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Digital Home-Monitoring of Patients after Kidney Transplantation: The MACCS Platform

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Published on: April 12, 2021

Augmenting a Transitional Care Model With Artificial Intelligence Decreased Readmissions.

Zenobia Brown¹, Danielle Bergman², Liberty Holt²

¹Northwell Health, Health Solutions Population Health Management, Manhasset, NY, USA.

Journal of the American Medical Directors Association

|April 13, 2023

Summary

This summary is machine-generated.

Augmenting transitional care with artificial intelligence (AI) reduced rehospitalization in older adults by 21%. AI identified high-risk patients, enabling targeted interventions to improve care transitions and decrease hospital readmissions.

Keywords:

Transitional care artificial intelligence readmission

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Area of Science:

Gerontology
Health Services Research
Medical Informatics

Background:

Coordinating patient care across transitions is crucial for effective healthcare delivery.
Reducing hospital readmissions, particularly in older adults, remains a significant challenge in healthcare.

Purpose of the Study:

To evaluate if artificial intelligence (AI) integrated into a transitional care model can decrease rehospitalization rates in older adults.
To assess the impact of AI-driven insights on clinical and social determinants of health data for predicting rehospitalization risk.

Main Methods:

A retrospective case-control study involving adult patients discharged from an integrated health system.
An AI algorithm analyzed clinical, socioeconomic, and behavioral data to predict 30-day rehospitalization risk and provided care recommendations.
Poisson regression was used to compare rehospitalization rates between AI-augmented and standard transitional care groups.

Main Results:

AI identified 29.3% of encounters as medium-high risk for rehospitalization.
The transitional care team implemented 40.2% of AI-generated recommendations for high-risk patients.
Patients receiving AI-augmented care experienced a 21.0% lower adjusted incidence of 30-day rehospitalization compared to controls.

Conclusions:

Augmenting transitional care programs with AI insights significantly reduces rehospitalization in older adults.
AI-driven patient risk stratification and targeted recommendations offer a potentially cost-effective strategy to improve care transitions.
Future research should explore the cost-effectiveness of AI in collaborative healthcare models.