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A data-driven Bayesian belief network model for exploring patient experience drivers in healthcare sector

Author

Listed:
  • Arwa Al Nuairi

    (Khalifa University of Science and Technology)

  • Mecit Can Emre Simsekler

    (Khalifa University of Science and Technology)

  • Abroon Qazi

    (American University of Sharjah)

  • Andrei Sleptchenko

    (Khalifa University of Science and Technology)

Abstract

Patient experience is a key quality indicator driven by various patient- and provider-related factors in healthcare systems. While several studies provided different insights on patient experience factors, limited research investigates the interdependencies between provider-related factors and patient experience. This study aims to develop a data-driven Bayesian belief network (BBN) model that explores the role and relative importance of provider-related factors influencing patient experience. A BBN model was developed using structural learning algorithms such as tree augmented Naïve Bayes. We used hospital-level aggregated survey data from the British National Health Service to explore the impact of eight provider-related factors on overall patient experience. Moreover, sensitivity and scenario-based analyses were performed on the model. Our results showed that the most influential factors that lead to a high patient experience score are: (1) confidence and trust, (2) respect for patient-centered values, preferences, and expressed needs, and (3) emotional support. Further sensitivity and scenario analyses provided significant insights into the effect of different hypothetical interventions and how the patient experience is affected. The study findings can help healthcare managers utilize and allocate their resources more effectively to improve the overall patient experience in healthcare systems.

Suggested Citation

  • Arwa Al Nuairi & Mecit Can Emre Simsekler & Abroon Qazi & Andrei Sleptchenko, 2024. "A data-driven Bayesian belief network model for exploring patient experience drivers in healthcare sector," Annals of Operations Research, Springer, vol. 342(3), pages 1797-1817, November.
    • Handle: RePEc:spr:annopr:v:342:y:2024:i:3:d:10.1007_s10479-023-05437-9
    DOI: 10.1007/s10479-023-05437-9
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    References listed on IDEAS

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    1. Marcot, Bruce G., 2012. "Metrics for evaluating performance and uncertainty of Bayesian network models," Ecological Modelling, Elsevier, vol. 230(C), pages 50-62.
    2. Beata Gavurova & Jan Dvorsky & Boris Popesko, 2021. "Patient Satisfaction Determinants of Inpatient Healthcare," IJERPH, MDPI, vol. 18(21), pages 1-18, October.
    3. Bruce G. Marcot & Anca M. Hanea, 2021. "What is an optimal value of k in k-fold cross-validation in discrete Bayesian network analysis?," Computational Statistics, Springer, vol. 36(3), pages 2009-2031, September.
    4. Kate Honeyford & Felix Greaves & Paul Aylin & Alex Bottle, 2017. "Secondary analysis of hospital patient experience scores across England’s National Health Service – How much has improved since 2005?," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-11, October.
    5. Delen, Dursun & Topuz, Kazim & Eryarsoy, Enes, 2020. "Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition," European Journal of Operational Research, Elsevier, vol. 281(3), pages 575-587.
    6. Hekkert, Karin Dorieke & Cihangir, Sezgin & Kleefstra, Sophia Martine & van den Berg, Bernard & Kool, Rudolf Bertijn, 2009. "Patient satisfaction revisited: A multilevel approach," Social Science & Medicine, Elsevier, vol. 69(1), pages 68-75, July.
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