ArticleHealth economics2019
Prospective payment systems and discretionary coding-Evidence from English mental health providers.
Article in Health economics, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Opaque ontology: neuroimaging classification of ICD-10 diagnostic groups in the UK Biobank.GigaScience · 2025Article
- Allocation of Users of Mental Health Services to Needs-Based Care Clusters: An Italian Pilot Study.Community mental health journal · 2024Article
- Using big data and Population Health Management to assess care and costs for patients with severe mental disorders and move toward a value-based payment system.BMC health services research · 2023Observational
- Use of past care markers in risk-adjustment: accounting for systematic differences across providers.The European journal of health economics : HEPAC : health economics in prevention and care · 2022Article
- The impact of extending nurse working hours on staff sickness absence: Evidence from a large mental health hospital in England.International journal of nursing studies · 2020Article
- Measuring the Activity of Mental Health Services in England: Variation in Categorising Activity for Payment Purposes.Administration and policy in mental health · 2019Article
- Prospective payment systems and discretionary coding-Evidence from English mental health providers.Health economics · 2019Article
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Authors and funding
7 authors.
Funding
Abstract
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.
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