Improving the Science of Continuous Quality Improvement Program and Evaluation

Dates of Program: August 2007 through November 2012

Field of Work: Advancing the science of quality improvement research and evaluation

Problem Synopsis: The health care field has adopted techniques from industry, such as continuous quality improvement (CQI), to improve patient care processes and outcomes. However, CQI in the health care field has come to mean different things to different audiences. Before practitioners and researchers can determine whether a quality improvement initiative has improved health outcomes, information about quality improvement needs to be consistently defined, measured, and reported.

Synopsis of the Work: RWJF awarded nine grants for research projects in three categories:

  • Providing a Framework for the Identification, Classification, and Evaluation of Quality Improvement Initiatives (one project)
  • Developing New Quality Improvement Measures (five projects)
  • Addressing Data Collection and Analysis Methodologies (three projects)

Key Results

  • The project to provide a framework for the identification, classification, and evaluation of quality improvement initiatives developed evidence-based methods for identifying and applying relevant quality criteria to published articles that empirically evaluate CQI literature.

    The projects to develop new quality improvement measures focused on measures that capture key inputs and outcomes of quality improvement efforts, as well as the definition, measurement, and reporting of information about quality improvement implementation and its context. Grantees developed and tested instruments and models that addressed systems thinking, primary care practices’ capacity for change, and the influence of context on the success of quality improvement projects, among other topics.

    The projects addressing data collection and analysis methodologies studied the reliability of a common quality improvement metric (falls per 1,000 patient-days); investigated the shortcomings of industrial quality assurance techniques when applied to health care data; and designed and built software to help health care organizations understand organizational performance during complex organizational changes requiring substantive learning by employees.