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

Trial Summary

What is the purpose of this trial?

This proposal responds to NIMH Objective 4.2.c to develop "decision-support tools and technologies that increase the effectiveness and continuous improvement of mental health interventions" by leveraging the Family First Prevention Services Act (FFPSA) policy opportunity. First, a web-based platform to host (a) a decision-support tool and (b) automated facilitation for group decisions with the tool will be developed with state partners' feedback. Next, decision makers leading their states' FFPSA quality improvement efforts will be engaged to pilot a decision-support intervention comprised of the tool and live or automated facilitation, and to evaluate the implementation quality of evidence-based programs adopted with the decision-support intervention.

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 data supports the effectiveness of the treatment Optimizing Responses with Collaborative Assessments-Automated (ORCA-A) and Optimizing Responses with Collaborative Assessments-Live (ORCA-L)?

Research shows that using routine outcome monitoring (ROM) and feedback tools can improve mental health treatment outcomes by helping clinicians make better decisions. Similar tools, like the ACORN Toolkit, have been shown to enhance psychotherapy results by providing valuable feedback to therapists. ^{1 2 3 4 5}

How does the Decision Support Tools for Mental Health treatment differ from other treatments for mental health conditions?

This treatment is unique because it uses decision support tools, which are computer-based systems that help clinicians and patients make informed decisions by providing evidence-based recommendations and personalized feedback. Unlike traditional treatments, these tools facilitate collaborative decision-making and can be accessed via the internet or smartphones, allowing for real-time tracking and adjustment of treatment plans. ^{6 7 8 9 10}

Research Team

Gracelyn Cruden, PhD

Principal Investigator

Chestnut Health Systems

Eligibility Criteria

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

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

Treatment Details

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.

Participant Groups

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+

Findings from Research

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]

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]

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]

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]

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]

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]

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]

References

1. United States pubmed.ncbi.nlm.nih.gov

A collaborative outcome resource network (ACORN): Tools for increasing the value of psychotherapy. [2019]

2. England pubmed.ncbi.nlm.nih.gov

The development and evaluation of a computerized decision aid for the treatment of psychotic disorders. [2019]

3. United States pubmed.ncbi.nlm.nih.gov

The Expanding Relevance of Routinely Collected Outcome Data for Mental Health Care Decision Making. [2019]

4. United States pubmed.ncbi.nlm.nih.gov

Implementation of a feedback system in a managed care environment: what are patients teaching us? [2019]

5. United States pubmed.ncbi.nlm.nih.gov

Linking outcomes analysis to critical clinical pathways. [2019]

6. United States pubmed.ncbi.nlm.nih.gov

Internet-Based Approaches to Collaborative Therapeutic Assessment: New Opportunities for Professional Psychologists. [2021]

7. United States pubmed.ncbi.nlm.nih.gov

A Successful Implementation Strategy to Support Adoption of Decision Making in Mental Health Services. [2021]

8. United States pubmed.ncbi.nlm.nih.gov

Comprehensive electronic decision support systems. [2017]

9. Switzerland pubmed.ncbi.nlm.nih.gov

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

10. United States pubmed.ncbi.nlm.nih.gov

Psychiatric decision making in the 90's: the coming era of decision support. [2019]