Artificial Intelligence for Preventive Care Coordination: Challenges, Opportunities, and Future Directions

Olha Ostapenko

Citation: Olha Ostapenko, "Artificial Intelligence for Preventive Care Coordination: Challenges, Opportunities, and Future Directions", Universal Library of Medical and Health Sciences, Volume 02, Issue 02.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Preventive medicine in the United States faces persistent structural challenges, including fragmented patient records, low screening adherence, high administrative burden, and care gaps that go unaddressed until patients develop costly acute or chronic conditions. This article examines how artificial intelligence (AI) technologies, specifically machine learning, natural language processing, and automated patient outreach systems, can address these barriers and improve the coordination of preventive care. Drawing on a systematic review of peer-reviewed literature published, the study analyzes AI applications across four functional domains: patient risk stratification, care gap identification, engagement automation, and clinical documentation. The findings show that AI-driven outreach raises screening completion rates by 20 to 38 percent compared with conventional approaches, while ambient documentation tools have recovered tens of thousands of physician hours previously consumed by EHR data entry. The paper proposes a six-module conceptual framework for AI-assisted preventive care coordination and identifies data interoperability, algorithmic bias, and regulatory alignment as conditions that must be addressed for effective adoption. Findings are relevant to health system administrators, policymakers, value-based care organizations, and technology developers engaged in digital health transformation.


Keywords: Artificial Intelligence, Preventive Care, Care Coordination, Care Gap Closure, Machine Learning, Natural Language Processing, Population Health Management, HEDIS, Electronic Health Records, Value-Based Care.

Download doi https://doi.org/10.70315/uloap.ulmhs.2024.0202011