A major step in the improvement of health care quality is the development of measures of quality that rely upon routinely collected information about office visits and hospital care. In an effort to improve quality measurement, the Quality and Patient Safety Indicators project evaluates methods for measuring quality by using routinely collected information about hospitalized patients. A comprehensive discharge data set built from a federal-state-private collaboration (Healthcare Cost and Utilization Project) served as the basis for developing these screening indicators for preventable morbidity and mortality. Health care organizations and state officials can use these inexpensive screening indicators as a guide for further data exploration. The Agency for Healthcare Research and Quality is disseminating these indicators to support local quality improvement efforts and fulfill the Congressional mandate for aggregate statistical reporting to monitor quality and patient safety trends over time.
As research and observation has illuminated physicians and the health care system are actually not infallible, providers, policy makers, and consumers are demanding more information about quality of health care - and the lack thereof. The HCUP Quality Indicator project has evaluated methods of detecting potential quality concerns using data that are collected routinely. The methods used to evaluate these "quality indicators" include review of the published literature on the topic, and novel statistical techniques. The resulting indicator set from this project will illuminate potential quality problems that end up contributing to whether or not a patient dies, develops serious preventable complications, or is receiving the best possible care.
This study, performed the UCSF-Stanford Evidence-based Practice Center, consists of three phases. First, the project team will conduct a systematic literature review of quality indicators and risk-adjustment methodologies based on hospital administrative data to provide evidence for refinements to the current AHRQ HCUP indicators. Second, the current and proposed HCUP quality indicators with appropriate risk-adjustment will be empirically tested using HCUP data. Third, the current HCUP software originally developed by AHRQ will be updated to incorporate the new indicators of risk-adjustment methods.