Prospective payment systems and discretionary coding: Evidence from English mental health providers

Giuseppe Moscelli (Lead / Corresponding author), Rowena Jacobs, Nils Gutacker, Maria Jose Aragón, Martin Chalkley, Anne Mason, Jan Boehnke

    Research output: Contribution to journalArticlepeer-review

    4 Citations (Scopus)
    277 Downloads (Pure)


    Reimbursement of English mental health hospitals is moving away from block contracts and towards activity and outcome-based payments. Under the new model, patients are categorised into 20 groups with similar levels of need, called clusters, to which prices may be assigned prospectively. Clinicians, who make clustering decisions, have substantial discretion and can, in principle, directly influence the level of reimbursement the hospital receives. This may create incentives for upcoding. Clinicians are supported in their allocation decision by a clinical clustering algorithm, the Mental Health Clustering Tool, which provides an external reference against which clustering behaviour can be benchmarked. The aims of this study are to investigate the degree of mismatch between predicted and actual clustering and to test whether there are systematic differences amongst providers in their clustering behaviour. We use administrative data for all mental health patients in England who were clustered for the first time during the financial year 2014/15 and estimate multinomial multilevel models of over, under, or matching clustering. Results suggest that hospitals vary systematically in their probability of mismatch but this variation is not consistently associated with observed hospital characteristics.

    Original languageEnglish
    Pages (from-to)387-402
    Number of pages16
    JournalHealth Economics
    Issue number3
    Early online date27 Dec 2018
    Publication statusPublished - 14 Feb 2019


    • mental health
    • hospitals
    • episodic payment
    • classification
    • discretionary behaviour
    • mixed-effects models

    ASJC Scopus subject areas

    • Health Policy


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