Advancing healthcare analytics: a thematic review of machine learning, health informatics, and real-world data applications

Abstract

Objective: To map the conceptual and methodological landscape of healthcare analytics by identifying dominant thematic clusters, synthesizing key trends, and outlining translational challenges and research opportunities in the field.

Methods: A total of 2,281 Scopus-indexed publications were analyzed using unsupervised text mining and clustering techniques. The analysis focused on identifying recurring themes, methodological innovations, and gaps within healthcare analytics literature across clinical, administrative, and public health contexts.

Results: Eight dominant themes were identified: intelligent systems for predictive healthcare, patient-centered health analytics, adaptive AI for clinical insights, demographic health analytics, digital mental health surveillance, ethical analytics for health surveillance, personalized care through data analytics, and AI-driven insights for outbreak response. These reflect a transition toward real-time, multimodal, and ethically grounded analytics ecosystems. Persistent challenges include data interoperability, algorithmic opacity, standardization of evaluation, and demographic bias.

Conclusions: The review highlights emerging priorities, including explainable AI, federated learning, and context-aware modeling, as well as ethical considerations related to data privacy and digital equity. Practical recommendations include co-designing with healthcare professionals, investing in infrastructure, and deploying real-time clinical decision support. Healthcare analytics is positioned as a foundational pillar of learning health systems with broad implications for translational research and precision health.

Keywords: Clinical decision support; Digital health; Health informatics; Healthcare analytics; Medical informatics; mHealth.