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Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review

Author

Listed:
  • M. M. Malik

    (The University of Melbourne)

  • S. Abdallah

    (Abu Dhabi University)

  • M. Ala’raj

    (Abu Dhabi University)

Abstract

With the widespread use of healthcare information systems commonly known as electronic health records, there is significant scope for improving the way healthcare is delivered by resorting to the power of big data. This has made data mining and predictive analytics an important tool for healthcare decision making. The literature has reported attempts for knowledge discovery from the big data to improve the delivery of healthcare services, however, there appears no attempt for assessing and synthesizing the available information on how the big data phenomenon has contributed to better outcomes for the delivery of healthcare services. This paper aims to achieve this by systematically reviewing the existing body of knowledge to categorize and evaluate the reported studies on healthcare operations and data mining frameworks. The outcome of this study is useful as a reference for the practitioners and as a research platform for the academia.

Suggested Citation

  • M. M. Malik & S. Abdallah & M. Ala’raj, 2018. "Data mining and predictive analytics applications for the delivery of healthcare services: a systematic literature review," Annals of Operations Research, Springer, vol. 270(1), pages 287-312, November.
    • Handle: RePEc:spr:annopr:v:270:y:2018:i:1:d:10.1007_s10479-016-2393-z
    DOI: 10.1007/s10479-016-2393-z
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    2. Roberto Aringhieri & Patrick Hirsch & Marion S. Rauner & Melanie Reuter-Oppermanns & Margit Sommersguter-Reichmann, 2022. "Central European journal of operations research (CJOR) “operations research applied to health services (ORAHS) in Europe: general trends and ORAHS 2020 conference in Vienna, Austria”," Central European Journal of Operations Research, Springer;Slovak Society for Operations Research;Hungarian Operational Research Society;Czech Society for Operations Research;Österr. Gesellschaft für Operations Research (ÖGOR);Slovenian Society Informatika - Section for Operational Research;Croatian Operational Research Society, vol. 30(1), pages 1-18, March.
    3. Menglei Ji & Mohammad Mosaffa & Amir Ardestani-Jaafari & Jinlin Li & Chun Peng, 2024. "Integration of text-mining and telemedicine appointment optimization," Annals of Operations Research, Springer, vol. 341(1), pages 621-645, October.
    4. Basile, L.J. & Carbonara, N. & Panniello, U. & Pellegrino, R., 2024. "The role of big data analytics in improving the quality of healthcare services in the Italian context: The mediating role of risk management," Technovation, Elsevier, vol. 133(C).
    5. Roberto Moro Visconti & Donato Morea, 2019. "Big Data for the Sustainability of Healthcare Project Financing," Sustainability, MDPI, vol. 11(13), pages 1-17, July.
    6. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2021. "The interconnectedness of the economic content in the speeches of the US Presidents," Annals of Operations Research, Springer, vol. 299(1), pages 593-615, April.
    7. Imran Ali & Devika Kannan, 2022. "Mapping research on healthcare operations and supply chain management: a topic modelling-based literature review," Annals of Operations Research, Springer, vol. 315(1), pages 29-55, August.
    8. Roberto Moro Visconti & Donato Morea, 2020. "Healthcare Digitalization and Pay-For-Performance Incentives in Smart Hospital Project Financing," IJERPH, MDPI, vol. 17(7), pages 1-25, March.
    9. Golmohammadi, Davood & Zhao, Lingyu & Dreyfus, David, 2023. "Using machine learning techniques to reduce uncertainty for outpatient appointment scheduling practices in outpatient clinics," Omega, Elsevier, vol. 120(C).
    10. Marina Johnson & Abdullah Albizri & Serhat Simsek, 2022. "Artificial intelligence in healthcare operations to enhance treatment outcomes: a framework to predict lung cancer prognosis," Annals of Operations Research, Springer, vol. 308(1), pages 275-305, January.
    11. Murtaza Nasir & Nichalin Summerfield & Ali Dag & Asil Oztekin, 2020. "A service analytic approach to studying patient no-shows," Service Business, Springer;Pan-Pacific Business Association, vol. 14(2), pages 287-313, June.
    12. Matteo Cinelli & Valerio Ficcadenti & Jessica Riccioni, 2020. "The interconnectedness of the economic content in the speeches of the US Presidents," Papers 2002.07880, arXiv.org.
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