|dc.description.abstract||Background and Purpose: Quality measurement is an essential, yet, complex component of mental health services that is often limited by a lack of clinically meaningful data across service providers. Understanding how services are organized, delivered, and effective is vital for ensuring and improving health care quality. In quality measurement of mental healthcare, structural indicators are common with fewer process and outcome indicators available. Using data from the RAI - Mental Health (RAI-MH), a comprehensive assessment system mandated for use in Ontario, this dissertation aims to define a set of mental health quality indicators (MHQIs), effectiveness quality indicators (EQIs), and risk adjustment strategy that can be used to evaluate and compare quality at the facility- and regional-levels.
Methodology: The MHQIs were developed using a retrospective analysis of two data sets: A pilot sample of 1,056 RAI-MH admission and discharge assessments collected from 7 inpatient mental health units in Ontario and a sample of 30,046 RAI-MH admission and discharge assessments collected from 70 Ontario hospitals as part of the Canadian Institute for Health Information Ontario Mental Health Reporting System. The MHQIs were chosen based on clinically meaningful domains identified by mental health and quality stakeholders, MHQI rates that were consistently above 5% or below 95% among hospitals, and appropriate variation in rates among hospitals in both sets of data. For each MHQI domain, regression modeling using generalized estimating equations was employed to choose risk adjustment variables and logistic or linear regression was used to perform risk adjustment to compare MHQI and EQI rates among hospitals and regions.
Results: A set of 27 MHQIs was defined measuring improvement and incidence/failure to improve in the following domains: depressive/psychosis/pain symptoms, cognitive/physical/social functioning, aggressive/ disruptive/violent behaviours, and control procedures. Also, 13 EQIs were defined to identify the magnitude of change in MHQI domains per 7 days between assessments. Regression models using generalized estimating equations identified between 1 and 8 risk adjustment covariates for each MHQI. Risk adjustment using logistic and linear regression resulted in over 50% of hospitals and LHINs changing in rank based on MHQI and EQI scores.
Conclusion: This dissertation has developed an evidence-based set of MHQIs and EQIs based on a clinically rich set of data. Since the data is available provincially, the MHQIs and EQIs can be used for hospital based, regional, and public reports on quality of inpatient mental health services. The MHQIs/EQIs can be linked to care planning and funding using the RAI-MH to promote quality improvement and accountability for recipients, providers, managers, governors, and funders of mental health services. Opportunities are also available to extend the use of the MHQIs to community mental health, so that system level evaluations of quality can be developed.||en