Enhancing Mental Health Care by Scientifically Matching Patients to Providers' Strengths

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).

Conditions

• Mental Illness

Interventions

BEHAVIORAL

Scientific Match

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).

Sponsors & Collaborators

• Patient-Centered Outcomes Research Institute

  collaborator OTHER

• University at Albany

  collaborator OTHER

• Psychological and Behavioral Consultants

  collaborator OTHER

• Outcome Referrals, Inc.

  collaborator INDUSTRY

• University of Massachusetts, Amherst

  lead OTHER

Principal Investigators

• Michael J Constantino, PhD · University of Massachusetts, Amherst

• James F Boswell, PhD · University at Albany, SUNY

• David R Kraus, PhD · Outcome Referrals, Inc.

• Thomas P Swales, PhD · Psychological and Behavioral Consultants

Study Design

Allocation

RANDOMIZED

Purpose

HEALTH_SERVICES_RESEARCH

Masking

DOUBLE

Model

PARALLEL

Eligibility

Min Age

18 Years

Max Age

70 Years

Sex

ALL

Healthy Volunteers

No

Timeline & Regulatory

Start

2017-11-06

Primary Completion

2019-09-23

Completion

2020-03-15

Countries

• United States

Study Locations