ing—identifying system failures, (2) analysis—understanding the factors that contribute to system failures, and (3) system redesign—making improvements in care processes to prevent errors in the future. Patient safety programs should invite the participation of patients and their families and be responsive to their inquiries.

Recommendation 6. The federal government should pursue a robust applied research agenda on patient safety, focused on enhancing knowledge, developing tools, and disseminating results to maximize the impact of patient safety systems. AHRQ should play a lead role in coordinating this research agenda among federal agencies (e.g., the National Library of Medicine) and the private sector. The research agenda should include the following:

  • Knowledge generation

  • High-risk patients—Identify patients at risk for medication errors, nosocomial infections, falls, and other high-risk events.

  • Near-miss incidents—Test the causal continuum assumption (that near misses and adverse events are causally related), develop and test a recovery taxonomy, and extend the current individual human error/recovery models to team-based errors and recoveries.

  • Hazard analysis—Assess the validity and efficiency of integrating retrospective techniques (e.g., incident analysis) with prospective techniques.

  • High-yield activities—Study the cost/benefit of various approaches to patient safety, including analysis of reporting systems for near misses and adverse events.

  • Patient roles—Study the role of patients in the prevention, early detection, and mitigation of harm due to errors.

  • Tool development

  • Early detection capabilities—Develop and evaluate various methods for employing data-driven triggers to detect adverse drug events, nosocomial infections, and other high-risk events (e.g., patient falls, decubitus ulcers, complications of blood product transfusions).

  • Prevention capabilities—Develop and evaluate point-of-care decision support to prevent errors of omission or commission.

  • Data mining techniques—Identify and develop data mining techniques to enhance learning from regional and national patient safety databases. Apply natural language processing techniques to facilitate the extraction of patient safety–related concepts from text documents and incident reports.



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