Practice-Based Intervention for Urinary Incontinence

(OPTIMA Trial)

Recruiting at 3 trial locations

Overseen By Maxwell B Moore, MS

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of California, San Diego

Disqualifiers: Pregnant, Severe memory impairment, Psychiatric history, others

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

Compensation: Varies

Number of Visits: 3

1600 Participants Needed

Trial Summary

What is the purpose of this trial?

The main goal of this clinical trial is to improve the care for urinary incontinence (UI) provided to adult women by primary care providers. The main questions it aims to answer are: * Can a practice-based intervention involving primary care providers lead to improved quality of incontinence care? * Will this intervention reduce the utilization of specialist care for urinary incontinence? * What effect will this intervention have on patient outcomes, including disease-specific outcomes, symptom severity, quality of life, and patient knowledge? * Does our intervention reduce disparities in care? Provider participants will be randomized at the office level to either an intervention group or a delayed intervention (control) group. The intervention group will receive an intervention consisting of academic detailing, clinical decision support tools, electronic referral, and the ability to refer to an advanced practice provider for co-management. The delayed intervention group will provide usual care until the crossover phase of the study, at which point they will receive the same intervention as the intervention group. Patient participants will bring up urinary incontinence with their primary care provider and complete three electronic surveys. Researchers will compare the intervention group to the delayed intervention (control) group to see if the intervention results in increased adherence to evidence-based quality indicators.

Do I need to stop my current medications for this trial?

The trial information does not specify whether you need to stop taking your current medications. It's best to discuss this with the trial coordinators or your primary care provider.

What data supports the effectiveness of the treatment Practice-Based Intervention for Urinary Incontinence?

Research shows that computer-based clinical decision support systems (CDS) can improve patient outcomes by helping healthcare providers make better decisions. These systems are effective in various clinical settings, suggesting they could also benefit treatments for urinary incontinence by providing timely, patient-specific recommendations. ^{1 2 3 4 5}

Is the practice-based intervention for urinary incontinence safe for humans?

The research articles reviewed do not provide specific safety data for the practice-based intervention for urinary incontinence or related interventions. They focus on the usability, benefits, and barriers of clinical decision support tools without addressing safety concerns. ^{1 6 7 8 9}

How is the Practice-Based Intervention for Urinary Incontinence treatment different from other treatments?

This treatment is unique because it combines several modern healthcare tools like electronic clinical decision support, electronic referrals, and co-management with advanced practice providers to improve care for urinary incontinence. Unlike traditional treatments, it leverages technology and collaborative care to enhance decision-making and streamline patient management. ^{1 10 11 12 13}

Eligibility Criteria

This trial is for adult women over 18 who speak English or Spanish and have urinary incontinence. They must confirm their condition with a screening tool and agree to participate. Primary care physicians from certain sites can also join, provided they're part of the recruited offices.

Inclusion Criteria

I am female.

Inclusion Criteria of Patients

I am older than 18 years.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Implementation of a practice-based intervention involving physician education, electronic decision support, and patient education/co-management by an advanced practice provider

6 months

Regular visits as per intervention protocol

Crossover Validation

Control group receives the intervention to validate improvements in patient care and outcomes

6 months

Follow-up

Participants are monitored for safety and effectiveness after intervention

6 months

Follow-up assessments at 3 and 6 months

Treatment Details

Interventions

• Academic Detailing
• APP Co-management
• Electronic Clinical Decision Support
• Electronic Referral

Trial Overview The study tests if an intervention involving academic detailing, electronic decision support, app co-management, and electronic referrals by primary care providers improves urinary incontinence care compared to usual practices.

Participant Groups

Group I: Practice-Based Intervention Experimental Treatment 4 Interventions

The intervention will include physician education, electronic decision support, implementation of an electronic referral system, and patient education/co-management by an advance practice provider.

Group II: Control Active Control 1 Intervention

Offices in this group will not receive the four-pronged intervention. However, after the study trial, there will be a "validation phase" in which the initial control group will then receive the intervention. This cross-over deign will then allow all 60 offices to receive the intervention. Data from the cross-over intervention group will be analyzed as a separate cohort to confirm improvements in patient care and outcomes as measured in the intervention group under the study trial can be duplicated.

Academic Detailing is already approved in United States for the following indications:

Approved in United States as Academic Detailing Intervention for:

• Improving quality of incontinence care
• Reducing utilization of specialist care for urinary incontinence
• Enhancing patient outcomes

Who Is Running the Clinical Trial?

University of California, San Diego

Lead Sponsor

Trials

1,215

Recruited

1,593,000+

RAND

Collaborator

Trials

145

Recruited

617,000+

Agency for Healthcare Research and Quality (AHRQ)

Collaborator

Trials

415

Recruited

6,777,000+

Cedars-Sinai Medical Center

Collaborator

Trials

523

Recruited

165,000+

University of California, Los Angeles

Collaborator

Trials

1,594

Recruited

10,430,000+

Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center

Collaborator

Trials

105

Recruited

46,600+

Findings from Research

Clinics affiliated with health systems utilized more clinical decision support (CDS) tools, such as automated reminders, but also faced greater barriers related to resources and user acceptance, indicating a complex relationship between support and challenges in implementation.

Rural clinics reported higher barriers to training, suggesting that targeted resources and support are needed to enhance CDS utilization, particularly in these settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Barriers to using clinical decision support in ambulatory care: Do clinics in health systems fare better? Shi, Y., Amill-Rosario, A., Rudin, RS., et al.[2022]

Computer decision support systems are effective tools that help clinicians make better diagnostic and therapeutic decisions by providing patient-specific recommendations and alerts, ultimately improving patient care.

Despite challenges in implementation, such as the need for robust computing infrastructure and changes to workflow, evidence from various clinical trials shows that these systems significantly enhance clinician performance across different healthcare settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer decision support systems. Payne, TH.[2019]

The article highlights the need for improved patient-centered clinical decision support (PC CDS) tools to enhance healthcare decision-making, emphasizing the importance of making research findings more shareable and publicly available.

A comprehensive assessment involving a technical expert panel, literature review, and interviews with 18 stakeholders identified key areas for improvement, including the need for standardized processes to integrate clinical guidelines and patient-generated health data into PC CDS systems.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The technical landscape for patient-centered CDS: progress, gaps, and challenges. Dullabh, P., Heaney-Huls, K., Lobach, DF., et al.[2022]

The study assessed the current state of patient-centered clinical decision support (PC CDS) tools, highlighting the need for improved shareability and standardization to enhance patient care and clinical outcomes.

Despite significant progress in developing PC CDS, the research identified ten key technical challenges that need to be addressed to facilitate better dissemination of best practices in healthcare.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Technology Landscape of Patient-Centered Clinical Decision Support - Where Are We and What Is Needed? Dullabh, P., Heaney-Huls, K., Hovey, L., et al.[2022]

A systematic review of 15 studies involving 13,480 patients found that computer-based clinical decision support systems (CCDSS) can have marginal positive effects on patient-reported outcomes (PRO), particularly in symptom management.

No negative effects were reported from the use of CCDSS, suggesting they are safe to implement, but further development of user-friendly systems with clear evidence-based guidance is needed to enhance their effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computer-Based Clinical Decision Support Systems and Patient-Reported Outcomes: A Systematic Review. Blum, D., Raj, SX., Oberholzer, R., et al.[2018]

Computerized clinical decision support systems (CCDSSs) have been shown to improve the process of care in 63% of the 40 randomized controlled trials reviewed, particularly for screening and managing dyslipidaemia in primary care.

However, the evidence for CCDSS effectiveness in other areas, such as cancer and mental health screening, is mixed, and there is limited information on their impact on patient outcomes, safety, and costs.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computerized clinical decision support systems for primary preventive care: a decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes. Souza, NM., Sebaldt, RJ., Mackay, JA., et al.[2022]

A survey of 153 primary care providers revealed that 98% of those using clinical pathways found them to provide evidence-based information and improve patient care, highlighting their perceived efficacy in clinical decision-making.

The main barriers to using these pathways were forgetfulness among users and a lack of awareness among non-users, indicating a need for better dissemination and education about these tools.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

EHR-Integrated Clinical Pathways Promote Education, Confidence, and Save Time for Primary Care Providers. Macke, LB., Sieja, A., Suresh, K., et al.[2023]

A new informatics framework, including a breast cancer risk navigation (BNAV) tool, has been developed to enhance breast cancer risk assessment and promote chemoprevention in primary care, aiming to improve decision-making for high-risk women.

Usability testing with seven subjects revealed important feedback on the BNAV tool, leading to interface improvements that could help increase provider engagement and ultimately boost the uptake of effective chemoprevention strategies.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Introducing a Comprehensive Informatics Framework to Promote Breast Cancer Risk Assessment and Chemoprevention in the Primary Care Setting. Finkelstein, J., Wood, J., Crew, KD., et al.[2020]

A pediatric cardiovascular disease (CVD) risk factor tool was positively received by clinicians, indicating its potential to improve the evaluation and prevention of CVD in children and teens.

Feedback from pediatricians led to important revisions for the tool, such as enhancing user-friendliness on mobile devices and refining recommendations, which are crucial for effective clinical decision support.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Usability Testing and Adaptation of the Pediatric Cardiovascular Risk Reduction Clinical Decision Support Tool. Williams, PA., Furberg, RD., Bagwell, JE., et al.[2023]

Current comparative studies of commercially available drug information (DI) databases are limited and often lack transparency, making it difficult for clinicians to make informed purchasing decisions.

Future research should focus on integrating DI resources with clinical decision support systems (CDSS) to ensure that alerts and information are consistent and relevant to the specific needs of healthcare practices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Quality of drug information database research for clinical decision support. Hoody, DW., Beckett, CF., Zielenski, C., et al.[2021]

Implementation science provides essential theories and frameworks that can significantly reduce the time it takes for medical innovations to be applied in clinical settings, which currently averages 17 years.

By applying implementation science principles, clinicians can effectively lead quality improvement initiatives, such as reducing catheter-associated urinary tract infections, ensuring that evidence-based practices are successfully integrated into everyday healthcare.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Applying Implementation Science in Surgical Infection Quality Improvement. Golob, JF., Evans, HL., Kreiner, LA., et al.[2021]

A survey of community paediatricians in Central West Ontario revealed that 98% are interested in accessing the proposed Central West Ontario Pediatric Information Network (CWPIN), indicating a strong demand for electronic clinical support tools.

Despite high computer literacy rates among paediatricians, differences in workplace computer use by sex suggest that role allocation may influence the potential usage of CWPIN, rather than computer skills being a barrier to implementation.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

e-Health? Clinical information network interest and impediments in a community paediatric setting. Cawdron, R., Calder, J., Issenman, RM.[2021]

A study involving ethnographic observations and interviews identified six key barriers to the effective use of clinical reminders in the Veteran's Health Administration (VHA), including workload, time to manage reminders, and training issues.

A follow-up survey of 261 participants revealed four additional barriers, such as ease of use and accessibility of workstations, highlighting the complexity of improving adherence to clinical reminders among healthcare providers.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Identifying barriers to the effective use of clinical reminders: bootstrapping multiple methods. Patterson, ES., Doebbeling, BN., Fung, CH., et al.[2018]

References

1. England pubmed.ncbi.nlm.nih.gov

Barriers to using clinical decision support in ambulatory care: Do clinics in health systems fare better? [2022]

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

Computer decision support systems. [2019]

3. England pubmed.ncbi.nlm.nih.gov

The technical landscape for patient-centered CDS: progress, gaps, and challenges. [2022]

4. Netherlands pubmed.ncbi.nlm.nih.gov

The Technology Landscape of Patient-Centered Clinical Decision Support - Where Are We and What Is Needed? [2022]

5. New Zealand pubmed.ncbi.nlm.nih.gov

Computer-Based Clinical Decision Support Systems and Patient-Reported Outcomes: A Systematic Review. [2018]

6. England pubmed.ncbi.nlm.nih.gov

Computerized clinical decision support systems for primary preventive care: a decision-maker-researcher partnership systematic review of effects on process of care and patient outcomes. [2022]

7. Netherlands pubmed.ncbi.nlm.nih.gov

EHR-Integrated Clinical Pathways Promote Education, Confidence, and Save Time for Primary Care Providers. [2023]

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

Introducing a Comprehensive Informatics Framework to Promote Breast Cancer Risk Assessment and Chemoprevention in the Primary Care Setting. [2020]

9. Canada pubmed.ncbi.nlm.nih.gov

Usability Testing and Adaptation of the Pediatric Cardiovascular Risk Reduction Clinical Decision Support Tool. [2023]

10. Netherlands pubmed.ncbi.nlm.nih.gov

Quality of drug information database research for clinical decision support. [2021]

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

Applying Implementation Science in Surgical Infection Quality Improvement. [2021]

12. England pubmed.ncbi.nlm.nih.gov

e-Health? Clinical information network interest and impediments in a community paediatric setting. [2021]

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

Identifying barriers to the effective use of clinical reminders: bootstrapping multiple methods. [2018]