The value of data and analytics for health care providers
TL;DR
The study concludes that the economic value of healthcare data extends beyond technology adoption and depends on effective data governance, organizational capabilities, and the integration of analytics into both clinical and managerial decision-making processes.
Abstract
The increasing digitization of healthcare has transformed data into a strategic asset for healthcare providers, enabling improvements in clinical decision-making, operational efficiency, and organizational performance. Advances in health information technologies have facilitated the collection, integration, and analysis of large volumes of clinical and administrative data, creating opportunities to enhance healthcare quality while reducing costs. This study investigates the economic value of data and analytics for healthcare providers through a hermeneutic systematic literature review. The review synthesizes existing research on five major domains of data-driven healthcare technologies: Electronic Health Records (EHRs), Computerized Clinical Decision Support Systems (CDSS), advanced analytics, business analytics, and telemedicine. The objective is to identify the mechanisms through which these technologies create value and to examine the factors influencing their successful implementation. The findings reveal that data and analytics generate value by improving diagnostic accuracy, supporting evidence-based clinical decisions, optimizing resource allocation, enhancing operational workflows, reducing medical errors, and strengthening financial and strategic management. EHRs serve as the foundation for data integration and interoperability, while CDSS and advanced analytics enable predictive modeling, personalized treatment recommendations, and proactive patient management. Business analytics contributes to organizational planning, performance measurement, and cost optimization, whereas telemedicine enhances healthcare accessibility, continuity of care, and patient engagement. Despite these benefits, the review identifies several barriers to value realization, including data quality issues, interoperability limitations, high implementation and maintenance costs, privacy and cybersecurity concerns, resistance to organizational change, and shortages of analytical expertise. The study concludes that the economic value of healthcare data extends beyond technology adoption and depends on effective data governance, organizational capabilities, and the integration of analytics into both clinical and managerial decision-making processes. By synthesizing the current body of knowledge, this review provides a comprehensive framework for understanding how data and analytics contribute to value creation in healthcare and offers practical implications for healthcare providers, policymakers, and researchers seeking to maximize the return on digital health investments and support the transition toward more efficient, data-driven healthcare systems.