Mental Health Clustering Tool Research Articles

Allocation of Users of Mental Health Services to Needs-Based Care Clusters: An Italian Pilot Study

In Italy, despite strong community-based mental health services, needs assessment is unsatisfactory. Using the Mental Health Clustering Tool (MHCT) we adopted a multidimensional and non-diagnosis dependent approach to assign mental health services users with similar needs to groups corresponding to resources required for effective care. We tested the MHCT in nine Departments of Mental Health in four Italian regions. After a brief training, 318 professionals assessed 12,938 cases with a diagnosis of schizophrenia, depression, bipolar disorder and personality disorder through the MHCT. 53% of cases were 40–59 years, half were females, 51% had a diagnosis of schizophrenia, 48% of cases were clinically severe. Clusters included different levels of clinical severity and diagnostic groups. The largest cluster was 11 (ongoing recurrent psychosis), with 18.9% of the sample, followed by cluster 3 (non-psychotic disorders of moderate severity). The MHCT could capture a variety of problems of people with mental disorders beyond the traditional psychiatric assessment, therefore depicting service population from a different standpoint. Following a brief training, MHCT assessment proved to be feasible. The automatic allocation of cases made the attribution to clusters easy and acceptable by professionals. To what extent clustering provide a sound base for care planning will be the matter of further research.

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

BackgroundMental health (MH) care often exhibits uneven quality and poor coordination of physical and MH needs, especially for patients with severe mental disorders. This study tests a Population Health Management (PHM) approach to identify patients with severe mental disorders using administrative health databases in Italy and evaluate, manage and monitor care pathways and costs. A second objective explores the feasibility of changing the payment system from fee-for-service to a value-based system (e.g., increased care integration, bundled payments) to introduce performance measures and guide improvement in outcomes.MethodsSince diagnosis alone may poorly predict condition severity and needs, we conducted a retrospective observational study on a 9,019-patient cohort assessed in 2018 (30.5% of 29,570 patients with SMDs from three Italian regions) using the Mental Health Clustering Tool (MHCT), developed in the United Kingdom, to stratify patients according to severity and needs, providing a basis for payment for episode of care. Patients were linked (blinded) with retrospective (2014–2017) physical and MH databases to map resource use, care pathways, and assess costs globally and by cluster. Two regions (3,525 patients) provided data for generalized linear model regression to explore determinants of cost variation among clusters and regions.ResultsSubstantial heterogeneity was observed in care organization, resource use and costs across and within 3 Italian regions and 20 clusters. Annual mean costs per patient across regions was €3,925, ranging from €3,101 to €6,501 in the three regions. Some 70% of total costs were for MH services and medications, 37% incurred in dedicated mental health facilities, 33% for MH services and medications noted in physical healthcare databases, and 30% for other conditions. Regression analysis showed comorbidities, resident psychiatric services, and consumption noted in physical health databases have considerable impact on total costs.ConclusionsThe current MH care system in Italy lacks evidence of coordination of physical and mental health and matching services to patient needs, with high variation between regions. Using available assessment tools and administrative data, implementation of an episodic approach to funding MH could account for differences in disease phase and physical health for patients with SMDs and introduce performance measurement to improve outcomes and provide oversight.

Survival and community care use by care home residents in England: does mental health matter?

AbstractThe aim was to provide evidence of mortality and community care costs of people living in care homes and to investigate its association with mental health based on the Mental Health Clustering Tool (MHCT). In an observational study, 5,782 residents living in 104 care homes were followed from 2014 to 2016. Residents were categorised into four groups using the MCHT: three with mental health conditions, ‘non-psychotic’, ‘psychotic’ or ‘organic’; and one without mental health conditions, ‘non-clustered’. Generalised estimating equations were used to explore associations between mean community care costs over 6 months per patient and the clustering of residents into the four groups. Differences in survival rates of residents were plotted using Kaplan–Meier curves and tested with the log-rank test and Cox regression analysis. Community care costs were similar among residents with dementia (£431) and without mental health conditions (£407), while costs were higher among residents with non-psychotic (£762) and psychotic (£1,724) mental health conditions. After adjusting for patient and care home characteristics, residents with dementia were 30 per cent less likely to die compared with residents without mental health conditions. Similarly, residents with psychotic conditions and residents with non-psychotic conditions were 25 and 20 per cent less likely to die, respectively, than residents without mental health conditions. The MHCT seems to provide an informative stratification of care home residents with regards to survival and community care use.

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

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.

The utility of novel outcome measures in a naturalistic evaluation of schizophrenia treatment.

BackgroundA number of naturalistic studies have investigated paliperidone palmitate (PP) using proxy measures of effectiveness. An unexplored option is to examine the utility of the mental health clustering tool (MHCT), which is used in UK clinical practice to measure patient well-being and is linked to allocation of resources. This study evaluated the effectiveness of PP using the MHCT, the Health of the Nation Outcome Scales (HoNOS), and, for comparison, more conventional outcome measures.MethodsThis was a naturalistic, 1-year evaluation of PP (n=50) in schizophrenia as well as a comparator antipsychotic drugs group. Changes in the MHCT cluster-score cost ranking and four HoNOS-derived factors were analyzed using a mixed-model statistical analysis to explore the utility of these measures.ResultsAt 1 year, 30 patients (60%) continued PP treatment. The mean “cluster-score cost ranking” (−1.5) and Severe Disturbance factor scores (−1.1) were significantly lower (p-value [adjusted] =0.0003, p-value [adjusted] =0.002, respectively) after 1 year of antipsychotic treatment but no differences were found between PP and the comparator antipsychotic drugs group. Patients prescribed PP were 1.8 times (95% CI 1.1−3.1) more likely to be discharged from hospital than those in the comparator antipsychotic drugs group.ConclusionPP’s continuation rate after 1 year made the study similar to the existing evaluations, and it was possible to prospectively evaluate antipsychotic effectiveness using the novel measures although these did not discriminate between PP and the comparator group. The investigation illustrates that in principle these novel measures are meaningful in naturalistic study designs.

Delaying the implementation of Payment by Results in mental health: the application of standardisation

Purpose – The purpose of this paper is to explore the issues surrounding a long planned expansion of Payment by Results (PbR) into mental health services and to highlight the factors responsible for the delay. Design/methodology/approach – PbR relies upon “standardisation” of conditions and treatments. This depends upon a scheme of classification that can realistically predict resources required to execute treatment of any one case. Plans to fund NHS mental health services on the basis of tariffs derived in this way have been delayed, and a key reason is the lack of high-quality data. This would require effective “standardisation-to the-average” of both a system of classification and a repertoire of costed treatment pathways. This paper investigated the delay implementation by exploring the difficulties in applying standardisation principles to service provision and tariff calculation. Findings – The paper identified the fundamental difficulty with PbR’s implementation in applying “standardisation” to practice. This is defining the mental disorder that the patient is suffering and designing care pathways at clinical level considering the balance between practical applicability and conceptual/constructional validity. This is necessary to enable the calculation of a national tariff. The conceptual flaws of the Health of the Nation Outcome Scale led to the constructional shortcomings which compromised the credibility and validity of Mental Health Clustering Tool regarding making accurate classification in a standardised way. The validity and credibility of calculating a national tariff thus became contentious on the basis of this inaccurate clinical classification system. Originality/value – This paper explored the driving factors of delay in implementing PbR in mental health through connecting the recent reform with the fundamental assumptions of “standardisation-to the-average”, which provided another perspective to illustrate the current obstacles.

Mental health clustering and diagnosis in psychiatric in-patients.

Aims and method This paper investigates the relationship between cluster (Mental Health Clustering Tool, MHCT) and diagnosis in an in-patient population. We analysed the diagnostic make-up of each cluster and the clinical utility of the diagnostic advice in the Department of Health’s Mental Health Clustering Booklet. In-patients discharged from working-age adult and older people’s services of a National Health Service trust over 1 year were included. Cluster on admission was compared with primary diagnosis on discharge.Results Organic, schizophreniform, anxiety disorder and personality disorders aligned to one superclass cluster. Alcohol and substance misuse, and mood disorders distributed evenly across psychosis and non-psychosis superclass clusters. Two-thirds of diagnoses fell within the MHCT ‘likely’ group and a tenth into the ‘unlikely’ group.Clinical implications Cluster and diagnosis are best viewed as complimentary systems to describe an individual’s needs. Improvements are suggested to the MHCT diagnostic advice in in-patient settings. Substance misuse and affective disorders have a more complex distribution between superclass clusters than all other broad diagnostic groups.

Does a cluster always equal a cluster? Geographical variation of cluster populations.

Aims and method To provide information regarding the extent to which the process of clustering using the mental health clustering tool captures the complexity of patient need across different geographical areas. Investigation was undertaken via a ‘deep dive’ into patient notes, with data collected on patients allocated to cluster 5, 8 or 13 in three different London boroughs.Results There is evidence for within-cluster differences between patients in different London boroughs in terms of various complexity factors. Further findings in relation to accuracy of clustering suggest some area-specific patterns in terms of clustering practice, raising the possibility that clinicians have different scoring thresholds in different areas.Clinical implications Complexity factors can affect resource use and therefore cost of service provision. In the case of a national tariff, providers of care to more complex patients may be placed at greater financial risk. It is therefore likely that some form of tariff adjustments will need to be introduced so as not to disadvantage patients and clinicians practising in areas of greater complexity.

Clustering in mental health payment by results: a critical summary for the clinician

SummaryMental health payment by results (PbR) is a disruptive new prospective payment system intended to replace National Health Service block contracts in England and provide a mechanism for opening up the mental health economy. Patients are allocated to one of 21 treatment clusters, each with a different price or tariff. Clinicians perform cluster allocation using the Mental Health Clustering Tool. The clustering process makes demands on clinicians' time even with support from information systems. Clustering is novel and it is unclear how it will work in practice. The process is likely to be susceptible to gaming.LEARNING OBJECTIVESUnderstand that the clinical process of diagnostic classification is different from the financial process of clustering categorisation.Understand the importance of learning the clustering tool ratings definitions in order to make accurate cluster allocations.Recognise that mental health payment by results is driving the widespread adoption of outcomes measurement.

Payment by results for mental health services: economic considerations of case-mix funding

SummaryAgainst the backdrop of a tight financial climate, a new method of funding mental health services is being rolled out in England's National Health Service. Called payment by results (PbR), it represents a fundamental change to the way providers of psychiatric services are paid for care of patients. The Mental Health Clustering Tool has been developed to capture activity which reflects the relative needs of patients, and cluster costs are being collected by service providers. The ultimate goal is the creation of a national tariff or fixed price for each cluster. This article describes the incentives generated by PbR and gives evidence on PbR in acute physical care services where it has been in operation for a decade, with respect to efficiency, quality, volume of activity, administrative costs, upcoding or gaming, equity of provision, and cross-subsidisation. It explores the challenges for mental health services as PbR is introduced.LEARNING OBJECTIVESUnderstand the difference between retrospective and prospective reimbursement systems and the incentives generated by each financing system.Understand the three key building blocks of PbR: currency, reference costs and tariffs.Consider the evidence on PbR in acute physical care and the implications for mental health services.

EPA-1738 – Auditing payment by results- how accurate are we at clustering our patients

The NHS introduced Payment by Results (PbR) as a means of classifying mixed cases treated within a hospital. A specific tariff is applied to each healthcare resource group (the unit of currency used). Payment follows the level of activity, according to the tariff. Care Clusters are a currency developed to support PbR for Mental Health Services. There are 21 clusters based upon patient's characteristics. Care Clusters are assigned using a decision tree of algorithm, based on the Person Score from the Mental Health Clustering Tool (MHCT), undertaken by a care professional involved in the patient's care. A random sample of 30 patients was audited. Patients were allocated care clusters by care professionals at initial assessment. The same patients were re-clustered by Doctor A(Middle Grade) and Doctor B(Consultant Grade). The audit tool was the MHCT. 100% of sample was allocated a care cluster at initial assessment. 70% of cases indicated differences in the care clusters initially allocated. There was a trend towards the Doctor A (middle grade) clustering higher than the care professional. Doctor B (consultant) had a tendency to cluster higher than Doctor B and the care professional. Clustering relies on subjective views of individuals conducting assessments. The more senior the professional, the higher the care cluster. This raises questions about individual assessment of risk/symptomatology and training provided in using MHCT. This could lead to serious consequences financially. Retrain all staff in use of MHCT, consider clustering in MDT settings and recluster cases to ensure accuracy.

Analysis of the Payment by Results Policy for Mental Health in England

Purpose: To analyse the Mental Health Payment by Results policy with an emphasis placed on exploring the perceptions of Providers involved in implementation, to identify any barriers to implementing the policy and provide recommendations to improve implementation. Methods: This report follows the structure of the Walt and Gilson policy analysis framework addressing each area of context, content and processes with a focus on actors who are involved in the implementation process to inform how this policy is being implemented in practice. Results from the primary interview data were triangulated with the literature. The data collected was subsequently analysed and barriers to implementation were identified. Results: The following barriers to implementation were identified: 1) Inadequate time frame 2) Inaccurate data arising from (i) staff not being trained adequately (ii) the Mental Health Clustering Tool (reliability and variability, issues of clarity, difference in thresholds) clustering at the correct times (iii) clustering at the correct times (iii) defining care packages (iv) costing clusters 3) Lack of guidance from central government 4) Insufficient IT systems which do not support the policy 5) Lack of preparation of providers and commissioners 6) Changes in commissioning structure. Conclusion: On the basis of the barriers identified and the literature review, recommendations to policy makers were made to assist them with improving the implementation of Mental Health Payment by Results.

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

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.

The Mental Health Clustering Tool for people with severe intellectual disability

Aims and methodWe assessed 92% (117/127) of the patients in our community mental health learning disability team using the Mental Health Clustering Tool (MHCT) to establish whether their needs could be captured sufficiently well to enable assignment to a care cluster for payment by results in mental health. We explored the characteristics of those assigned to Cluster 0 to identify how they differed from those who could be assigned to Clusters 1-21.ResultsAs expected, nearly half of the case-load (48%) could not be assigned to any cluster except Cluster 0, the variance cluster, which is used when the needs of patients cannot be captured by the current 21 care clusters but a service is, or will be, provided.Clinical implicationsThe MHCT in its current form does not adequately capture the needs of people with more severe intellectual disability. An integrated mental health and learning disability clustering tool is in development. This is expected to include new rating scales and new clusters, however until the development is completed and validated it will not be possible to implement payment by results in mental health in learning disability services.

Mental Health Payment-by-Results Clusters and the Model of Human Occupation Screening Tool

In the United Kingdom, payment for mental health services follows a system of payment-by-results. A mental health clustering tool, classifying individuals into clusters based on service needs, was developed as a broad-based means of planning care packages. It is yet unknown whether payment-by-results clusters will be reflective of the occupational needs of service users. To evaluate the relationship between service users' membership in service need clusters and occupational groupings based on outcomes from an independent measure on participation and engagement in self-care, productivity, and leisure (Model of Human Occupation Screening Tool [MOHOST]), retrospective medical record data were gathered from 675 service users with a range of psychiatric disorders from two organizations in England. Using six subscales of the MOHOST, the two-step cluster analysis identified occupational groupings. Then the multivariate analysis of variance was used to examine whether the mean scores of the six MOHOST subscales were significantly different across the occupational groupings. Finally, participants' membership in the payment-by-results clusters was compared to their membership in the occupational groupings. Participants fell into one of three levels: high-, middle-, or low-functioning occupational groups. These groups did not bear direct relationships with the individual payment-by-results clusters. This suggests that the mental health clustering tool and the MOHOST are likely targeting different characteristics in the service users.

The Development of Care Pathways and Packages in Mental Health Based on the Model of Human Occupation Screening Tool

Purpose: Payment by Results (PbR) was recently introduced to mental health care in England. The system allocates service users to one of 20 clusters and will provide funding based on cluster membership, rather than on block contracts. Occupational therapists are challenged to define care packages for each of the clusters. Method: To facilitate their development, this study identified the occupational profiles of service users in each cluster based on measures offered by the Model of Human Occupation. The study used existing data from the clinical records of 625 service users from two organisations, collected through use of the Mental Health Clustering Tool and the Model of Human Occupation Screening Tool. Results: Across the sample, service users showed greatest problems with habituation, followed by volition and process skills. Qualitatively distinct occupational profiles were identified across the clusters. Service users with non-psychotic problems showed the least interference with occupational participation; those in clusters involving psychosis showed moderate levels of interference; and those in clusters involving cognitive impairment demonstrated the most difficulty with occupational participation. Conclusion: These findings provide an important evidence base for thinking about the occupational needs of service users within the various PbR clusters and the corresponding services that might be offered.