Enhancing Mental Health Care by Scientifically Matching Patients to Providers' Strengths
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About
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).
Full description
Background and Significance:
Mental illness is an extraordinary and highly burdensome public health problem. Unfortunately, even for individuals who access mental health care (MHC), the care is too often substandard. Research has consistently demonstrated that approximately 10-15% of patients will deteriorate or experience harm during treatment. Further, when these rates are combined with no-change rates, only 40% or less of patients meaningfully recover. Importantly, treatment research has illuminated substantial variability in providers' outcomes. Simply put, the MHC provider impacts treatment outcomes, and stakeholders lack systematic access to valid and actionable information to optimize effective patient-provider matches. Without collecting and disseminating performance data, stakeholders lack vital information on which to base health care choices and personalize treatment. Conversely, there potentially is immense advantage to matching patients to providers based on scientific outcome data. Patients, stakeholders, researchers, and clinicians have all endorsed such applied knowledge transfer as a high priority. In response, the investigators have developed and piloted a technology to test this match concept and patient-centered health model.
Prominent health care agencies have placed outcome/performance measurement at the center of core initiatives. The Institute of Medicine specifically recommends integrating provider performance data in treatment decision-making. Despite this rhetoric, 2 Cochrane Reviews combined could only identify 4 studies that addressed this question with minimal methodology standards; the results were mixed. Importantly, none involved a targeted dissemination intervention, and none involved MHC. Previous research, including our own, has empirically demonstrated substantial differences in projected treatment effect sizes depending on to which therapist a patient is referred. The key evidence gap is the need for a rigorous test of the effectiveness of a targeted MHC provider-performance dissemination intervention compared to standard/pragmatic referral and case assignment. Relatedly, the Patient-Centered Outcomes Research Institute (PCORI) has called for increased "precision" or "personalized" treatment, with a focus on tailoring. The match algorithm responds directly to this high priority call to customize care in a personal and evidence-based way.
Specific Aims:
The aim of this comparative effectiveness research (CER) is to test an innovative, scientifically informed patient-therapist referral match algorithm based on MHC provider outcome data. The investigators will employ a randomized controlled trial (RCT) to compare the match algorithm with the commonplace pragmatic referral matching (based on provider availability, convenience, or self-reported specialty). Psychosocial treatment itself will remain naturalistically administered by varied providers (e.g., psychologists, social workers) to patients with complex mental health concerns within a partner clinic network, Psychological and Behavioral Consultants (PsychBC). The investigators hypothesize that the scientific match group will outperform the pragmatic match group in decreasing patient symptoms and treatment dropout, and in promoting patient functional outcomes, outcome expectations, and care satisfaction, as well as patient-therapist alliance quality. Doing so will establish the match algorithm as a mechanism of effective patient-centered MHC.
Methods:
The investigators will compare the effectiveness of naturalistic MHC either with or without the scientific matching aid with a double blind, individual level RCT. The investigators will first conduct a baseline assessment of PsychBC therapists' (target enrollment N=44) performance (across at least 15 cases) to determine their strengths in treating 12 behavioral health domains measured by the primary outcome tool on which our match algorithm is based -- the Treatment Outcome Package (TOP). The TOP is already administered routinely in our partner network. Based on years of predictive analytic research, this tool classifies therapists as "effective," "neutral," or "ineffective/harmful" for each TOP domain. The blinded therapists will be crossed over conditions.
Next, for the trial, new adult outpatients (target enrollment N=281) will be randomly assigned to the Match condition or case assignment as usual (typically based on pragmatic considerations, such as provider availability, convenience, or self-reported specialty). The only patient exclusion criterion will be people who are not the primary decision-maker for their care. Thus, patients will present with a multitude of problems across a spectrum of diagnoses. With therapist assignment as the only manipulation, participating therapists will treat patients fully naturalistically. Treatment outcomes will be assessed regularly through mutual termination or up to 16 weeks. Primary analyses will involve hierarchical linear modeling to examine comparative rates and patterns of change on the outcomes.
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Inclusion and exclusion criteria
Inclusion:
- Being 18-70 years of age
- Being the primary, informed decision-maker for one's care
- Willingness to be randomized to condition and to complete a few study-specific measures
Exclusion:
- Being under 18 or over 70 years of age
- Not being responsible for one's own treatment decisions
- Unwillingness to be randomized to condition and/or to complete a few study-specific measures
Trial design
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Interventional model
Masking
288 participants in 2 patient groups
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Data sourced from clinicaltrials.gov
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