"A catalyst for action": Factors for implementing clinical risk prediction models of infection in home care settings. (1st December 2020)

• Record Type: Journal Article
• Authors: Dowding, Dawn; Russell, David; McDonald, Margaret V; Trifilio, Marygrace; Song, Jiyoun; Brickner, Carlin; Shang, Jingjing
• Is Part Of: Journal of the American Medical Informatics Association. Volume 28:Number 2(2021)
• Page Start: 334
• Page End: 341
• Publication Date: 2020-12-01
• Subjects: decision support model -- clinical risk prediction -- home care services -- infection control -- nursing; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocaa267
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective: The study sought to outline how a clinical risk prediction model for identifying patients at risk of infection is perceived by home care nurses, and to inform how the output of the model could be integrated into a clinical workflow. Materials and Methods: This was a qualitative study using semi-structured interviews with 50 home care nurses. Interviews explored nurses' perceptions of clinical risk prediction models, their experiences using them in practice, and what elements are important for the implementation of a clinical risk prediction model focusing on infection. Interviews were audio-taped and transcribed, with data evaluated using thematic analysis. Results: Two themes were derived from the data: (1) informing nursing practice, which outlined how a clinical risk prediction model could inform nurse clinical judgment and be used to modify their care plan interventions, and (2) operationalizing the score, which summarized how the clinical risk prediction model could be incorporated in home care settings. Discussion: The findings indicate that home care nurses would find a clinical risk prediction model for infection useful, as long as it provided both context around the reasons why a patient was deemed to be at high risk and provided some guidance for action. Conclusions: It is important to evaluate the potential feasibility and acceptability of a clinical risk prediction model, to inform the intervention design and implementation strategy. The results of this study can provide guidance for the development of the clinical risk prediction tool as an intervention for integration in home care settings.