Medication Adherence Clinical Decision Support (ADH Wizard)

• ClinicalTrials.gov Identifier: NCT03748420
• Recruitment Status: Active, not recruiting
• First Posted: November 20, 2018
• Last Update Posted: March 12, 2021
• Sponsor: HealthPartners Institute
• Collaborator: National Heart, Lung, and Blood Institute (NHLBI)
• Information provided by (Responsible Party): HealthPartners Institute

Study Description

Brief Summary:

More than 50% of adults treated for diabetes, hypertension, or lipid disorders have suboptimal medication adherence, a prominent barrier to continued improvement in chronic disease care in the United States. Primary care providers (PCPs) often fail to identify medication nonadherence and/or have insufficient time and training to address underlying reasons for it. In this project, we propose a patient-centered and technology-driven strategy to identify patients with adherence issues and apply a team approach to help them achieve evidence-based personalized goals for glucose, blood pressure, or lipids. This intervention extends the use of a widely available clinical decision support (CDS) infrastructure to support a model of care that, for the first time outside of a fully integrated care environment, will integrate pharmacists within the primary care team. The intervention relies on a continuous health informatics loop to do the following: (a) identify high-risk patients with adherence problems at the point of care by expanding the capability of an electronic medical record (EMR)-linked CDS to identify poor adherence to medications; (b) establish and maintain an auto-populating up-to-date registry of patients identified for proactive pharmacist outreach; (c) implement a pharmacist outreach strategy based on an information-motivation-behavioral (IMB) framework recommended by the World Health Organization (WHO) with demonstrated ability to influence adherence across a variety of clinical applications; and (d) coordinate care and adherence information by incorporating pharmacist assessment and action plans into CDS at subsequent office encounters.

Condition or disease	Intervention/treatment	Phase
Hyperlipidemia; Hypertension; Diabetes; Medication Nonadherence	Other: Adherence-enhanced clinical decision support; Behavioral: ReachOut Adherence intervention	Not Applicable

Detailed Description:

Patients are randomly allocated 1:1 through a computer-generated program to either control or intervention.

Control: All control patients will continue to receive the basic Electronic Medical Record (EMR)-linked Clinical Decision Support (CDS) tool for cardiovascular (CV) risk factor management. This CDS includes algorithmically derived identification of high CV risk patients and prioritized treatment suggestions for lipids, Blood Pressure (BP), glycemic control, weight, tobacco, and aspirin use based on distance from goal, current medications, labs, allergies, and safety considerations. The basic CDS does not include any information on medication adherence. Patients will receive basic CDS plus usual care.

Intervention: For intervention patients, the basic CDS system for CV risk-factor control is enhanced to support a team-based care model that identifies risk of non-adherence, computes adherence information and incorporates it in the CDS, creates a registry to direct proactive pharmacist outreach, and coordinates action plans. To do this, the CDS Web service will combine EMR-identified medications with Epic Medication Adherence score (e-PDC) data. Algorithms will identify poor adherence using e-PDC scores available within the Epic EMR at patient encounters. When medication adherence issues are identified, alert messages will appear on the CDS tools for patients and providers. Patients with index visits will be followed-up for 6 months as they continue to receive CDS at subsequent encounters. At 6 months, patient e-PDC as well as clinical outcomes will be reevaluated. Patients with persistent adherence issues identified are added to a registry that is used by the pharmacists to conduct outreach. Pharmacist outreach will be conducted primarily by phone, but in-person arrangements are also an option. Pharmacists conducting outreach will identify themselves to patients as part of the care team working with the primary care provider (PCP). Examples of specific action plans that may be recommended includes education, recommending lower cost alternative medications or combination medications, addressing side effects, using pill boxes, modifying pill-taking schedules and/or using reminder systems, or referrals to medication therapy management (MTM) services or health educators. Pharmacists will be guided by a script template for the phone outreach that walks them through the IMB intervention and data collection. The pharmacists involved will have full read/write access to the EMR in full compliance with HIPAA regulations. They can make medication changes through established care protocols and/or communicate with the prescriber through secure messaging or phone consultation. The date of the pharmacist outreach and actions that result from the IMB intervention will be documented in the EMR and incorporated into subsequent CDS tools and registries to reflect the new patient state.

Study Design

• Study Type: Interventional (Clinical Trial)
• Estimated Enrollment: 8357 participants
• Allocation: Randomized
• Intervention Model: Parallel Assignment
• Masking: None (Open Label)
• Primary Purpose: Health Services Research
• Official Title: A Team-Based and Technology-Driven Adherence Intervention to Improve Chronic Disease Outcomes
• Actual Study Start Date: August 19, 2020
• Estimated Primary Completion Date: July 2022
• Estimated Study Completion Date: July 2022

Arms and Interventions

Arm	Intervention/treatment
No Intervention: Usual Care; Basic clinical decision support alone
Experimental: ReachOut Adherence intervention; Adherence-enhanced clinical decision support plus pharmacist-based adherence outreach	Other: Adherence-enhanced clinical decision support; Clinical decision support system integrating electronic medication data with claims data on prescription fills; Behavioral: ReachOut Adherence intervention; The pharmacist outreach intervention is based on the theoretically grounded and evidence-based Information-Motivation-Behavioral framework endorsed by the World Health Organization to improve adherence

Outcome Measures

Primary Outcome Measures :

1. Change in blood pressure medication adherence [ Time Frame: 12 months after an index visit ]
   Number of patients who achieve an Epic Medication Adherence Score (e-PDC) greater than or equal to 80% for all blood pressure medications
2. Change in blood pressure control [ Time Frame: 12 months after an index visit ]
   Mean change in systolic blood pressure (SBP) from the last BP within the 12 months prior to the index visit, to the last BP value within 12 months after the index visit.
3. Change in non-insulin glycemic medication adherence [ Time Frame: 12 months after an index visit ]
   Number of patients who achieve an Epic Medication Adherence Score (e-PDC) greater than or equal to 80% for all non-insulin glycemic medications
4. Change in glycemic control [ Time Frame: 12 months after an index visit ]
   Change in A1C (glycated hemoglobin) value from the last lab test within the 12 months prior to the index visit, to the last lab test within 12 months after the index visit.
5. Change in statin medication adherence [ Time Frame: 12 months after an index visit ]
   Number of patients who achieve an Epic Medication Adherence Score (e-PDC) greater than or equal to 80% for all statin medications

Secondary Outcome Measures :

1. Cost of the intervention [ Time Frame: 12 months after an index visit ]
   Intervention costs and incremental medical care costs attributable to the intervention, defined from the health system perspective.
2. Long-term Health Impact [ Time Frame: Simulated 10, 20 and 30-years post intervention ]
   We will estimate the long-term health impact as predicted by the HealthPartners Institute ModelHealthTM: Cardiovascular Disease microsimulation model.
3. Long-term cost-effectiveness [ Time Frame: Simulated 10, 20 and 30-years post intervention ]
   We will estimate the cost-effectiveness of the intervention as predicted by the HealthPartners Institute ModelHealthTM: Cardiovascular Disease microsimulation model.

Eligibility Criteria

• Ages Eligible for Study: 18 Years to 75 Years (Adult, Older Adult)
• Sexes Eligible for Study: All
• Accepts Healthy Volunteers: No

Criteria

Inclusion Criteria:

• One or more of the following clinical criteria:

A. In the 12 months prior to the index visit, most recent A1C ≥8% AND have one or more active non-insulin glycemic medications on their electronic health record (EHR) medication list AND a potential adherence issue for one or more of these medications based on the Epic Medication Adherence score (e-PDC <80%, moderate or high confidence).

B. Two consecutive encounters with BP values ≥140/90 mm Hg AND one or more BP medications on their EHR medication list AND a potential adherence issue identified (e-PDC <80%, moderate/high confidence).

C. Meet the American College of Cardiology/American Heart Association (ACC/AHA) criteria listed below for moderate or high-intensity statin use AND a statin medication on their EHR medication list AND a potential statin adherence issue identified (e-PDC <80%, moderate/high confidence):

1. Age >21 with atherosclerotic cardiovascular disease (ASCVD) identified by a cardiovascular disease (CVD) diagnosis on the problem list or two or more International Classification of Diseases (ICD)-10 diagnostic codes in the last 2 years
2. Age >21 and LDL >190 mg/dL
3. Aged 40 to 75 AND diabetes identified by the diagnosis on the problem list or two or more ICD-10 diagnostic codes in the last 2 years
4. Aged 40 to 75 with 10-year CV Risk Score >7.5% based on the ACC/AHA 10-year ASCVD risk equation.

Exclusion Criteria:

An individual who meets any of the following criteria will be excluded from the study analysis:

1. Patients enrolled in hospice,
2. Patients with active cancer or undergoing chemotherapy
3. Patients with pregnancy in the last year
4. Patients without HealthPartners insurance coverage for at least 11 of the 12 months before the index visit will be excluded from cost analysis.
5. For Statin cohort, ≥1 LDL result <100 mg/dl within 2 years

Contacts and Locations

Locations

United States, Minnesota

HealthPartners Care System

Bloomington, Minnesota, United States, 55425

Sponsors and Collaborators

HealthPartners Institute

National Heart, Lung, and Blood Institute (NHLBI)

Investigators

• Principal Investigator: JoAnn M Sperl-Hillen, MD; HealthPartners Institute

More Information

Publications:

Gilmer TP, O'Connor PJ, Sperl-Hillen JM, Rush WA, Johnson PE, Amundson GH, Asche SE, Ekstrom HL. Cost-effectiveness of an electronic medical record based clinical decision support system. Health Serv Res. 2012 Dec;47(6):2137-58. doi: 10.1111/j.1475-6773.2012.01427.x. Epub 2012 May 11.

Desai JR, Sperl-Hillen JM, O'Connor PJ. Patient preferences in diabetes care: overcoming barriers using new strategies. J Comp Eff Res. 2013 Jul;2(4):351-4. doi: 10.2217/cer.13.36.

Kharbanda EO, Nordin JD, Sinaiko AR, Ekstrom HL, Stultz JM, Sherwood NE, Fontaine PL, Asche SE, Dehmer SP, Amundson JH, Appana DX, Bergdall AR, Hayes MG, O'Connor PJ. TeenBP: Development and Piloting of an EHR-Linked Clinical Decision Support System to Improve Recognition of Hypertension in Adolescents. EGEMS (Wash DC). 2015 Jul 9;3(2):1142. doi: 10.13063/2327-9214.1142. eCollection 2015.

O'Connor P. Opportunities to Increase the Effectiveness of EHR-Based Diabetes Clinical Decision Support. Appl Clin Inform. 2011 Aug 31;2(3):350-4. doi: 10.4338/ACI-2011-05-IE-0032. Print 2011.

O'Connor PJ, Desai JR, Butler JC, Kharbanda EO, Sperl-Hillen JM. Current status and future prospects for electronic point-of-care clinical decision support in diabetes care. Curr Diab Rep. 2013 Apr;13(2):172-6. doi: 10.1007/s11892-012-0350-z.

O'Connor PJ, Sperl-Hillen JM, Rush WA, Johnson PE, Amundson GH, Asche SE, Ekstrom HL, Gilmer TP. Impact of electronic health record clinical decision support on diabetes care: a randomized trial. Ann Fam Med. 2011 Jan-Feb;9(1):12-21. doi: 10.1370/afm.1196.

Sperl-Hillen JM, Averbeck B, Palattao K, Amundson J, Rush B, PJ OC. Outpatient EHR-Based Diabetes Clinical Decision Support That Works: Lessons Learned From Implementing Diabetes Wizard. Diabetes Spectrum. 2010;23(3):150-154.

Sperl-Hillen JM, Crain AL, Margolis KL, Ekstrom HL, Appana D, Amundson G, Sharma R, Desai JR, O'Connor PJ. Clinical decision support directed to primary care patients and providers reduces cardiovascular risk: a randomized trial. J Am Med Inform Assoc. 2018 Sep 1;25(9):1137-1146. doi: 10.1093/jamia/ocy085.

HealthPartners Institute for Education and Research. ModelHealth Microsimulation Models. 2015. https://www.healthpartners.com/hprf/modelhealth/index.html. Accessed March 2, 2017.

Agency for Healthcare Research and Quality. Medical Expenditure Panel Survey. 2001-2010; http://meps.ahrq.gov/mepsweb/. Accessed March 2, 2017.

Kung Yee L, Zeger S. Longitudinal data analysis using generalized linear models. Biometrika. 1986;73(1):13-22.

Raebel MA, Schmittdiel J, Karter AJ, Konieczny JL, Steiner JF. Standardizing terminology and definitions of medication adherence and persistence in research employing electronic databases. Med Care. 2013 Aug;51(8 Suppl 3):S11-21. doi: 10.1097/MLR.0b013e31829b1d2a.

• Responsible Party: HealthPartners Institute
• ClinicalTrials.gov Identifier: NCT03748420
• Other Study ID Numbers: A16-691; 1R01HL136937-01A1 ( U.S. NIH Grant/Contract )
• First Posted: November 20, 2018
• Last Update Posted: March 12, 2021
• Last Verified: March 2021

Individual Participant Data (IPD) Sharing Statement:

• Plan to Share IPD: No

• Studies a U.S. FDA-regulated Drug Product: No
• Studies a U.S. FDA-regulated Device Product: No

Keywords provided by HealthPartners Institute:

Decision Support Systems, Clinical; Cardiovascular Diseases; Cardiovascular Risk Factor; Medication Non-adherence

Additional relevant MeSH terms:

Hyperlipidemias; Dyslipidemias; Lipid Metabolism Disorders; Metabolic Diseases