Background: The use of both clinical factors and social determinants of health (SDoH) in referral decision-making for case management may improve optimal use of resources and reduce outcome disparities among patients with diabetes.
Objective: This study proposes the development of a data-driven decision-support system incorporating interactions between clinical factors and SDoH into an algorithm for prioritizing who receives case management services. The paper presents a design for prediction validation and preimplementation assessment that uses a mixed methods approach to guide the implementation of the system.
Background: The leaders of health care organizations are grappling with rising expenses and surging demands for health services. In response, they are increasingly embracing artificial intelligence (AI) technologies to improve patient care delivery, alleviate operational burdens, and efficiently improve health care safety and quality.
Objective: In this paper, we map the current literature and synthesize insights on the role of leadership in driving AI transformation within health care organizations.
We investigated nurses' experiences of hospital-acquired pressure injury (PI) prevention in acute care services to better understand how PI prevention may be optimised. We used the Theoretical Domains Framework to systematically identify barriers and enablers to evidence-based preventive practices as required by the International Guideline. This study was one element of a complex capacity building project on PI surveillance and prevention within the acute health service partners of Monash Partners Academic Health Science Centre, an accredited academic health partnership located in Melbourne, Australia.
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