Incorporating Treatment Outcomes into Quality Measurement of Depression Care

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 DESCRIPTION (provided by applicant): Background: Depression affects an estimated one million Veterans Health Administration (VHA) patients each year and is a leading cause of disability and suicide death. There are several effective treatments for depression, including antidepressant medications and psychotherapy, yet the degree to which these treatments improve depression symptoms in clinical settings depends on the quality of care provided. Current VHA quality measures for depression mostly emphasize care processes, such as the number of days of medication dispensed or the number of psychotherapy sessions attended. However, comprehensive quality measurement should also include assessments of the clinic structures (e.g., staffing) that enable effective care processes and whether the ultimate goal of care-improved patient outcomes-is achieved. Incorporating patient-reported outcomes into quality improvement is a health system priority and has recently been recommended by the Institute of Medicine. Systematically collecting patient-reported outcomes in health care systems such as the VHA is challenging, particularly without burdening providers or introducing biases related to which patients receive follow-up assessments. This study will address these challenges by collecting depression symptom outcomes according to the Patient Health Questionnaire (PHQ-9) using an automated, telephone-based interactive voice response (IVR) system. PHQ-9 data collected across clinics in VISN 11 will be used to develop and test clinic-level outcome quality measures (OQMs). OQMs, after case-mix adjustment for differences in clinic patient populations, will allow determination of the structure and process measures (including a new measure of treatment intensification) associated with outcomes. Findings will enable leaders to identify under-performing clinics and the key aspects of care to address in order to achieve better depression outcomes for patients. Objectives: 1) Develop and assess outcome quality measures for depression from PHQ-9 and case-mix adjustment data collected by an automated IVR system, 2) assess the relationships between outcomes and care processes, including a new measure of treatment intensification, and 3) determine the association between facility characteristics (i.e., structures of care) and depression care processes and outcomes. Methods: This prospective longitudinal study will sample 2,500 VHA patients from 50 primary care and mental health clinics in VISN 11. Included patients will have a new clinical diagnosis of a depressive disorder and a PHQ-9 score ≥ 10. Patient characteristics (including duration of symptoms and socio-demographic factors) at baseline and PHQ-9 scores at 6 weeks, 12 weeks, 26 weeks and one year post-diagnosis will be collected via IVR. IVR data will be merged with health system electronic medical records of comorbid diagnoses, health system encounters, and pharmacy use. Threats to validity of IVR-based OQMs will be assessed by the percentage of enrolled patients who complete a 12-week PHQ-9 (i.e., response rate) and by predictors of call completion (i.e., response bias). Case-mix adjusted multilevel models will be used to determine reliability according to the intraclass correlation coefficient. OQMs will be defined as the clinic-level residuals in these models. Clinic-level residuals indicate an individua clinic's performance in comparison to the expected performance for the average clinic. The validity of current VHA depression care process measures (e.g., 84 days of antidepressant medication supply, 8 psychotherapy visits within 14 weeks) and a new measure of treatment intensification will be assessed by determining their association with depression outcomes at the individual and clinic-level. The association between care structures (e.g., mental health staff to-patient ratios, travel distance to clinic) and outcomes will similarly be examined, and separate models will examine the association between structures and care processes.