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Process mining to discover patterns in patient outcomes in a Psychological Therapies Service

Author

Listed:
  • C. Potts

    (Ulster University)

  • R. R. Bond

    (Ulster University)

  • J-A. Jordan

    (Northern Health and Social Care Trust)

  • M. D. Mulvenna

    (Ulster University)

  • K. Dyer

    (Northern Health and Social Care Trust
    Northern Health and Social Care Trust)

  • A. Moorhead

    (Ulster University)

  • A. Elliott

    (Northern Health and Social Care Trust
    Northern Health and Social Care Trust)

Abstract

In the mental health sector, Psychological Therapies face numerous challenges including ambiguities over the client and service factors that are linked to unfavourable outcomes. Better understanding of these factors can contribute to effective and efficient use of resources within the Service. In this study, process mining was applied to data from the Northern Health and Social Care Trust Psychological Therapies Service (NHSCT PTS). The aim was to explore how psychological distress severity pre-therapy and attendance factors relate to outcomes and how clinicians can use that information to improve the service. Data included therapy episodes (N = 2,933) from the NHSCT PTS for adults with a range of mental health difficulties. Data were analysed using Define-Measure-Analyse model with process mining. Results found that around 11% of clients had pre-therapy psychological distress scores below the clinical cut-off and thus these individuals were unlikely to significantly improve. Clients with fewer cancelled or missed appointments were more likely to significantly improve post-therapy. Pre-therapy psychological distress scores could be a useful factor to consider at assessment for estimating therapy duration, as those with higher scores typically require more sessions. This study concludes that process mining is useful in health services such as NHSCT PTS to provide information to inform caseload planning, service management and resource allocation, with the potential to improve client’s health outcomes.

Suggested Citation

  • C. Potts & R. R. Bond & J-A. Jordan & M. D. Mulvenna & K. Dyer & A. Moorhead & A. Elliott, 2023. "Process mining to discover patterns in patient outcomes in a Psychological Therapies Service," Health Care Management Science, Springer, vol. 26(3), pages 461-476, September.
    • Handle: RePEc:kap:hcarem:v:26:y:2023:i:3:d:10.1007_s10729-023-09641-8
    DOI: 10.1007/s10729-023-09641-8
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    References listed on IDEAS

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    1. Tom C. Russ & Eva Woelbert & Katrina A. S. Davis & Jonathan D. Hafferty & Zina Ibrahim & Becky Inkster & Ann John & William Lee & Margaret Maxwell & Andrew M. McIntosh & Rob Stewart, 2019. "How data science can advance mental health research," Nature Human Behaviour, Nature, vol. 3(1), pages 24-32, January.
    2. Davide Duma & Roberto Aringhieri, 2020. "An ad hoc process mining approach to discover patient paths of an Emergency Department," Flexible Services and Manufacturing Journal, Springer, vol. 32(1), pages 6-34, March.
    3. Ines Verena Arnolds & Daniel Gartner, 2018. "Improving hospital layout planning through clinical pathway mining," Annals of Operations Research, Springer, vol. 263(1), pages 453-477, April.
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