Decision Support Tools for Mental Health

(K-ORCA Trial)

Overseen By Kelli Wright

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Chestnut Health Systems

Disqualifiers: Not involved in child welfare

No Placebo Group All trial participants will receive the active study treatment (no placebo)

Compensation: Varies

Number of Visits: Unknown

80 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

The trial aims to enhance mental health interventions by developing a decision-support tool that assists leaders in making better choices about programs for the Family First Prevention Services Act. Participants will test two versions of the tool: Optimizing Responses with Collaborative Assessments-Automated (ORCA-A), which provides automated assistance, and Optimizing Responses with Collaborative Assessments-Live (ORCA-L), which offers live facilitation, to determine which is more effective. This trial suits adults involved in decision-making related to child welfare services under the Act. As an unphased trial, it offers participants the chance to contribute to innovative solutions in child welfare decision-making.

Will I have to stop taking my current medications?

The trial information does not specify whether you need to stop taking your current medications.

What prior data suggests that these decision-support tools are safe for mental health interventions?

Research has shown that decision-support tools like ORCA-A (Optimizing Responses with Collaborative Assessments-Automated) have been studied in mental health settings. These tools use AI (artificial intelligence) and are generally well-accepted, assisting in decision-making without causing harm or discomfort. Reviews of AI systems in mental healthcare suggest they are safe and can enhance decision-making.

For ORCA-L (Optimizing Responses with Collaborative Assessments-Live), research on similar teamwork practices in mental healthcare has shown positive results. These practices emphasize collaboration and communication in treatment decisions, which have been safe and well-received by participants.

Both ORCA-A and ORCA-L aim to improve mental health decisions. Current evidence suggests they are safe to use, with no reports of harmful side effects. They help people make better treatment choices without causing discomfort or risk. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about these decision support tools for mental health because they offer innovative ways to enhance decision-making processes. Unlike traditional treatments, which often rely solely on therapy or medication, ORCA-A provides automated facilitation, while ORCA-L offers live facilitation, both of which support decision-makers in mental health settings. These tools are designed to optimize responses by integrating collaborative assessments, potentially leading to more personalized and effective mental health strategies. By utilizing both automated and live facilitation, these tools could bridge gaps in mental health care, offering more tailored support for individuals.

What evidence suggests that this trial's decision-support tools could be effective for mental health interventions?

Research has shown that decision-support tools like ORCA-A, one of the treatments in this trial, can improve mental health care. These tools guide decision-makers in selecting effective programs by providing automated assistance. Early results suggest this method clarifies the decision process and bases it more on evidence.

For ORCA-L, another treatment option in this trial, studies indicate that live support with decision tools can enhance mental health treatment. Regularly checking and providing feedback on outcomes helps clinicians make better decisions. This approach supports effective care coordination and enhances results. ^{1 2 6 7 8}

Who Is on the Research Team?

Gracelyn Cruden, PhD

Principal Investigator

Chestnut Health Systems

Are You a Good Fit for This Trial?

This trial is for adults over 18 who speak English, are not institutionalized, and are involved in state decision-making related to the Family First Prevention Services Act. They must be willing to participate in multiple data collection events.

Inclusion Criteria

I live in my own home or with family.

I speak English.

I was involved in decisions for the Family First Prevention Services Act.

Timeline for a Trial Participant

Development

Development of a web-based platform to host the decision-support tool and automated facilitation strategy with input from decision makers.

6 months

Pilot Testing

Decision makers pilot the decision-support intervention comprised of the tool and live or automated facilitation.

12 months

Regular virtual meetings with decision makers

Follow-up

Participants are monitored for the quality of decision experiences, processes, and outcomes post-intervention.

12 months

Bi-weekly virtual check-ins

What Are the Treatments Tested in This Trial?

Interventions

• Optimizing Responses with Collaborative Assessments-Automated (ORCA-A)
• Optimizing Responses with Collaborative Assessments-Live (ORCA-L)

Trial Overview

The K-ORCA trial is testing a web-based decision-support tool with automated or live facilitation aimed at improving mental health interventions. It's part of efforts under the FFPSA policy to enhance program implementation quality.

How Is the Trial Designed?

Group I: ORCA-A Active Control 1 Intervention

Decision Makers receiving automated facilitation to accompany ORCA

Group II: ORCA-L Active Control 1 Intervention

Decision Makers receiving live facilitation to accompany ORCA

Who Is Running the Clinical Trial?

Chestnut Health Systems

Lead Sponsor

Trials

27

Recruited

24,500+

National Institute of Mental Health (NIMH)

Collaborator

Trials

3,007

Recruited

2,852,000+

Published Research Related to This Trial

The implementation of a decision support toolkit in 52 community mental health centers (CMHCs) over 12 months significantly improved the quality of care, as evidenced by high participation and improved utilization of the toolkit components (p < .0001).

Survey responses from individuals receiving services confirmed the successful implementation of decision support, indicating that community-based providers can effectively enhance mental health services through structured collaborative learning.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Successful Implementation Strategy to Support Adoption of Decision Making in Mental Health Services. MacDonald-Wilson, KL., Hutchison, SL., Karpov, I., et al.[2021]

Collaborative assessment actively involves clients in the evaluation process, allowing them to discuss their data with assessors to set personalized treatment goals, which enhances engagement and insight.

The Evidence-Based Assessment System for Clinicians (EAS-C) utilizes internet-based tools to track psychological variables daily, providing immediate feedback and individualized support, which can improve treatment outcomes and bridge the gap between research and clinical practice.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Internet-Based Approaches to Collaborative Therapeutic Assessment: New Opportunities for Professional Psychologists. Smith, RE., Fagan, C., Wilson, NL., et al.[2021]

AI technologies in mental health can enhance patient access and care quality, but current studies mainly address challenges faced by policymakers rather than frontline clinicians.

The proposed framework for 'pragmatic AI-augmentation' categorizes AI technologies into automation, engagement, and clinical decision support, highlighting their potential benefits and the practical challenges clinicians may encounter when integrating them into daily practice.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Pragmatic AI-augmentation in mental healthcare: Key technologies, potential benefits, and real-world challenges and solutions for frontline clinicians. Kellogg, KC., Sadeh-Sharvit, S.[2022]

Advancements in information technology are enabling the development of research-based decision support tools that can improve the accuracy of psychiatric assessments and care allocation.

Recent computerized decision support tools have shown improved reliability and validity compared to earlier versions, suggesting a promising future for their application in mental health care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Psychiatric decision making in the 90's: the coming era of decision support. Gray, GV., Glazer, WM.[2019]

Comprehensive electronic decision support systems can enhance mental health care by integrating client preferences, research evidence, and clinician skills, promoting shared decision-making.

These systems aim to facilitate collaborative management of both illness and wellness, potentially leading to better outcomes for clients and more effective treatment strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Comprehensive electronic decision support systems. Drake, RE., Deegan, PE., Woltmann, E., et al.[2017]

Integrating expert decision-support software with outcomes analyses can significantly improve the consistency and quality of clinical decision-making in behavioral healthcare, allowing providers from different backgrounds to make informed choices.

These systems can enhance accountability in behavioral healthcare by linking patient assessment data to clinical decisions, which may help advocate for better healthcare benefits for psychiatric patients.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Linking outcomes analysis to critical clinical pathways. Pigott, HE.[2019]

The Treatment-E-Assist (TREAT) system, a computerized clinical decision aid, was found to be feasible and effective for clinicians in personalizing treatment for patients with psychotic disorders, based on a pilot study involving 6 clinicians and 16 patients.

Clinicians reported that TREAT enhanced their awareness of treatment options and effectively utilized routine outcome monitoring (ROM) data to inform treatment plans, indicating its potential to improve care for patients with severe mental illnesses.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The development and evaluation of a computerized decision aid for the treatment of psychotic disorders. Tasma, M., Roebroek, LO., Liemburg, EJ., et al.[2019]

Routine outcome monitoring (ROM) using standardized measurement tools improves patient outcomes in mental health care by allowing clinicians to track patterns and compare results across different settings.

The increasing variability in clinician skills and patient outcomes emphasizes the need for ROM in decision-making processes, suggesting it could play a crucial role in enhancing the effectiveness of mental health services.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Expanding Relevance of Routinely Collected Outcome Data for Mental Health Care Decision Making. Boswell, JF., Constantino, MJ., Kraus, DR., et al.[2019]

In a large-scale outcomes management project involving over 7,000 clinicians, patients with significant psychological distress showed a quick response to treatment, indicating that timely intervention is crucial for effective care.

The study highlighted that while treatment duration and intensity vary, maintaining patient engagement is key; even those with poor initial responses can achieve positive outcomes if they stay involved in their treatment.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Implementation of a feedback system in a managed care environment: what are patients teaching us? Brown, GS., Jones, ER.[2019]

The ACORN Toolkit is a comprehensive clinical information system designed to enhance mental health services by integrating diverse datasets, including client outcomes and therapist information.

By providing clinical feedback to therapists, the ACORN Toolkit has contributed to improved outcomes in psychotherapy, highlighting its potential impact on current practices and future training in mental health care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A collaborative outcome resource network (ACORN): Tools for increasing the value of psychotherapy. Brown, GS., Simon, A., Cameron, J., et al.[2019]

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This systematic review and meta-analysis aims to fill this gap by synthesizing evidence on the effectiveness of AI-based CAs in improving mental health.

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