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Patient choice modelling: how do patients choose their hospitals?

Author

Listed:
  • Honora Smith

    (University of Southampton)

  • Christine Currie

    (University of Southampton)

  • Pornpimol Chaiwuttisak

    (University of Southampton)

  • Andreas Kyprianou

    (University of Southampton)

Abstract

As an aid to predicting future hospital admissions, we compare use of the Multinomial Logit and the Utility Maximising Nested Logit models to describe how patients choose their hospitals. The models are fitted to real data from Derbyshire, United Kingdom, which lists the postcodes of more than 200,000 admissions to six different local hospitals. Both elective and emergency admissions are analysed for this mixed urban/rural area. For characteristics that may affect a patient’s choice of hospital, we consider the distance of the patient from the hospital, the number of beds at the hospital and the number of car parking spaces available at the hospital, as well as several statistics publicly available on National Health Service (NHS) websites: an average waiting time, the patient survey score for ward cleanliness, the patient safety score and the inpatient survey score for overall care. The Multinomial Logit model is successfully fitted to the data. Results obtained with the Utility Maximising Nested Logit model show that nesting according to city or town may be invalid for these data; in other words, the choice of hospital does not appear to be preceded by choice of city. In all of the analysis carried out, distance appears to be one of the main influences on a patient’s choice of hospital rather than statistics available on the Internet.

Suggested Citation

  • Honora Smith & Christine Currie & Pornpimol Chaiwuttisak & Andreas Kyprianou, 2018. "Patient choice modelling: how do patients choose their hospitals?," Health Care Management Science, Springer, vol. 21(2), pages 259-268, June.
    • Handle: RePEc:kap:hcarem:v:21:y:2018:i:2:d:10.1007_s10729-017-9399-1
    DOI: 10.1007/s10729-017-9399-1
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    2. Amerigo Ferrari & Chiara Seghieri & Andrea Giannini & Paolo Mannella & Tommaso Simoncini & Milena Vainieri, 2023. "Driving time drives the hospital choice: choice models for pelvic organ prolapse surgery in Italy," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 24(9), pages 1575-1586, December.
    3. Mensen, Anne, 2022. "Concentration of hospital capacities and patients' access to care," Ruhr Economic Papers 952, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    4. Visa Pitkänen & Ismo Linnosmaa, 2021. "Choice, quality and patients’ experience: evidence from a Finnish physiotherapy service," International Journal of Health Economics and Management, Springer, vol. 21(2), pages 229-245, June.
    5. Gao, Ya & Pan, Haixiao & Xie, Zhilin & Habib, Khandker Nurul, 2023. "Understanding patients heterogeneity in healthcare travel and hospital choice - A latent class analysis with covariates," Journal of Transport Geography, Elsevier, vol. 110(C).
    6. Mahua Patra & Satarupa Bandyopadhyay, 2020. "Health seeking behaviour and its determinants in urban areas of developing countries: A primary survey in Kolkata city, India," International Journal of Health Planning and Management, Wiley Blackwell, vol. 35(6), pages 1438-1452, November.
    7. Michael Irlacher & Dieter Pennerstorfer & Anna‐Theresa Renner & Florian Unger, 2023. "Modeling Interregional Patient Mobility: Theory And Evidence From Spatially Explicit Data," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 64(4), pages 1493-1532, November.
    8. Niu, Baozhuang & Xu, Haotao & Dai, Zhipeng, 2022. "Check Only Once? Health Information Exchange between Competing Private Hospitals," Omega, Elsevier, vol. 107(C).

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