Inside the Green House "Black Box": Opportunities for High-Quality Clinical Decision Making

Health Serv Res. 2016 Feb;51 Suppl 1(Suppl 1):378-97. doi: 10.1111/1475-6773.12427. Epub 2015 Dec 27.

Abstract

Objective: To develop a conceptual model that explained common and divergent care processes in Green House (GH) nursing homes with high and low hospital transfer rates.

Data sources/settings: Eighty-four face-to-face, semistructured interviews were conducted with direct care, professional, and administrative staff with knowledge of care processes in six GH organizations in six states.

Study design/data collection: The qualitative grounded theory method was used for data collection and analysis. Data were analyzed using open, axial, and selective coding. Data collection and analysis occurred iteratively.

Principal findings: Elements of the GH model created significant opportunities to identify, communicate, and respond to early changes in resident condition. Staff in GH homes with lower hospital transfer rates employed care processes that maximized these opportunities. Staff in GH homes with higher transfer rates failed to maximize, or actively undermined, these opportunities.

Conclusions: Variations in how the GH model was implemented across GH homes suggest possible explanations for inconsistencies found in past research on the care outcomes, including hospital transfer rates, in culture change models. The findings further suggest that the details of culture change implementation are important considerations in model replication and policies that create incentives for care improvements.

Keywords: Medical decision making; culture change; long-term care; nursing; nursing homes; qualitative research.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't

MeSH terms

  • Clinical Decision-Making / methods*
  • Grounded Theory
  • Humans
  • Nursing Homes / organization & administration*
  • Nursing Staff / organization & administration
  • Organizational Innovation
  • Patient Readmission*
  • Patient Transfer / statistics & numerical data
  • Qualitative Research