The Role of Data Analytics in Optimizing Hospital Resource Allocation and Decision-making

Edward; Nanny Djaya

doi:10.30872/jtpc.v9i2.313

Authors

Edward Atma Jaya Teaching & Research Hospital, Jakarta Indonesia Author
Nanny Djaya Medical Faculty Public health & Nutrition, Atma Jaya Catholic University, Jakarta Indonesia Author

DOI:

https://doi.org/10.30872/jtpc.v9i2.313

Keywords:

health records, hospitals, resource

Abstract

Hospitals operate within highly complex and resource-constrained environments where decisions regarding beds, workforce, operating rooms, diagnostic services, and medical supplies directly influence patient safety, quality of care, operational efficiency, and financial sustainability. Persistent challenges such as emergency department overcrowding, prolonged waiting times, workforce shortages, and rising healthcare costs indicate systemic inefficiencies in traditional hospital resource allocation practices. Historically, hospital decision-making has relied on retrospective reporting, static staffing ratios, and managerial experience, approaches that are increasingly inadequate for managing real-time variability and interdependencies within modern healthcare systems. The rapid digitalization of healthcare, particularly through the widespread adoption of electronic health records and integrated hospital information systems, has created unprecedented opportunities to apply data analytics to hospital operations. Data analytics enables hospitals to transform large volumes of clinical and operational data into actionable insights that support evidence-based decision-making. This review examines the role of data analytics in optimizing hospital resource allocation and decision-making by synthesizing contemporary evidence across descriptive, diagnostic, predictive, and prescriptive analytics. It explores key analytical methodologies, including machine learning, process mining, simulation modeling, and optimization techniques, and evaluates their application in critical operational domains such as emergency department crowding, inpatient flow, length of stay prediction, workforce planning, and strategic capacity management. In addition, this review critically discusses implementation challenges that limit the real-world impact of hospital analytics, including data quality issues, interoperability barriers, governance gaps, privacy concerns, and algorithmic bias. The findings indicate that data analytics can substantially enhance hospital performance when embedded within clinical workflows and supported by robust organizational governance. However, sustainable benefits require alignment between analytical models, decision-making processes, ethical oversight, and institutional culture.

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