Combining routine outcomes measurement and ‘Payment by Results’:
                        will it work and is it worth it?

Macdonald, Alastair; Elphick, Martin

doi:10.1192/bjp.bp.110.090993

Cited by 24 publications

(31 citation statements)

References 5 publications

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“…The MHCT assesses the domains of behaviour, symptoms, impairment, social functioning and risk factors, and is used to assign patients to one of 21 clusters, falling under one of three broad 'super-classes' (non-psychotic, psychotic and organic). 7 One of the main reasons for not using diagnostic information for clustering in Mental Health Payment by Results was that mental disorder diagnosis was shown to be a poor predictor of HSU in studies involving national and multisite trial data sets. [8][9][10][11] On the other hand, it has been argued that although mental disorder diagnosis alone is not sufficient for clustering purposes, information about broad diagnoses and care pathways can be combined, in a simple and practical manner, to form reliable clusters with homogenous resource patterns.…”

Section: Introductionmentioning

confidence: 99%

A Systematic Review of the Predictors of Health Service Utilisation by Adults with Mental Disorders in the UK

Twomey

Baldwin

Hopfe

et al. 2015

European Psychiatry

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Objectives: To identify variables that predict health service utilisation (HSU) by adults with mental disorders in the UK, and to determine the evidence level for these predictors.Design: A narrative synthesis of peer-reviewed studies published after the year 2000. The search was conducted using four databases (ie, PsycINFO, CINAHL Plus with full text, MEDLINE and EMBASE) and completed on 25 March 2014. Setting:The majority of included studies were set in health services across primary, secondary, specialist and inpatient care. Some studies used data from household and postal surveys.Participants: Included were UK-based studies that predicted HSU by adults with mental disorders. Participants had a range of mental disorders including psychotic disorders, personality disorders, depression, anxiety disorders, eating disorders and dementia.Primary outcome: A wide range of HSU outcomes were examined, including general practitioner (GP) contacts, medication usage, psychiatrist contacts, psychotherapy attendances, inpatient days, accident and emergency admissions and 'total HSU'.Results: Taking into account study quality, 28 studies identified a range of variables with good preliminary evidence supporting their ability to predict HSU. Of these variables, comorbidity, personality disorder, age (heterogeneous age ranges), neurotic symptoms, female gender, a marital status of divorced, separated or widowed, non-white ethnicity, high previous HSU and activities of daily living, were associated with increased HSU. Moreover, good preliminary evidence was found for associations of accessing a primary care psychological treatment service and medication use with decreased HSU.

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Section: Introductionmentioning

confidence: 99%

A Systematic Review of the Predictors of Health Service Utilisation by Adults with Mental Disorders in the UK

Twomey

Baldwin

Hopfe

et al. 2015

European Psychiatry

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“…2,15 Nevertheless, PbR remains an opportunity to have more transparent commissioning and potentially can act as a strong incentive for the routine recording of clinical outcomes, and may encourage clinical involvement in financial management. 16 …”

Section: Discussionmentioning

confidence: 99%

Payment by results: validating care cluster allocation in the real world

Bekas

Michev

2013

Psychiatrist

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Aims and methodTo validate care cluster allocation for payment by results (PbR) in mental health and to evaluate clustering and auditing methodologies. We applied exclusion criteria to the patient population of a mental health trust. An automated validation compared cluster with expected ICD-10 codes or scores on the Health of the Nation Outcome Scales (HoNOS) and Mental Health Clustering Tool (MHCT). Six hundred ‘mismatched’ cases were reviewed in depth to better understand the reasons why these cases appeared misclustered.ResultsThere was a significant mismatch between ICD-10 codes, HoNOS and MHCT scores and allocated care cluster, with differences between services and localities. Some clusters appeared to be more accurately allocated. The ‘deep dive’ analysis indicated that most mismatches occurred because psychosis was allocated to a non-psychotic cluster and vice versa, but also as a result of inherent weaknesses of the MHCT.Clinical implicationsHigh levels of inappropriate care cluster allocation highlight the need to improve practice. Weaknesses in the MHCT and ICD-10 coding mean that the final arbiter should be clinical judgement. Auditing will, by necessity, have a significant margin of error.

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“…Perhaps taking into account the lack of evidence for the utility of diagnostically-defined clusters in predicting costs, and the subsequent exclusion of mental health services from the original Medicare system of the United States,[7–11] NHS policy-makers have pursued a ‘multi-domain’ approach to clustering. Patients are assigned to mental health clusters using the Health of the Nation Outcome Scales (HoNOS)[12] and additional risk-based items.…”

Section: Introductionmentioning

confidence: 99%

Utility of the Health of the Nation Outcome Scales (HoNOS) in Predicting Mental Health Service Costs for Patients with Common Mental Health Problems: Historical Cohort Study

et al. 2016

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BackgroundFew countries have made much progress in implementing transparent and efficient systems for the allocation of mental health care resources. In England there are ongoing efforts by the National Health Service (NHS) to develop mental health ‘payment by results’ (PbR). The system depends on the ability of patient ‘clusters’ derived from the Health of the Nation Outcome Scales (HoNOS) to predict costs. We therefore investigated the associations of individual HoNOS items and the Total HoNOS score at baseline with mental health service costs at one year follow-up.MethodsAn historical cohort study using secondary care patient records from the UK financial year 2012–2013. Included were 1,343 patients with ‘common mental health problems’, represented by ICD-10 disorders between F32-48. Costs were based on patient contacts with community-based and hospital-based mental health services. The costs outcome was transformed into ‘high costs’ vs ‘regular costs’ in main analyses.ResultsAfter adjustment for covariates, 11 HoNOS items were not associated with costs. The exception was ‘self-injury’ with an odds ratio of 1.41 (95% CI 1.10–2.99). Population attributable fractions (PAFs) for the contribution of HoNOS items to high costs ranged from 0.6% (physical illness) to 22.4% (self-injury). After adjustment, the Total HoNOS score was not associated with costs (OR 1.03, 95% CI 0.99–1.07). However, the PAF (33.3%) demonstrated that it might account for a modest proportion of the incidence of high costs.ConclusionsOur findings provide limited support for the utility of the self-injury item and Total HoNOS score in predicting costs. However, the absence of associations for the remaining HoNOS items indicates that current PbR clusters have minimal ability to predict costs, so potentially contributing to a misallocation of NHS resources across England. The findings may inform the development of mental health payment systems internationally, especially since the vast majority of countries have not progressed past the early stages of this development. Discrepancies between our findings with those from Australia and New Zealand point to the need for further international investigations.

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Combining routine outcomes measurement and ‘Payment by Results’: will it work and is it worth it?

Abstract: The Department of Health in England has long encouraged the routine measurement of clinical outcomes in mental health services but has now decided to use outcome measures as part of a new payments system - Payment by Results. We examine how these two policies should or might interact.

Cited by 24 publications

References 5 publications

A Systematic Review of the Predictors of Health Service Utilisation by Adults with Mental Disorders in the UK

A Systematic Review of the Predictors of Health Service Utilisation by Adults with Mental Disorders in the UK

Payment by results: validating care cluster allocation in the real world

Utility of the Health of the Nation Outcome Scales (HoNOS) in Predicting Mental Health Service Costs for Patients with Common Mental Health Problems: Historical Cohort Study

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