The Measurement & Management of Clinical Outcomes in Mental Health

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The following data were extracted for each patient; square parentheses show correspondence to IAPT minimum data standard: Inclusion criteria:. Removal of duplicates from the extracted data based on postcode, date of initial assessment and reference number was performed. These governmental geographical regions are fine resolution and many UK statistics are produced at this level, including IMD, enabling attribution of an estimate of socio-economic deprivation to each record; these were categorized by quintiles: quintile 1, low deprivation; quintiles 2—4, medium deprivation; quintile 5, high deprivation.

Baseline: service running prior to the establishment of the QI collaborative. Week 1 February through to Week 58 April ; 58 weeks. Implementation: service running post-establishment of the QI collaborative. Week 59 April to Week September , 77 weeks. Sustainability: service running post-dis-establishment of the QI collaborative. Week September to Week May , 35 weeks. Two-way ANOVA compares the mean PHQ9 on entry for patients from low, medium and high levels of deprivation during the baseline, implementation and sustainability phases of the QI initiative.

In this case, a p-chart is used, since the measure is a proportion of patients satisfying certain criteria and the denominator is not constant from month to month [ 18 ]. Its use in healthcare has increased over the last 20 years and can be useful in managing change [ 25 ]. These are typically displayed as four lines—one showing the data, one a solid line of central tendency and two dashed control limits.

In each case, the null hypothesis states that the proportion of patients achieving MTR does not differ across categories of deprivation. During the baseline phase, Weeks 1—58 February to March , before the start of the QI initiative, referrals were made with a mean referral of Patients included were referred from unique LSOAs. The percentages of patients not informed of other services or had an unplanned or exit i.

Routine health outcomes measurement - Wikipedia

The average values are plotted in Fig. The average PHQ9 on entry for patients. A higher value represents a greater severity of depression.

The null hypothesis of no difference between the average values as classified by the initiative phase and IMD cannot be rejected. A negative value represents an improvement. However, Fig. A p-chart showing the proportion attaining MTRDEP, at monthly frequency and with variable width limits of expected variation, accounting for differing denominators.

Clinical Outcome Assessment

No rule breaks were observed. The process is considered to be in statistical control. Thus, during each initiative phase there is no evidence for inequity of outcome in this measure. During the implementation phase, the average weekly referral rate increased compared with the baseline phase; which was subsequently sustained.

It is difficult to associate any specific component of the intervention with increases in referrals due to the concurrent delivery of several components; yet taken as a whole, the combined intervention demonstrated an increase in referral. A previous geospatial evaluation demonstrated that a strategy to improve access to the Westminster IAPT service increased access for all patients, especially from more deprived areas with associated higher healthcare needs. Patients entering the service from areas of higher deprivation have a higher average PHQ9 score; this gives credence to an underlying assumption that the severity of CMDs, specifically depression, is positively associated with IMD score, which acts as a proxy for need.

Whilst differences in PHQ9 scores exist for entry, the clinical meaningfulness of this is doubtful. No evidence for inequity of outcome between areas of differing levels of deprivation is observed in these data between the phases. Therefore, even with increased access to the service because of the QI initiative, there is no evidence that the prescribed course of therapies benefited patients from all deprivation categories differentially.

What Are Health Outcome Measures?

The analysis of MTRDEP, demonstrating meaningful changes in depression, found no difference between patients living in areas of low, medium and high deprivation or with the phases of the QI initiative. Whilst there are more dropouts in the sustainability phase, the QI initiative aimed only to increase access; this finding highlights a novel issue that those people now encouraged to join might be less likely to be retained.

Analysis of the point at which dropout occurs over the prescribed course of treatment could be considered, and used to aid retention. In Europe, the odds of people from more disadvantaged backgrounds i.

Measuring Outcomes for Quality and Accountability

There can be significant levels of unmet mental health need in communities, especially in inner cities, where demographics and social factors affect consultation and health-seeking behaviour [ 10 ], requiring health policies and initiatives need to tackle these inequalities at both local and national levels. Physical and mental health has a complex relationship, and so providing care to those with long-term needs can have other benefits not captured by PHQ9 metrics. This analysis provides knowledge of a healthcare system at a population level and highlights the use of routine data in providing decision support and service evaluation.

The importance of using data that are currently available and pertinent to the service should not be overlooked: they provide an opportunity to evaluate with minimal burden to service staff and generate outputs with metrics familiar to providers and commissioners alike [ 19 ].

The use of routine outcome measures

This is particularly beneficial where metrics are available at high temporal and geographical resolutions, allowing processes and outcomes of care to be monitored. The outcomes for those living within areas of low, medium and high deprivation have no evidence of inequity, but there is heterogeneity in outcome at a patient level: within each group, some patients are responding more to treatment.

A patient level model encompassing demographics and finer scale predictors would be required to investigate this heterogeneity. The anxiety component of an MTR metric was not considered: the measure of anxiety GAD7 has not yet had its responsiveness over time directly evidenced as a primary measure in longitudinal studies [ 20 ].

Hence, the depression metric PHQ9 , which has been so evidenced, was used [ 21 ]. A single self-report measure PHQ9 may not capture the full complexity of each case, but as the prescribed therapies have been evidence to affect this measure directly [ 4 , 6 , 21 ] and it is the recorded clinical outcome, this analysis focuses on the depression score. Whilst a previous geographic analysis showed improved equity of access [ 12 ], and here no evidence of differences in outcomes is found at a population level for those completing the prescribed course of therapies, there are differences in the proportions dropping out of the service from high, medium and low deprivation areas.

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Outcome measurement to outcome management: The critical step

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Physical health costs should thus be reviewed in conjunction with mental health costs to capture any offset. Conclusion The proliferation of managed care Medicaid mental health programs requires public accountability to ensure that high quality care is being provided along with cost containment. Although many national initiatives have addressed outcomes measurement, none has focused specifically on outcomes of mental health services for children and adolescents.

This document identifies desirable outcomes and characteristics of service systems for this population and their families. Multiple outcomes domains that take into account the interests of different stakeholders are outlined. Specific, measurable quality indicators are provided, organized by the performance categories of access, appropriateness, effectiveness, and cost. Prevention as a performance category is added because of its particular relevance to children. Five quality indicators within each performance category are recommended for implementing a CQI process.

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Specific quality indicators for children with severe and persistent disorders who also tend to be high-service utilizers are provided. Empirical as well as pragmatic approaches to data collection are recommended and discussed. A glossary of terms, as well as resources and instruments for outcomes measurement, are provided in appendices A and B.


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