Enhancing Mental Health Care by Scientifically Matching Patients to Providers' Strengths
Condition: Mental Illness
MeSH terms: Mental Disorders
Study type: Interventional

Summary: 
      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).
    

Detailed 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.
    

Intervention name(s): Scientific Match
Intervention type(s): Behavioral
Intervention description(s): We have developed an innovative, personalized Match System based on provider track records determined with a multidimensional outcomes tool - the Treatment Outcome Package (TOP). Specifically, patients are assigned to therapists with previously established strengths (i.e., being historically effective) in treating their primary problems (e.g., depression, anxiety).
Other intervention name(s): Match System
Allocation: Randomized
Intervention model: Parallel Assignment
Masking: Double (Participant, Care Provider)
Primary purpose: Health Services Research