Implementation and Outcomes of AI-Enhanced Care Coordination Models for Improving Quality of Life and Reducing Hospital Readmissions in Adult Patients with Multiple Chronic Conditions

Abstract

Multiple chronic conditions (MCC) often face complicated healthcare needs that require the coordination of care among various clinical providers. Fragmented communication and extended decision-making processes are the challenges to conventional care frameworks. The current study evaluated the performance of an AI-enhanced care coordination model that integrates predictive analytics to improve the outcomes of adults with MCC. The participants of the intervention were 100 participants (50 undergoing AI-assisted care and 50 undergoing standard care). Quantitative data were assessed between groups with regard to 30-day readmission, emergency department, medication adherence and quality of life. Findings showed that the AI-assisted group had statistically significantly fewer emergency department visits (p<0.001), medication adherence (p<0.001), and quality of life (p<0.001) compared to the standard-care group. Nevertheless, there were no significant differences in readmission rates in groups (p = 0.177). The qualitative data of health care providers and patients highlighted the role of the system in the provision of early interventions, improving the interaction process, and promoting patient engagement. On the whole, the AI model showed significant potential to enhance care coordination and patient outcomes in patients with MCC; however, additional studies with larger samples and longer-term follow-ups are justified.

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