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Enhancing mental health care by scientifically matching patients to providers' strengths
Investigator (PI): Constantino, Michael
Performing Organization (PO): (Current): University of Massachusetts Amherst, School of Arts and Sciences, Department of Psychological and Brain Sciences / (413) 545-2383
Supporting Agency (SA): Patient-Centered Outcomes Research Institute (PCORI)
Initial Year: 2016
Final Year: 2020
Record Source/Award ID: PCORI/IHS-1503-28573
Funding: Total Award Amount: $1,632,317
Award Type: Contract
Award Information: PCORI: More information and project results (when completed)
Abstract: Many patients receive substandard, or even harmful, mental health care (MHC). Research has shown that 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 (for example, depression, substance abuse) yet appear to struggle in others (for example, anxiety, social functioning); and some are reliably ineffective 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 lowers 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, we aim to demonstrate the comparative effectiveness of a scientifically based patient-provider match system compared with status quo pragmatic case assignment. We expect in the scientific match group significantly better treatment outcomes (for example, symptoms, quality of life) and higher patient satisfaction with treatment. We 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 of them struggle to find the right provider, which unnecessarily prolongs suffering and creates 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 (for example, symptom domains, functioning domains, quality of life). The success of this project will be driven by a comprehensive and fully integrated patient and stakeholder partner engagement plan. We have assembled a project advisory board with members representing patient, provider, administrator, and health care policy perspectives. With diverse complementary skills, our board will work collaboratively to ensure that procedures meet methodology standards, patient and stakeholder views are represented and feedback incorporated, and the results are widely and effectively publicized to the relevant targets and stakeholders.
MeSH Terms:
  • Anxiety /therapy
  • Comparative Effectiveness Research
  • Depression /therapy
  • Humans
  • Mental Disorders /*rehabilitation
  • /therapy
  • Mental Health Services /*organization & administration
  • Patient Satisfaction
  • Patient-Centered Care
  • Quality of Life
  • Referral and Consultation
  • Substance-Related Disorders /therapy
Country: United States
State: Massachusetts
Zip Code: 01003
UI: 20162152
Project Status: Ongoing
Record History: ('2017: Project extended to 2020',)