Emerging Models of Care Using IT in Long-Term/Post-Acute Care: A Comparative Analysis of Human and AI-Driven Qualitative Insights.

Abstract

Purpose: As the global population ages, long-term/post-acute care (LTPAC) systems face challenges in ensuring quality care for older adults with complex medical needs. Using health information technology (IT) is a promising strategy to address these challenges. However, evidence gaps remain regarding barriers and facilitators to technology integration in LTPAC. Thus, the current study explored barriers and facilitators to technology adoption in emerging models of care for older adults through the International Summit on Innovation and Technology for the Care of Older People (IS-ITCOP).

Method: The IS-ITCOP Summit, held in June 2024, brought together 47 interdisciplinary experts from eight countries. Qualitative data were collected via facilitated discussion groups and analyzed using two approaches: human-coded thematic analysis and ChatGPT 4.0-driven analysis.

Results: Shared themes included technology barriers, ethical considerations, workforce challenges, and patient-centered care. Human analysis emphasized abstract themes, whereas ChatGPT provided granular insights on emerging technologies.

Conclusion: Combining human and artificial intelligence-driven analyses enriched understanding, highlighting opportunities and challenges for integrating IT into LTPAC systems. [Journal of Gerontological Nursing, 51(4), 6-11.].