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Improving Adherence to Cardiovascular Disease Medications With Information Technology
William M. Vollmer, PhD; Ashli A. Owen-Smith, PhD; Jeffrey O. Tom, MD, MS; Reesa Laws, BS; Diane G. Ditmer, PharmD; David H. Smith, PhD; Amy C. Waterbury, MPH; Jennifer L. Schneider, MPH; Cyndee H. Yo
The 3 Key Themes in Health Information Technology
Julia Adler-Milstein, PhD
Leveraging EHRs to Improve Hospital Performance: The Role of Management
Julia Adler-Milstein, PhD; Kirstin Woody Scott, MPhil; and Ashish K. Jha, MD, MPH
Electronic Alerts and Clinician Turnover: The Influence of User Acceptance
Sylvia J. Hysong, PhD; Christiane Spitzmuller, PhD; Donna Espadas, BS; Dean F. Sittig, PhD; and Hardeep Singh, MD, MPH
Cost Implications of Human and Automated Follow-up in Ambulatory Care
Eta S. Berner, EdD; Jeffrey H. Burkhardt, PhD; Anantachai Panjamapirom, PhD; and Midge N. Ray, MSN, RN
Primary Care Capacity as Insurance Coverage Expands: Examining the Role of Health Information Technology
Renuka Tipirneni, MD, MSc; Ezinne G. Ndukwe, MPH; Melissa Riba, MS; HwaJung Choi, PhD; Regina Royan, MPH; Danielle Young, MPH; Marianne Udow-Phillips, MHSA; and Matthew M. Davis, MD, MAPP
Adoption of Electronic Prescribing for Controlled Substances Among Providers and Pharmacies
Meghan Hufstader Gabriel, PhD; Yi Yang, MD, PhD; Varun Vaidya, PhD; and Tricia Lee Wilkins, PharmD, PhD
Health Information Exchange and the Frequency of Repeat Medical Imaging
Joshua R. Vest, PhD, MPH; Rainu Kaushal, MD, MPH; Michael D. Silver, MS; Keith Hentel, MD, MS; and Lisa M. Kern, MD
Information Technology and Hospital Patient Safety: A Cross-Sectional Study of US Acute Care Hospitals
Ajit Appari, PhD; M. Eric Johnson, PhD; and Denise L. Anthony, PhD
Automated Detection of Retinal Disease
Lorens A. Helmchen, PhD; Harold P. Lehmann, MD, PhD; and Michael D. Abràmoff, MD, PhD
Trending Health Information Technology Adoption Among New York Nursing Homes
Erika L. Abramson, MD, MS; Alison Edwards, MS; Michael Silver, MS; Rainu Kaushal, MD, MPH; and the HITEC investigators
Electronic Health Record Availability Among Advanced Practice Registered Nurses and Physicians
Janet M. Coffman, PhD, MPP, MA; Joanne Spetz, PhD; Kevin Grumbach, MD; Margaret Fix, MPH; and Andrew B. Bindman, MD
The Value of Health Information Technology: Filling the Knowledge Gap
Robert S. Rudin, PhD; Spencer S. Jones, PhD; Paul Shekelle, MD, PhD; Richard J. Hillestad, PhD; and Emmett B. Keeler, PhD
Overcoming Barriers to a Research-Ready National Commercial Claims Database
David Newman, JD, PhD; Carolina-Nicole Herrera, MA; and Stephen T. Parente, PhD
The Effects of Health Information Technology Adoption and Hospital-Physician Integration on Hospital Efficiency
Na-Eun Cho, PhD; Jongwha Chang, PhD; and Bebonchu Atems, PhD

Improving Adherence to Cardiovascular Disease Medications With Information Technology

William M. Vollmer, PhD; Ashli A. Owen-Smith, PhD; Jeffrey O. Tom, MD, MS; Reesa Laws, BS; Diane G. Ditmer, PharmD; David H. Smith, PhD; Amy C. Waterbury, MPH; Jennifer L. Schneider, MPH; Cyndee H. Yo
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.
Objectives
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.

Methods
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.

Results
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).

Conclusions
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-SP510
PATIENT (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.

METHODS

Additional methods, details, and results are included in the eAppendix, available at www.ajmc.com.

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

RESULTS

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

 
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