A Clinical Decision Support Tool for Electronic Health Records

Study Overview
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Brief Summary
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For behavioral health clinicians who are interested in getting tailored treatment and level of care recommendations, BH-CDS is a desktop/tablet web-based application that provides clinicians with data and a rationale for better decision-making to improve patient care. Few Clinical Decision Support (CDS) systems are available for Behavioral Health, and unlike existing CDS this product will compile relevant patient data and organize these data into general treatment recommendations linked to the patient's presenting circumstances, symptoms and substance use issues. The BH-CDS solution shall factor patient characteristics into a Latent Class Analysis (LCA) that will group patients according to their responses with other patients with similar responses (i.e., a subgroup or class). Once patients have been assigned to a class, the solution shall present recommendations to counselors that use the software.

Detailed Description
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Summary of the specific aims and impact on public health of the Phase II. Substance abuse treatment is often complicated by a client's family, employment, psychiatric, or legal problems. When these co-existing issues are addressed with evidence-based practices (EBPs), outcomes improve. The inclusion of behavioral health evidence-based practices to enhance Medication-Assisted Treatment (MAT) is the subject of a number of federal and state treatment initiatives. However, the integration of such evidence-based practices into clinical settings continues to lag, despite extensive efforts to educate clinicians through training. Since it is often difficult to integrate EBPs into the clinical workflow, clinicians rely on established (and often ineffective) patterns of care. This grant proposed to (1) use electronic health record data on patients with a diagnosis of opioid use disorder to create profiles of patient groups using latent class analysis (LCA) analysis and determine, for each class, which combination of services are empirically associated with positive outcomes; (2) develop clinical decision support (CDS) software to help counselors classify patients and match them to appropriate services, and (3) conduct a field trial (randomized controlled trial or RCT) to test the impact of the CDS software on clinical practice. Provide a succinct account of published and unpublished results, indicating progress toward achievement of the originally stated aims. Latent Class Analysis: The first aim (using electronic health record data on patients with a diagnosis of opioid use disorder to create profiles of patient groups using LCA and determining which combinations of services are empirically associated with positive outcomes for each class of opioid users) was successfully achieved, as discussed in previous progress reports. Four classes were identified: Class 1: Individuals in this class tend to have relatively high medical and mental health problems, be taking psychiatric medications and tend to experience control problems with their temper. Class 2: Individuals in this class tend to have mental health problems, but are not taking psychiatric medications. They do not generally snort or inject opiates and tend not to have serious medical problems. Class 3: Individuals in this class tend to have medical and mental health problems and are taking psychiatric medications. They have a tendency to snort or inject opiates and may have some problems controlling their temper. Class 4: Individuals in this class tend to have a high tendency to snort or inject opiates. They have medium medical problems and low mental health issues. Software Development: Based on the LCA results, CDS software was developed to help counselors classify patients and match them to appropriate services. Field Trial: The purpose of this field trial was to evaluate the effectiveness of this new CDS software when compared to clinical care as usual or treatment-as-usual (TAU), and to gather information about feasibility and perceived usefulness of the CDS software from the counselor's perspective. It was anticipated that when compared to TAU, clients in the experimental condition would (1) have significantly greater matched evidenced-based and wraparound services, (2) have greater engagement in treatment, (3) have less frequent use of substances, (4) have greater biopsychosocial functioning, and (5) have greater cost effectiveness (i.e., less cost to achieve successful outcomes).

Official Title
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A Clinical Decision Support Tool for Electronic Health Records

Conditions
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Substance Abuse

Intervention / Treatment
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* Behavioral: BHCDS-based recommendations
* Behavioral: Non-tailored recommendations


Participation Criteria
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Eligibility Criteria
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Counselor Inclusion Criteria: full or part-time counselors English speaking Treat clients with opioid use problems Have an active e-mail account Client Inclusion Criteria: Currently meet with a counselor in the study at least once a month able to read and speak English in treatment for an opioid use problem completed detox, if it was necessary Exclusion Criteria: -

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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Accepts Healthy Volunteers

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Health Services Research
Allocation: Randomized
Intervention Model: Parallel Assignment
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: BHCDS-based recommendations<br>The Experimental condition will use the BH-CDS tool and receive tailored recommendations in addition to treatment as usual. | Behavioral: BHCDS-based recommendations<br> <br> * Other names: Behavioral Health Clinical Decision Support, BH-CDS;|
| Placebo Comparator: Non-tailored recommendations<br>The Control condition will use the BH-CDS tool and receive non-tailored recommendations in addition to treatment as usual. | Behavioral: Non-tailored recommendations<br> <br> * Other names: control condition;|

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Change in clients' past 30-day substance use and psychosocial functioning at 1 month and 3 months post-baseline as measured by ASI-MV composite scores | Measured through ASI-MV composite scores at each time point. Composite Scores for the Addiction Severity Index - Multimedia Version (ASI-MV) are generated from a number of answered questions in each domain that refer to client behavior over the last 30 days. Therefore, they are useful for identifying changes in problem status and can be used in research and outcome evaluation. For more information, please see: Butler, S. F., Budman, S. H., Goldman, R. J., Newman, F. J., Beckley, K. E., Trottier, D., & Cacciola, J. S. (2001). Initial validation of a computer-administered Addiction Severity Index: The ASI-MV. Psychology of Addictive Behaviors, 15(1), 4. | 1-month, 3-month | 
| Number of client treatment visits | Total number of treatment and assessment visits throughout the field trial period. | 3-month | 
| Number of services each client receives or is referred to | Total number of services (including wraparound services, such as housing support or medical consultation) the client received or was referred to throughout the field trial period. | 3-month | 

 Terms related to the study
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Keywords Provided by Centre Hospitalier Valida
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clinical decision support, substance use, opioid