Comparing the performance of English mental health providers in achieving patient outcomes

The BigMove Intervention for People With Physical and Mental Health Conditions: A First Evaluation of Self-Perceived Health, Quality of Life, Coping and Mental and Social Functioning

Background

The BigMove intervention aims to improve the functioning and quality of life of people with physical and mental health conditions via an integrated care approach. This pilot study evaluates the impact of the intervention on self-perceived health (SPH), quality of life (QoL), active coping behaviour, and mental and social functioning.

Methods

Data were analysed from N = 457 participants who had been referred to the intervention by their general practitioner (mean age 48.98 years; 76% female). Three patient-reported and one clinician-rated measures were used: SPH, QoL (MANSA), active coping behaviour (UPCC-ACT), mental and social functioning (HoNOS). Pre- and post-intervention measurements (from 2011 to 2018) were compared using paired-samples t-tests. Due to missing data, analyses were conducted with 205-257 participants per completed outcome. Associations with age and sex were assessed using repeated-measures ANOVA. Clinically relevant change was evaluated with the Edwards-Nunnally index and standard error of measurement (SEM) scores.

Results

Post-intervention, there were statistically significant improvements for all outcomes (p < 0.0001) with moderate to large effect sizes (d = 0.41 to 1.02). The observed changes in outcomes can be considered as clinically relevant improvements.

Conclusion

This pilot study provides preliminary evidence that the intervention has positive effects on SPH, QoL, active coping behaviour, and mental and social functioning.

Effectiveness of treatment for 6813 patients with mental health conditions in Cambridgeshire: a cross-sectional study

BACKGROUND

The Health of the Nation Outcomes Scales (HoNOS) has been widely used as an outcome measure in UK mental health settings for the past decade. The data-set gathered provides a unique opportunity to evaluate the effectiveness of the totality of mental healthcare in 'real-world' conditions; much of our clinical evidence currently comes from highly parameterised clinical trials investigating single interventions in highly selected patients.

AIMS

To examine all outcomes measured by HoNOS for a range of diagnostic groups, evaluate the influence of patient demographics on those outcomes, and observe changes in patient groups over time.

METHOD

Here we show the data from 6813 adult patients treated in Cambridgeshire between 2012 and 2017. Patients were split into three diagnostic groups: psychosis, non-psychosis and organic. Changes in HoNOS scores from initial assessment to discharge were tested and regressions were used to evaluate the influence of age, gender and ethnicity on the changes, as well as to model changes in the severity of initial presenting symptoms with time.

RESULTS

HoNOS scores significantly improve after treatment for psychotic, non-psychotic and organic conditions in adults and older adults. Age, but not gender or ethnicity, influenced change in HoNOS scores. Patients entering secondary mental health services had increased initial HoNOS scores over time.

CONCLUSIONS

The UK repository of HoNOS scores provides a significant and relatively underutilised resource that can be exploited to gain insights into mental illness and treatment effectiveness. This is likely to have many applications, including influencing the commissioning of services.

Measuring and optimising the efficiency of community hospital inpatient care for older people: the MoCHA mixed-methods study

Background

Community hospitals are small hospitals providing local inpatient and outpatient services. National surveys report that inpatient rehabilitation for older people is a core function but there are large differences in key performance measures. We have investigated these variations in community hospital ward performance.

Objectives

(1) To measure the relative performance of community hospital wards (studies 1 and 2); (2) to identify characteristics of community hospital wards that optimise performance (studies 1 and 3); (3) to develop a web-based interactive toolkit that supports operational changes to optimise ward performance (study 4); (4) to investigate the impact of community hospital wards on secondary care use (study 5); and (5) to investigate associations between short-term community (intermediate care) services and secondary care utilisation (study 5).

Methods

Study 1 – we used national data to conduct econometric estimations using stochastic frontier analysis in which a cost function was modelled using significant predictors of community hospital ward costs. Study 2 – a national postal survey was developed to collect data from a larger sample of community hospitals. Study 3 – three ethnographic case studies were performed to provide insight into less tangible aspects of community hospital ward care. Study 4 – a web-based interactive toolkit was developed by integrating the econometrics (study 1) and case study (study 3) findings. Study 5 – regression analyses were conducted using data from the Atlas of Variation Map 61 (rate of emergency admissions to hospital for people aged ≥ 75 years with a length of stay of < 24 hours) and the National Audit of Intermediate Care.

Results

Community hospital ward efficiency is comparable with the NHS acute hospital sector (mean cost efficiency 0.83, range 0.72–0.92). The rank order of community hospital ward efficiencies was distinguished to facilitate learning across the sector. On average, if all community hospital wards were operating in line with the highest cost efficiency, savings of 17% (or £47M per year) could be achieved (price year 2013/14) for our sample of 101 wards. Significant economies of scale were found: a 1% rise in output was associated with an average 0.85% increase in costs. We were unable to obtain a larger community hospital sample because of the low response rate to our national survey. The case studies identified how rehabilitation was delivered through collaborative, interdisciplinary working; interprofessional communication; and meaningful patient and family engagement. We also developed insight into patients’ recovery trajectories and care transitions. The web-based interactive toolkit was established [http://mocha.nhsbenchmarking.nhs.uk/ (accessed 9 September 2019)]. The crisis response team type of intermediate care, but not community hospitals, had a statistically significant negative association with emergency admissions.

Limitations

The econometric analyses were based on cross-sectional data and were also limited by missing data. The low response rate to our national survey means that we cannot extrapolate reliably from our community hospital sample.

Conclusions

The results suggest that significant community hospital ward savings may be realised by improving modifiable performance factors that might be augmented further by economies of scale.

Future work

How less efficient hospitals might reduce costs and sustain quality requires further research.

Funding

This project was funded by the National Institute for Health Research (NIHR) Health Services and Delivery Research programme and will be published in full in Health Services and Delivery Research; Vol. 8, No. 1. See the NIHR Journals Library website for further project information.

Funding approaches for mental health services: Is there still a role for clustering?

Funding for mental health services in England faces many challenges including operating under financial constraints where it is not easy to demonstrate the link between activity and funding. Mental health services need to operate alongside and collaborate with acute hospital services where there is a well-established system for paying for activity. The funding landscape is shifting at a rapid pace and we outline the distinctions between the three main options - block contracts, episodic payment and capitation. Classification of treatment episodes via clustering presents an opportunity to demonstrate activity and reward it within these payment approaches. We have been engaged in research to assess how well the clustering system is performing against a number of fundamental criteria. Clusters need to be reliably recorded, to correspond to health needs, and to treatments that require roughly similar resources. We find that according to these criteria, clusters are falling short of providing a sound basis for measuring and financing services. Yet, we argue, it is the best available option and is essential for a more transparent funding approach for mental health to demonstrate its claim on resources, and that, as such, clusters should be a starting point for evolving a better funding system.