Creating a High-Reliability Health Care System: Improving Performance on Core Processes of Care at Johns Hopkins Medicine.
Acad Med. 2014 Dec 16;
Authors: Pronovost PJ, Armstrong CM, Demski R, Callender T, Winner L, Miller MR, Austin JM, Berenholtz SM, Yang T, Peterson RR, Reitz JA, Bennett RG, Broccolino VA, Davis RO, Gragnolati BA, Green GE, Rothman PB
In this article, the authors describe an initiative that established an infrastructure to manage quality and safety efforts throughout a complex health care system and that improved performance on core measures for acute myocardial infarction, heart failure, pneumonia, surgical care, and children's asthma. The Johns Hopkins Medicine Board of Trustees created a governance structure to establish health care system-wide oversight and hospital accountability for quality and safety efforts throughout Johns Hopkins Medicine. The Armstrong Institute for Patient Safety and Quality was formed; institute leaders used a conceptual model nested in a fractal infrastructure to implement this initiative to improve performance at two academic medical centers and three community hospitals, starting in March 2012. The initiative aimed to achieve ≥ 96% compliance on seven inpatient process-of-care core measures and meet the requirements for the Delmarva Foundation and Joint Commission awards. The primary outcome measure was the percentage of patients at each hospital who received the recommended process of care. The authors compared health system and hospital performance before (2011) and after (2012, 2013) the initiative. The health system achieved ≥ 96% compliance on six of the seven targeted measures by 2013. Of the five hospitals, four received the Delmarva Foundation award and two received the Joint Commission award in 2013. The authors argue that, to improve quality and safety, health care systems should establish a system-wide governance structure and accountability process. They also should define and communicate goals and measures and build an infrastructure to support peer learning.
PMID: 25517699 [PubMed - as supplied by publisher]