Eshra, Sanjida Alam and Akhir, Al (2024) Business Analytics for Optimizing Oncology Service Capacity, Resource Utilization, Patient Flow, and Care Delivery Outcomes. American Journal of Economics and Business Management, 7 (3). ISSN 2576-5973

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Abstract

Background: Oncology services face increasing demand, limited treatment capacity, workforce pressures and complex patient pathways. Business analytics may support more informed decisions regarding capacity, resource allocation, patient flow and care delivery, but its applications and reported effects remain dispersed across different oncology settings. Aim: This review synthesizes the applications, operational value and limitations of business analytics in oncology service management. Methods: An integrative review was conducted using studies published between January 2015 and December 2023. Scopus, Web of Science, PubMed, Embase and IEEE Xplore were searched for peer-reviewed English-language studies addressing forecasting, machine learning, simulation, optimization, dashboards or process mining in oncology services. Eligible evidence was organized around analytics types, data sources, service-capacity optimization, resource utilization, patient flow and care-delivery outcomes. A narrative synthesis was used because methodological and outcome heterogeneity prevented meaningful statistical pooling. Results: The reviewed evidence indicates that descriptive, diagnostic, predictive and prescriptive analytics can improve operational visibility and support demand forecasting, appointment scheduling, workforce planning, equipment allocation and pathway monitoring. Simulation and optimization were frequently applied to chemotherapy and radiotherapy capacity decisions, while predictive modelling and process mining supported demand estimation and bottleneck identification. However, reported benefits were context-dependent, and evidence linking operational improvements to clinical outcomes remained limited. Data incompleteness, weak interoperability, insufficient external validation, model interpretability, privacy, bias and implementation barriers reduced transferability. Conclusion: Business analytics may support oncology service management, but effective use requires reliable data, transparent validation, clinician involvement, and evaluation of safety, efficiency, experience, and equity.

Item Type:	Article
Uncontrolled Keywords:	Business analytics, oncology services, capacity optimization, resource utilization, patient flow
Subjects:	H Social Sciences
Depositing User:	admin eprints
Date Deposited:	01 Sep 2026 16:36
Last Modified:	01 Sep 2026 16:36
URI:	http://eprints.umsida.ac.id/id/eprint/17027

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