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Using Visual and Narrative Methods to Achieve Fair Process in Clinical Care
Published on: February 16, 2011
Quality improvement implementation and disparities: the case of the health disparities collaboratives
1Section of General Internal Medicine, Department of Medicine, University of Chicago, Chicago, IL 60637, USA. mchin@medicine.bsd.uchicago.edu
Background:
The Health Disparities Collaboratives (HDCs), a quality improvement (QI) collaborative incorporating rapid QI, a chronic care model, and learning sessions, have been implemented in over 900 community health centers across the country.
Objectives:
To determine the HDC's effect on clinical processes and outcomes, their financial impact, and factors important for successful implementation.
Research Design:
Systematic review of the literature.
Results:
The HDCs improve clinical processes of care over short-term period of 1 to 2 years, and clinical processes and outcomes over longer period of 2 to 4 years. Most participants perceive that the HDCs are successful and worth the effort. Analysis of the Diabetes Collaborative reveals that it is societally cost-effective, with an incremental cost-effectiveness ratio of $33,386 per quality-adjusted life year, but that consistent revenue streams for the initiative do not exist. Common barriers to improvement include lack of resources, time, and staff burnout. Highest ranked priorities for more funding are money for direct patient services, data entry, and staff time for QI. Other common requests for more assistance are help with patient self-management, information systems, and getting providers to follow guidelines. Relatively low-cost ways to increase staff morale and prevent burnout include personal recognition, skills development opportunities, and fair distribution of work.
Conclusions:
The HDCs have successfully improved quality of care, and the Diabetes Collaborative is societally cost-effective, but policy reforms are necessary to create a sustainable business case for these health centers that serve many uninsured and underinsured populations.
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