Diabetics Veterans

Why are Intermediate Outcomes in Diabetics Veterans Still Sub-Optimal?

Co-Investigators: Brad Doebbeling, MD, MSc and Laura Jones, PhD

Project Funding Number IIR 04-266 funded by the VA Health Services Research and Development

January 2, 2006-December 31, 2007

Background:  Recent reports suggest substantial progress on some processes of diabetes care, with over 90% of diabetic veterans receiving A1c assessment.  Yet, as many as 50% still have risk factors (A1c, blood pressure (BP), low density lipoprotein-cholesterol (LDL-C)) above goal.  This gap between knowing the levels and successfully reducing them deserves immediate attention.

Objectives:  We propose a new set of Clinical Action Performance Measure (CAPMs) for diabetes that focus on intensifying medication regimes to reduce A1c, BP, and LDL-C, constructed entirely from esisting 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) two 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 contruct 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, examing 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) patients, (2) clinician and (3) VAMC level characteristics assocatied 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 concordanct care.  After accounting for patient-level characteristics, (H2.2) for clinicians, being a generalist or practicing in CBOC is associated with lower CAPM adherence; and for VAMCs, (h2.3a) Qualtiy Manager Survey measure 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 associted with better CAPM adherence at athe 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: This is an observational study using local VISTA data from 2 VAMCs and national DEpiC, 2001 VAMC Quality Mnaager Survey and 2001 VHA Employee Survey data to develop CAPMs from the VA/DoD Guidelines.  Using structured, iterative group process based on evidence and expert opinion and the RAND Appropriateness Method, we will engage two panels of clinicians and implementation experts for development, refinement and validation.  We will test specific hypotheses at the patient, provider, and VAMC level with multilevel multivariable models to account for clustered data. CAPMs will be constructed entirely from existing data.

Findings/Results: No results at this time.