Enhancing Mental Health Care by Scientifically Matching Patients to Providers' Strengths
| Status: | Recruiting |
|---|---|
| Conditions: | Psychiatric |
| Therapuetic Areas: | Psychiatry / Psychology |
| Healthy: | No |
| Age Range: | 18 - 70 |
| Updated: | 9/15/2018 |
| Start Date: | December 2016 |
| End Date: | July 2020 |
| Contact: | Michael J Constantino, PhD |
| Email: | mconstantino@psych.umass.edu |
| Phone: | 4135451388 |
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).
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).
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, 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.
Study Aims:
The aim of this CER is to test the effectiveness of 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.
Study Description:
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' (N=44) performance (across at
least 15 cases) to determine their strengths in treating 12 behavioral health domains
measured by the primary outcome measure on which our match algorithm is based: the Treatment
Outcome Package (TOP). TOP is already administered routinely in our partner network. Based on
years of predictive analytic research, this tool classifies therapists as "effective,"
"unclassifiable/ineffective," or "harmful" for each TOP domain. The blinded therapists will
be crossed over conditions.
A research assistant will assess at baseline new adult patients (N = 264, 6 per therapist,
based on our power analysis) referred from mental health or primary care providers. 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. In addition to a diagnostic interview and relevant measures of the dependent
variables and demographic characteristics, the baseline assessment will include risk-adjusted
TOP scores that will inform experimental group matching. Next, patients will be randomly
assigned to the match group or the no scientific match group. Treatment outcome will be
assessed regularly through mutual termination or up to 16 weeks. Ancillary assessments will
include self- and clinician-rated global distress, therapeutic alliance quality, patient
outcome expectations, treatment dropout, and patient satisfaction. Primary analyses will
involve hierarchical linear modeling to examine comparative rates and patterns of change on
the outcomes.
Although performance measurement has existed for decades and some research has begun to
explore the impact of disseminating provider performance data on health care outcomes, it has
(a) rarely employed a RCT design, (b) typically focused on correlating passive dissemination
with distal systems-level outcomes, and (c) only been conducted outside of MHC. This research
will address these limitations by investigating the comparative effectiveness of using MHC
provider outcomes to inform the assignment/referral of this patient to this scientifically
matched psychotherapist. This comparator is ideal because it represents the status quo where
virtually no attempt is made to use scientific data to match specific patients with specific
therapists who have a proven track record in the patient's area(s) of need.
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, 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.
Study Aims:
The aim of this CER is to test the effectiveness of 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.
Study Description:
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' (N=44) performance (across at
least 15 cases) to determine their strengths in treating 12 behavioral health domains
measured by the primary outcome measure on which our match algorithm is based: the Treatment
Outcome Package (TOP). TOP is already administered routinely in our partner network. Based on
years of predictive analytic research, this tool classifies therapists as "effective,"
"unclassifiable/ineffective," or "harmful" for each TOP domain. The blinded therapists will
be crossed over conditions.
A research assistant will assess at baseline new adult patients (N = 264, 6 per therapist,
based on our power analysis) referred from mental health or primary care providers. 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. In addition to a diagnostic interview and relevant measures of the dependent
variables and demographic characteristics, the baseline assessment will include risk-adjusted
TOP scores that will inform experimental group matching. Next, patients will be randomly
assigned to the match group or the no scientific match group. Treatment outcome will be
assessed regularly through mutual termination or up to 16 weeks. Ancillary assessments will
include self- and clinician-rated global distress, therapeutic alliance quality, patient
outcome expectations, treatment dropout, and patient satisfaction. Primary analyses will
involve hierarchical linear modeling to examine comparative rates and patterns of change on
the outcomes.
Although performance measurement has existed for decades and some research has begun to
explore the impact of disseminating provider performance data on health care outcomes, it has
(a) rarely employed a RCT design, (b) typically focused on correlating passive dissemination
with distal systems-level outcomes, and (c) only been conducted outside of MHC. This research
will address these limitations by investigating the comparative effectiveness of using MHC
provider outcomes to inform the assignment/referral of this patient to this scientifically
matched psychotherapist. This comparator is ideal because it represents the status quo where
virtually no attempt is made to use scientific data to match specific patients with specific
therapists who have a proven track record in the patient's area(s) of need.
Inclusion Criteria:
- Adult men and women (age 18-65)
- Largely referred by Atrius primary care and obstetrics/gynecology for triage and
treatment through Atrius's behavioral health specialty practice.
- Has an Atrius primary care physician (PCP).
- Willingness to be randomized to condition and to complete a few study-specific
measures.
- Most likely that the sample will be predominated by the following diagnoses: complex
mood, trauma, and anxiety disorders, eating disorders, simple schizophrenia,
borderline personality disorder, substance abuse, and insomnia.
Exclusion Criteria:
- Patients who are not the primary, informed decision-maker for their care
- Adults over age 65 years
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