Enhancing Mental Health Care by Scientifically Matching Patients to Providers' Strengths
Research has shown that mental health care (MHC) providers differ significantly in their ability to help patients. In addition, providers demonstrate different patterns of effectiveness across symptom and functioning domains. For example, some providers are reliably effective in treating numerous patients and problem domains, others are reliably effective in some domains (e.g., depression, substance abuse) yet appear to struggle in others (e.g., anxiety, social functioning), and some are reliably ineffective, or even harmful, across patients and domains. Knowledge of these provider differences is based largely on patient-reported outcomes collected in routine MHC settings.
Unfortunately, provider performance information is not systematically used to refer or assign a particular patient to a scientifically based best-matched provider. MHC systems continue to rely on random or purely pragmatic case assignment and referral, which significantly "waters down" the odds of a patient being assigned/referred to a high performing provider in the patient's area(s) of need, and increases the risk of being assigned/referred to a provider who may have a track record of ineffectiveness. This research aims to solve the existing non-patient-centered provider-matching problem.
Specifically, the investigators aim to demonstrate the comparative effectiveness of a scientifically-based patient-provider match system compared to status quo pragmatic case assignment. The investigators expect in the scientific match group significantly better treatment outcomes (e.g., symptoms, quality of life) and higher patient satisfaction with treatment. The investigators also expect to demonstrate feasibility of implementing a scientific match process in a community MHC system and broad dissemination of the easily replicated scientific match technology in diverse health care settings. The importance of this work for patients cannot be understated. Far too many patients struggle to find the right provider, which unnecessarily prolongs suffering and promotes health care system inefficiency. A scientific match system based on routine outcome data uses patient-generated information to direct this patient to this provider in this setting. In addition, when based on multidimensional assessment, it allows a wide variety of patient-centered outcomes to be represented (e.g., symptom domains, functioning domains, quality of life).
|Study Design:||Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Double (Participant, Care Provider)
Primary Purpose: Health Services Research
|Official Title:||Enhancing Mental Health Care by Scientifically Matching Patients to Providers' Strengths|
- Treatment Outcome Package (TOP; Kraus, Seligman, & Jordan, 2005) [ Time Frame: Up to 36 months ]The TOP-CS consists of 58 items assessing 12 symptom and functional (including strengths) domains (risk-adjusted for case mix variables assessed via 37 items on the companion TOP-CM, such as divorce, job loss, comorbidity): work functioning, sexual functioning, social conflict, depression, panic (somatic anxiety), psychosis, suicidal ideation, violence, mania, sleep, substance abuse, and quality of life. Global symptom severity is assessed by summing all items or by averaging the z-scores (i.e., standard deviation units relative to the general population mean) across each of the 12 clinical scales. Domain-specific symptom severity is quantified as the individual z-scores for each clinical scale using general population means and standard deviations for the conversion.
- Symptom Checklist-10 (SCL-10; Rosen, Drescher, Moos, & Gusman, 1999) [ Time Frame: Up to 36 months ]
- Existential Isolation Scale (EIS; Pinel et al., 2014). [ Time Frame: Up to 36 months ]
- Inventory of Interpersonal Problems-32 (IIP-32; Horowitz, Alden, Wiggins, & Pincus, 2000) [ Time Frame: Up to 36 months ]
- Working Alliance Inventory—Short Form, patient version (WAI-SF-P; Tracey, & Kokotovic, 1989) [ Time Frame: Up to 36 months ]
|Study Start Date:||December 2016|
|Estimated Study Completion Date:||July 2020|
|Estimated Primary Completion Date:||June 2019 (Final data collection date for primary outcome measure)|
No Intervention: Pragmatic Match
Randomly assigned, by a case-assigning administrator, to naturalistic treatment with a pragmatically matched provider (control group)
Experimental: Scientific Match
Randomly assigned, by a case-assigning administrator, to naturalistic treatment with a scientifically matched provider (experimental group)
Behavioral: Scientific Match
Consecutive consenting patients at each site will be randomly assigned to condition. The project coordinator, unaware of therapist baseline performance, will generate the randomization sequences using an online random generator. Therapists will be crossed—that is, some of their cases will be matched, while others will be non-matched. Within condition, patients will be assigned sequentially to the site-specific therapists until therapists reach their study quota. In the low probability event that there is no therapist meeting minimal match criteria for a patient in the match condition, that patient will be removed from the study protocol (though will, of course, still receive treatment) and replaced with the next patient where a match does exist (this will also be carefully tracked).
Show Detailed Description
Please refer to this study by its ClinicalTrials.gov identifier: NCT02990000
|Contact: Michael J Constantino, PhDemail@example.com|
|Contact: James F Boswell, PhDfirstname.lastname@example.org|
|United States, Massachusetts|
|University of Massachusetts Amherst||Recruiting|
|Amherst, Massachusetts, United States, 01003|
|Contact: Michael Constantino, PhD 413-545-1388 email@example.com|
|Principal Investigator:||Michael J Constantino, PhD||University of Massachusetts, Amherst|
|Principal Investigator:||James F Boswell, PhD||University at Albany, SUNY|
|Principal Investigator:||David R Kraus, PhD||Outcome Referrals|
|Principal Investigator:||Samuel S Nordberg, PhD||Atrius Health|