Trends in predictive AI adoption and their implications for human-centric knowledge management in sustainable healthcare, Jacqueline Anena, Rahaman Hasan

Jacqueline Anena
DBA Research Scholar, Rushford Business School
PATH
Uganda
ORCID: 0009-0002-7124-3416
Rahaman Hasan
Canterbury Christ Church University
UK

ORCID: 0000-0003-1690-2458
DOI: 10.47556/B.OUTLOOK2026.24.1

Purpose: The technological advancements related to the adoption of Artificial Intelligence (AI) are at a boon, which makes it necessary to understand the trends and patterns in predictive AI adoption for human-centric knowledge management in sustainable healthcare. This study explores the implications of predictive AI and the dynamic balance between AI-driven intelligence, along with human cognitive capabilities, in enhancing decision-making, operational efficiency, and long-term sustainable healthcare outcomes.
Design: The study is based on secondary data analysis using the report released by the Office of the National Coordinator for Health Information Technology for 2023-2024 in the year 2025. The data briefs on hospital trends in predictive AI adoption, evaluation, and governance. A descriptive, secondary qualitative and interpretive research approach will be adopted to assess adoption patterns, evaluation mechanisms, and governance practices, while linking these trends to human-centric knowledge management and sustainability frameworks. Thematic analysis shall be conducted, characterised by the following steps: data familiarisation, code development, and theme generation. This set of steps is for generating the themes related to predictive AI adoption trends, human–AI knowledge tension, and sustainable hybrid decision systems.
Findings: The findings are likely to indicate that with the help of the ONC report, a steady increase in predictive AI adoption among hospitals, rising from 66% in 2023 to 71% in 2024, with key applications in clinical risk prediction, diagnostic support, and administrative efficiency. While AI significantly enhances accuracy and efficiency, human intelligence remains indispensable for ethical reasoning, contextual interpretation, and patient-centred care. The study also reveals that although hospitals increasingly evaluate AI systems for accuracy and bias, gaps in governance and disparities in adoption across hospital types pose challenges to equitable and sustainable healthcare delivery.
Originality: This study contributes to the existing literature by integrating predictive AI adoption trends with the concept of human-centric knowledge management, offering a novel perspective that bridges technological advancement with human intelligence and sustainability in healthcare.
Implications: It underscores the importance of robust governance frameworks, continuous evaluation, and ethical oversight to ensure sustainable outcomes. The findings provide valuable insights for policymakers, healthcare administrators, and researchers to design human-centred AI systems that promote efficiency, equity, and long-term sustainability in healthcare.
Keywords: Predictive Artificial Intelligence; Healthcare; Sustainable; Governance; Human-Centered