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September 2011 - August 2015
Automated performance metrics for quality improvement in complex chronic disease


  • Professor, Medicine (CHP/PCOR)
Samson W. Tu
Connie M. Oshiro
Susana Martins
Dan Y. Wang
Michael W. Ashcraft
Amy E. Furman

As the population of older adults in the US increases, there is a growing need for performance measurement systems that take multiple comorbid conditions into account. Most existing performance measures examine one disease at a time, and lack coordination of clinical practice guidelines for multiple conditions. This project aims to improve the treatment of hypertension, heart disease, hyperlipidemia, chronic kidney disease, and heart failure by building on prior clinical decision support (CDS) work to automate quality measurement and automate CDS to assist health professionals in primary care teams. CDS modules for the five clinical conditions above, each of which already take account of comorbidities with respect to that condition, will be integrated into an existing clinical dashboard designed to monitor and improve performance.  Later in the project the researchers plan to coordinate and prioritize guideline recommendations.

Specific aims of this project are to (1) create knowledge bases for automated decision support to supplement current performance measures with clinical detail about evidence-based management for five chronic diseases, (2) to conduct stakeholder interviews with key VA program offices and quality managers regarding prioritization and coordination of recommendations for patients with multiple comorbidities, (3) to elaborate the ATHENA-CDS knowledge bases with additional clinical knowledge about prioritization based on the findings from stakeholder interviews, and (4) to develop systems to provide clinical decision support (CDS) across multiple chronic conditions to health professionals in Patient-Aligned Care Teams (PACTs), which are the VA-specific form of Patient Centered Medical Home (PCMH).

This project builds on several previous lines of work from this lab.  The CDS being integrated with the Clinical Dashboard is based on the ATHENA-CDS system.  The automation of performance measurement builds on prior work in the VA HSR&D QUERI project “Guidelines to Performance Measures: Automating Quality Review for Heart Failure.”