eastern Colorado Health Care System).

The authors would also like to thank the following individuals for their roles in the study. All are Kaiser Permanente staff unless otherwise indicated.

Regional KP Advisory Boards: Miriam Bell, MPH; Joshua Barzilay, MD; Wiley Chan, MD; Karen Ching, MD; Juanita Cone, MD; Adrianne Feldstein, MD, MPH; Teri Laurenti, PharmD, CCP; Lisa Nakashimada, PharmD; Samir Patel, MD; Dawn Rock, JD; Ross Takara, MD; Anne Whitlock, BSN, RN, CCP.

Study coordination: Barbara Bachman; Judy Donald; Dana Hankerson-Dyson, MPH; Mara Kalter, MS.

Data collection/entry: Allison Bonifay, MA, LPC; Catherine A. Briggs, BS; Arwen Bunce, MA; Julie Cavese, MA; Allison Firemark, MA; Jeffrey Jensen, MA; Sue Leung, PhD; Jennifer Sanchez; Megan Scheminske, MS; Nina Scott, LCSW; Donna White.

Design and production of educational materials: Sanders Anderson, MSc (Aquent); Alex MacMillan, BA; KPNW Printing & Mailing Department; Amplifii (Bill Skinner).

Data analysis/management: Jennie Brewer; Paul Cheek; James V. Davis, BA; Donna Eubanks, BSCS; Peter Joski; Eric Kopp; Gayle T. Meltesen; Laura Schild; Kari Walker, MS; Carmen Wong, MBA.

Pharmacy staff: Kelly Anderson, CphT; Debbi Baker, PharmD; Regina Christiansen; Nadia Hassan, PharmD, BCPS; Teri Laurenti,PharmD, CCP; Amy Stone Murai, MS, APRN; Kathryn Gauen Ring, RPh; Ruth Su; staff and managers at the KP automated refill centers in all 3 regions; the KPNW Medication Management Program; and KPHI PSS Program.

We also thank Jill Pope, BA, for her assistance with critical editing of the manuscript and Debra Burch and Robin Daily for secretarial assistance in the preparation of this manuscript. These contributors did not receive compensation besides their salaries.

Finally, we thank the Eliza Corporation, Inc (Danvers, Massachusetts) staff who contributed to developing and operating IVR calls, and KP members and health plan staff who participated in the study.

Written permission has been obtained from all persons named in the Acknowledgments section above.

The full trial protocol and other study materials can be found at Affiliations: From Kaiser Permanente Northwest, Portland, OR (WMV, RL, DHS, ACW, JLS, DGD); The Center for Health Research, Kaiser Permanente Georgia, Atlanta (AAO-S, SV); The Center for Health Research, Kaiser Permanente Hawaii, Honolulu (JOT, CHY, AW); and Johns Hopkins University, Baltimore, MD (CJR).

Source of Funding: This project was supported by grant number R01HS019341 from the Agency for Healthcare Research and Quality. The content of this manuscript—which includes design and conduct of the study; collection, management, analysis, and interpretation of the data; and preparation, review, and approval of the manuscript&mdash;is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality.

Author Disclosures: The authors report no relationship or financial interest with any entity that would pose a conflict of interest with the subject matter of this article.

Authorship Information: Concept and design (WMV, JOT, DGD, DHS, ACW, JLS, SV, CJR); acquisition of data (AAO-S, RL, JLS, CHY, AW, SV); analysis and interpretation of data (WMV, AAO-S, JOT, RL, DGD, JLS, AW, SV, CJR); drafting of the manuscript (WMV, AAO-S, RL, DHS, CJR); critical revision of the manuscript for important intellectual content (AAO-S, JOT, DHS, ACW, JLS, AW, SV, CJR); statistical analysis (WMV); provision of study materials or patients (AAO-S, RL, DGD, SV); obtaining funding (WMV, SV); administrative, technical, or logistic support (AAO-S, JOT, RL, ACW, CHY, SV); and supervision (WMV, AAO-S, RL, ACW, CHY).

Address correspondence to: William M. Vollmer, PhD, Senior Investigator, Center for Health Research, Kaiser Permanente Northwest, 3800 N Interstate Ave, Portland, OR 97227. E-mail: Naderi SH, Bestwick JP, Wald DS. Adherence to drugs that prevent cardiovascular disease: meta-analysis on 376,162 patients. Am J Med. 2012;125(9):882-887.

2. Ho PM, Bryson CL, Rumsfeld JS. Medication adherence: its importance in cardiovascular outcomes. Circulation. 2009;119(23):3028-3035.

3. The Network for Excellence in Health Innovation. Thinking outside the pillbox: a system-wide approach to improving patient medication adherence for chronic disease. Published August 12, 2009. Accessed November 4, 2010.

4. Sokol MC, McGuigan KA, Verbrugge RR, Epstein RS. Impact of medication adherence on hospitalization risk and healthcare cost. Med Care. 2005;43(6):521-530.

5. Steiner JF. Rethinking adherence. Ann Intern Med. 012;157(8):580-585.

6. Haynes RB, Ackloo E, Sahota N, McDonald HP, Yao X. Interventions for enhancing medication adherence. Cochrane Database Syst Rev. 2008;2:CD000011.

7. Misono AS, Cutrona SL, Choudhry NK, et al. Healthcare information technology interventions to improve cardiovascular and diabetes medication adherence. Am J Manag Care. 2010;16(12 suppl HIT):SP82-SP92.

8. Howren MB, Van Liew JR, Christensen AJ. Advances in patient adherence to medical treatment regimens: the emerging role of technology in adherence monitoring and management. Social and Personality Psychology Compass. 2013;7(7):427-443.

9. Vollmer WM, Feldstein A, Smith DH, et al. Use of health informtion technology to improve medication adherence. Am J Manag Care. 2011;17(12 Spec No.):SP79-SP87.

10. Derose SF, Green K, Marrett E, et al. Automated outreach to increase primary adherence to cholesterol-lowering medications. JAMA Intern Med. 2013;173(1):38-43.

11. Piette JD, Weinberger M, Kraemer FB, McPhee SJ. Impact of automated calls with nurse follow-up on diabetes treatment outcomes in a Department of Veterans Affairs Health Care System: a randomized controlled trial. Diabetes Care. 2001;24(2):202-208.

12. Vervloet M, Linn AJ, van Weert JC, de Bakker DH, Bouvy ML, van Dijk L. The effectiveness of interventions using electronic reminders to improve adherence to chronic medication: a systematic review of the literature. J Am Med Inform Assoc. 2012;19(5):696-704.

13. Christakis DA, Garrison MM, Lozano P, Meischke H, Zhou C, Zimmerman FJ. Improving parental adherence with asthma treatment guidelines: a randomized controlled trial of an interactive website. Acad Pediatr. 2012;12(4):302-311.

14. McMahon GT, Fonda SJ, Gomes HE, Alexis G, Conlin PR. A randomized comparison of online- and telephone-based care management with internet training alone in adult patients with poorly controlled type 2 diabetes. Diabetes Technol Ther. 2012;14(11):1060-1067.

15. Friedly J, Bauer Z, Comstock BA, et al. Challenges conducting comparative effectiveness research: Clinical and Health Outcomes Initiative in Comparative Effectiveness (CHOICE) experience. Comparative Effectiveness Research. 2014;4:1-12.

16. Andrade SE, Kahler KH, Frech F, Chan KA. Methods for evaluation of medication adherence and persistence using automated databases. Pharmacoepidemiol Drug Saf. 2006;15(8):565-574.

17. Vollmer WM, Xu M, Feldstein A, Smith D, Waterbury A, Rand C. Comparison of pharmacy-based measures of medication adherence. BMC Health Serv Res. 2012;12:155.

18. National Committee for Quality Assurance (NCQA). HEDIS 2014,Healthcare Effectiveness Data and Information Set. Vol. 1, narrative. Washington, DC: National Committee for Quality Assurance; 2013.

19. National Committee for Quality Assurance (NCQA). HEDIS 2014: Healthcare Effectiveness Data and Information Set. Vol. 2, technical specifications for health plans. Washington, DC, National Committee for Quality Assurance; 2013.

20. SAS Enhanced Logging Facilities [computer program]. Version 9.2. Cary, NC: SAS Institute Inc; 2008.

21. STATA Data Analysis and Statistical Software. Version 11.2. College Station, TX: StataCorp; 2011.

22. Stamler J, Rose G, Stamler R, Elliott P, Dyer A, Marmot M. INTERSALT study findings. public health and medical care implications. Hypertension. 1989;14(5):570-577.

23. Cholesterol Treatment Trialists’ (CTT) Collaboration; Baigent C, Blackwell L, Emberson J, et al. Efficacy and safety of more intensive lowering of LDL cholesterol: a meta-analysis of data from 170,000 participants in 26 randomised trials. Lancet. 2010;376(9753):1670-1681.

24. Daugherty SL, Powers JD, Magid DJ, et al. The association between medication adherence and treatment intensification with blood pressure control in resistant hypertension. Hypertension 2012;60(2):303-309.

25. Schedlbauer A, Davies P, Fahey T. Interventions to improve adherence to lipid lowering medication. Cochrane Database Syst Rev. 2010;3:CD004371.

26. Lieu TA, Capra AM, Makol J, Black SB, Shinefield HR. Effectiveness and cost-effectiveness of letters, automated telephone messages, or both for underimmunized children in a health maintenance organization. Pediatrics. 1998;101(4):E3.

27. Mosen DM, Feldstein AC, Perrin N, et al. Automated telephone calls improved completion of fecal occult blood testing. Med Care. 2010;48(7):604-610.

28. Ho PM, Lambert-Kerzner A, Carey EP, et al. Multifaceted intervention to improve medication adherence and secondary prevention measures after acute coronary syndrome hospital discharge: a randomized clinical trial. JAMA Intern Med.

29. Gellad WF, Grenard J, McGlynn EA. A Review of Barriers to Medication Adherence: A Framework for Driving Policy Options. Santa Monica, CA; RAND Corporation, 2009.

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