Abstract
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.
Highlights
There was a significant mismatch between ICD-10 codes, Health of the Nation Outcome Scales (HoNOS) and Mental Health Clustering Tool (MHCT) scores and allocated care cluster, with differences between services and localities
The ‘deep dive’ analysis indicated that most mismatches occurred because psychosis was allocated to a non-psychotic cluster and vice versa, and as a result of inherent weaknesses of the MHCT
A report by the Sainsbury Centre for Mental Health raised concerns about the challenges that payment by results (PbR) presented in mental health, as this approach is characterised by long-term and episodic conditions, variability of services and the cost of care is influenced by a multitude of factors beyond diagnosis.[6]
Summary
The largest group of mismatches between ICD-10 code and cluster (Fig. 4) was due to the fact that some cases were allocated to a ‘non-psychotic’ cluster (34%) despite the presence of psychosis in recorded diagnoses. This was not justifiable on any clinical grounds for example, the cluster included major psychosis such as schizophrenia (16.4%) and bipolar disorder (12%), for which the patients were receiving highly complex care packages under the care programme approach. The second largest group of mismatches was due to cases being allocated to a ‘psychotic’ cluster despite having no psychotic condition recorded (16.7%). 8% of mismatched cases were deemed to be as a result of errors in the algorithm and the MHCT (Fig. 5)
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