Artificial Intelligence driven Customer Behavior Analytics for Healthcare: A Systematic Review of Patient Experience, Engagement, and Decision-Making Patterns
DOI:
https://doi.org/10.59675/E322Keywords:
Artificial Intelligence, Healthcare Analytics, Patient Experience, Digital Health, Patient EngagementAbstract
The study discussed systematic literature review regarding the usage of Artificial Intelligence (AI) technologies as tools for analyzing, understanding and predicting consumer behavior in healthcare ecosystems, including patterns of patient engagement, service utilization and decision-making.
Design/methodology/approach - A systematic literature review was performed across the major academic databases including PubMed, Scopus, Web of Science and IEEE Xplore, capturing articles published from 1980 to 2024. The literature review provided a synthesis of 87 peer-reviewed articles, which focus on the application of AI for customer behavior analytics in the healthcare context, categorized into five thematic groups: predictive analytics, personalization engines, sentiment analysis, mapping patient journeys, and decision support systems.
Findings - AI technologies, in particular machine learning and natural language processing, are making strides in understanding complex patient behaviors, with predictive accuracy rates exceeding 85% in appointment adherence and compliance with treatment plans. Deep learning models are emerging as particularly useful in identifying patients who are at-risk and personalizing healthcare interventions, which have also resulted in increased patient satisfaction scores and lower healthcare costs.
Research limitations/implications – Although technology has advanced significantly, obstacles remain related to data privacy, bias in algorithms, the transparency of AI models, and integration with current healthcare information systems. This review identifies a research gap in longitudinal studies and promotes further research into ethical frameworks for AI implementation in healthcare contexts.
Practical implications – Healthcare organizations can use patient data and AI-informed behavioral insights to improve patient experiences, better allocate resources, decrease no-show rates, and develop health promotion initiatives targeting specific populations. Administrators and policymakers can utilize the implications presented in this study as actionable frameworks for improvement.
Originality/value – This is the first review to comprehensively synthesize AI technology with healthcare customer behavior, to provide taxonomy of AI technologies, and to present implications for future research as well as substantial contributions to the practice of healthcare delivery.
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