Population Health Management: Leveraging it for Better Patient Outcome’s

Abstract

Purpose: The primary aim is to investigate how IT integration within PHM frameworks significantly improves patient care by enabling proactive health interventions and fostering a comprehensive understanding of population health trends.
 Methodology: Utilizing a mixed-method approach, the study evaluates various IT tools and technologies ranging from EHRs to AI-driven analytics within PHM contexts. The research methodology includes data analysis through Python’s scikit-learn and pandas libraries, emphasizing model training, evaluation, and application areas in real healthcare settings.
 Findings: Results indicate that IT advancements, particularly EHRs, telehealth, and AI predictive analytics, markedly enhance the effectiveness of PHM. Key outcomes include improved chronic disease management, more effective preventive healthcare measures, and heightened patient engagement. Moreover, IT facilitates optimal resource allocation and operational efficiency within healthcare systems.
 Unique Contribution to Theory, Policy, and Practice: This study contributes to healthcare literature by providing empirical evidence on the transformative role of IT in PHM. It offers a theoretical framework for integrating IT solutions in healthcare strategies, informing policy by highlighting the necessity for interoperability standards, and suggesting practical guidelines for healthcare practitioners to implement technology-driven PHM effectively.