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BACKGROUND/RATIONALE:
Recent reports suggest substantial progress toward better quality of diabetes care for veterans. For example, over 90% of VA users with diabetes now receive annual A1c testing. Meanwhile, improvement in risk factor levels (A1c, lipids, blood pressure (BP)) has not kept the same pace: as many as 50% of diabetic patients have risk factor levels above goal. Control of risk factors is critical to improving diabetes outcomes. However, current quality measures focus mainly on assessment. A logical next step is to focus on clinical actions that lead directly to risk factor control.
OBJECTIVE(S):
We propose a new set of Clinical Action Performance Measures (CAPMs) for diabetes that focus on intensifying medication regimens to reduce A1c, BP, and LDL-C, constructed entirely from existing data. We will use well-established methods of quality indicator development, guided by our REAP's conceptual framework of moving evidence to quality indicators, then analyzing care according to these newly developed indicators. We will build on the VA's strong foundation of quality improvement and health services research, utilizing data from (1) 2 mature VA projects: the Diabetes Epidemiology Cohort (DEpiC) (D. Miller PI) and the VAMC Quality Manager Survey (B. Doebbeling PI); and (2) the VHA Employee Survey; and collaborate with the Chief Officer of OQP and the Directors of QUERI-DM to:
1. DEVELOP a set of automated CAPMs: a. Draft a set of CAPMs for glycemic control (CAPM-A1c), BP control (CAPM-BP), and lipid control (CAPM-LDL) based on VA/DOD evidence-based guidelines with input from OQP, QUERI-DM, and a literature review; b. Select a subset of CAPMs for further refinement using a National Expert Panel; c. Refine the selected CAPMs with a Local Clinician Panel using a structured, iterative formative research process, the Nominal Group Technique; d. Validate the automated CAPMs: 1. For construct validity, develop a structured chart review instrument and compare the automated CAPMs with CAPMs derived using manual medical record review; 2. For content and face validity, use the National Expert Panel; 3. For predictive validity, examine the CAPMs' association with subsequent risk factor levels. We hypothesize that (H1) among patients with uncontrolled risk factors, CAPM adherence by their physicians is associated with subsequent improved risk factor levels.
2. EVALUATE the CAPMs' readiness for implementation, by examining (1) patient, (2) clinician and (3) VAMC level characteristics associated with CAPM adherence, using Birmingham and Roudebush VAMC VISTA, national DEpiC, VAMC Quality Manager Survey and VHA Employee Survey data. We hypothesize that: (H2.1) patients who are older, minority, infrequent users of VA outpatient services, and frequent users of non-VA outpatient care receive less CAPM concordant care. After accounting for patient-level characteristics, (H2.2) for clinicians, being a generalist or practicing in CBOCs is associated with lower CAPM adherence; and for VAMCs, (H2.3a) Quality Manager Survey measures of more intense implementation of guidelines, use of physician feedback, or physician-nurse communication are associated with better CAPM adherence; and (H2.3b) VHA Employee Survey measures of better customer orientation, teamwork and communication are associated with better CAPM adherence at the VAMC level.
3. Capitalize on the CAPMs' construction out of existing data and extend collaborations with national VA quality champions developed in the first 2 Specific Aims to DISSEMINATE AND IMPLEMENT the automated CAPMs: a. Implement them rapidly locally with VISN 7 leadership support. b. Use OQP's performance measure development process for piloting and national rollout. c. Integrate them into QUERI-DM implementation programs and interventions.
METHODS:
Performance will be employed for piloting and national rollout. Subsequently, the CAPMs will be integrated into QUERI-DM implementation programs and interventions. The validated CAPMs should be available to the VA community within 2 years.
This study will have 3 phases: (1) CAPM development; (2) CAPM evaluation; and (3) CAPM dissemination. CAPMs will be drafted and validated using national experts and local clinician panels.
Existing quantitative data from multiple sources will be obtained for analysis through data use agreements. New data will be collected from physicians and expert panelists using qualitative methods such as the nominal group technique (NGT) and direct observation of physician practices, and through structured chart review at the BVAMC. The validated CAPMs will be disseminated throughout the VA community using existing communication networks.
FINDINGS/RESULTS:
Data collection has been completed for the physician observation objective. Data sets derived from VISTA, the Quality Manager's survey, and Medicare databases to meet other project objectives have been obtained through data use agreements. Initial data analysis prior to formulation of the CAPMs has begun. The CAPMs may lead to a better understanding of the etiology of the disconnect between process and outcome, and their implementation will improve care for veterans by delaying diabetes' many devastating outcomes including stroke, heart attack, amputation, blindness and renal failure.
IMPACT:
The VA will have a validated set of diabetes performance measures constructed entirely from existing data and focusing on a cutting edge, new dimension of quality of care: medication intensification to reduce risk factor levels. Our dissemination plan will lead to early VA-wide implementation, and it will lay the foundation for VHA and QUERI-DM to continue testing innovative approaches to implementation of best-practices for all veterans with diabetes. Lastly, the CAPM concept should translate readily to other disease states.
PUBLICATIONS:
None at this time.
DRA:
Chronic Diseases, Health Services and Systems
DRE:
Quality of Care
Keywords:
Chronic heart failure, Cost effectiveness, Diabetes, Quality assurance, improvement, Telemedicine
MeSH Terms:
none
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