Personalized CDS for Heart Failure

Overseen By Cathryn Perreira, MA

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: University of Colorado, Denver

Disqualifiers: Non-cardiology, Non-primary care, others

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

Compensation: Varies

Number of Visits: Unknown

1075 Participants Needed

Trial Summary

What is the purpose of this trial?

Clinical decision support (CDS) tools can 'nudge' clinicians to make the best decisions easy. Although required by "meaningful use" regulations, more than 40% of CDS lead to no change and the remaining lead to improvements that are modest at best. This is because CDS tools often ignore contextual factors and present irrelevant information. Although many tools have undergone patient-specific optimization, 'traditional CDS' are rarely clinician-specific. For example, a traditional CDS tool for beta blockers and heart failure with reduced ejection fraction (HFrEF) addresses common prescribing misconceptions by stating asthma is not a contraindication and providing a safe threshold for blood pressure. For clinicians without these misconceptions, these statements are irrelevant and distract from key information. A 'personalized CDS' would evaluate clinician past prescribing patterns to determine whether prescribing misconceptions might exist and then conditionally present information to address those misconceptions. The objective of this research is to create personalized clinician-specific CDS that overcome shortcomings of traditional CDS. The central hypothesis is a personalized CDS that minimizes irrelevant information will lead to a higher rate of prescribing guideline-directed management and therapy (GDMT) for HFrEF compared to a traditional CDS.

Will I have to stop taking my current medications?

The trial information does not specify whether participants need to stop taking their current medications.

What data supports the effectiveness of the treatment Personalized Clinical Decision Support (CDS) for Heart Failure?

Research shows that Clinical Decision Support (CDS) systems can help doctors manage chronic heart failure by providing timely and relevant information, which can improve patient care. Additionally, CDS tools designed with input from physicians can assist in making better treatment decisions in emergency settings. ^{1 2 3 4 5}

Is Personalized CDS for Heart Failure safe for humans?

Clinical decision support systems (CDSS) have been shown to help reduce medication errors and adverse drug events, which suggests they are generally safe. However, poor design of these systems can introduce new errors, so it's important that they are well-designed and tested. ^{1 3 6 7 8}

How is the Personalized Clinical Decision Support (CDS) treatment for heart failure different from other treatments?

The Personalized Clinical Decision Support (CDS) treatment for heart failure is unique because it uses software to help doctors make better decisions by providing personalized recommendations based on a patient's specific health information, which can improve care and reduce errors compared to traditional methods. ^{3 4 9 10 11}

Research Team

Katy E Trinkley, PharmD, PhD

Principal Investigator

University of Colorado, Denver

Eligibility Criteria

This trial is for clinicians with prescribing privileges at UCHealth's outpatient cardiology or primary care clinics. It aims to observe and improve their prescribing behaviors, especially in the context of heart failure treatment.

Inclusion Criteria

Study subjects are potential users of the CDS, specifically clinicians with prescribing privileges who practice at one of the health system's (UCHealth) outpatient cardiology or primary care clinics. The study also evaluates patient characteristics that could influence their prescribing decisions.

Exclusion Criteria

Clinicians who do not practice in cardiology or primary care clinics or do not practice within UCHealth system.

Timeline

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Intervention

Implementation of personalized and traditional clinical decision support (CDS) tools to evaluate their effectiveness in prescribing guideline-directed management and therapy (GDMT) for heart failure with reduced ejection fraction (HFrEF)

6 months

Follow-up

Participants are monitored for the effectiveness of CDS tools in improving prescription practices

4 weeks

Treatment Details

Interventions

• Personalized Clinical Decision Support (CDS)
• Traditional Clinical Decision Support (CDS)

Trial Overview The study compares two types of Clinical Decision Support (CDS) tools: a 'personalized CDS' tailored to individual clinician's past behavior and misconceptions, versus a 'traditional CDS' that provides general guidance on heart failure prescriptions.

Participant Groups

Group I: Traditional Clinical Decision Support (CDS) Experimental Treatment 1 Intervention

Group II: Personalized Clinical Decision Support (CDS) Experimental Treatment 1 Intervention

Who Is Running the Clinical Trial?

University of Colorado, Denver

Lead Sponsor

Trials

1,842

Recruited

3,028,000+

Findings from Research

Over a 30-month period, two organizations collected extensive user feedback on clinical decision support (CDS) tools, receiving 2,639 Likert feedback comments and 623,270 override comments, highlighting the importance of user input in improving these systems.

The feedback led to rapid responses to build errors and helped identify issues related to knowledge management and user interface, demonstrating that incorporating user suggestions can significantly enhance the effectiveness of CDS tools.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Real-Time User Feedback to Support Clinical Decision Support System Improvement. Rubins, D., McCoy, AB., Dutta, S., et al.[2023]

The integrated clinical prediction rule (iCPR) project demonstrated that a novel form of clinical decision support (CDS) can be effectively integrated into electronic health records, leading to a 57.4% adoption rate among users and a 42.7% acceptance rate of its recommendations.

Key factors for the success of the iCPR included strong leadership support, dedicated training for users, and the implementation of context-sensitive triggers that prompted CDS interventions based on specific workflow events.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A Framework for Usable and Effective Clinical Decision Support: Experience from the iCPR Randomized Clinical Trial. Kannry, J., McCullagh, L., Kushniruk, A., et al.[2020]

A clinical decision support software (CDSS) for treating chronic heart failure was developed through a collaborative process and evaluated over six months, showing that 70% of participants found it more useful than traditional paper guidelines.

The evaluation revealed that general practitioners had lower computer literacy compared to junior doctors and medical students, which may hinder the CDSS's implementation; thus, improving computer skills and integrating the CDSS into clinical workflows could enhance its usage.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical decision support software for management of chronic heart failure: development and evaluation. Leslie, SJ., Hartswood, M., Meurig, C., et al.[2022]

A study involving 51 emergency department physicians revealed that most believe clinical decision support (CDS) could enhance patient care for heart failure, but many expressed concerns about their ability to consistently follow its recommendations due to workload issues.

Physicians suggested improvements for the CDS, including better accessibility and integration into their workflow, and preferred a system that encourages interaction without making it mandatory.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Physicians' Perceptions of Clinical Decision Support to Treat Patients With Heart Failure in the ED. Casey, SD., Reed, ME., LeMaster, C., et al.[2023]

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]

Clinical decision support (CDS) tools significantly improve the prescription of appropriate medications and dosages, with a pooled risk ratio of 1.48 based on 6 studies, indicating they are effective at the point of medication ordering or dispensing.

CDS tools also enhance laboratory monitoring and treatment responses to abnormal test results, showing a pooled risk ratio of 1.40, but evidence for their effectiveness in reducing adverse drug events remains inconsistent, suggesting further research is needed.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

The Effect of Laboratory Test-Based Clinical Decision Support Tools on Medication Errors and Adverse Drug Events: A Laboratory Medicine Best Practices Systematic Review. Whitehead, NS., Williams, L., Meleth, S., et al.[2022]

A study analyzing the usability of clinical decision support systems (CDSS) for community-acquired pneumonia and neutropenic fever found 422 recorded events, including 3 mistakes that could have been life-threatening, highlighting the critical need for careful design and evaluation of these systems.

Incorporating usability engineering principles early in the design of CDSS can help identify interface issues that may lead to adverse medical events, emphasizing the importance of user-friendly software in improving patient safety.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

How usability of a web-based clinical decision support system has the potential to contribute to adverse medical events. Graham, TA., Kushniruk, AW., Bullard, MJ., et al.[2022]

The implementation of a clinical decision support system (CDSS) significantly reduced the rate of excessive prescriptions from 4.5% to 3.6% over a 90-day study, indicating improved prescribing quality.

Physicians responded to about 25% of the alerts generated by the CDSS, with no new prescription errors occurring, suggesting that the system effectively guided safe medication practices without introducing additional risks.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Patient-specific electronic decision support reduces prescription of excessive doses. Seidling, HM., Schmitt, SP., Bruckner, T., et al.[2012]

The CAD/DM Smart Form is a Clinical Decision Support System designed to enhance the management of chronic diseases like coronary artery disease and diabetes mellitus.

A pilot study indicates that this Smart Form could significantly improve patient care, suggesting its effectiveness in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Improving management of chronic diseases with documentation-based clinical decision support: results of a pilot study. Schnipper, JL., McColgan, KE., Linder, JA., et al.[2019]

Clinical decision support software (CDSS) can enhance the treatment of chronic heart failure by providing evidence-based guidelines, but its effectiveness is limited by low computer skills among family physicians and the software's inability to address nonmedical patient needs.

To improve the implementation of CDSS in chronic heart failure management, strategies such as enhancing computer skills among healthcare providers and integrating CDSS into existing referral pathways are recommended.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical decision support software for chronic heart failure. Leslie, SJ., Denvir, MA.[2007]

The study highlights the importance of trust in the adoption of standards-based clinical decision support (CDS) tools, which can be enhanced through transparency, expert collaboration, and user feedback.

While standards-based CDS resources have been developed, challenges remain in their broad implementation, particularly for lower-resourced health systems that require additional support to effectively utilize these tools.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Lessons Learned from a National Initiative Promoting Publicly Available Standards-Based Clinical Decision Support. Dhopeshwarkar, RV., Freij, M., Callaham, M., et al.[2023]

References

1. Germany pubmed.ncbi.nlm.nih.gov

Real-Time User Feedback to Support Clinical Decision Support System Improvement. [2023]

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

A Framework for Usable and Effective Clinical Decision Support: Experience from the iCPR Randomized Clinical Trial. [2020]

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

Clinical decision support software for management of chronic heart failure: development and evaluation. [2022]

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

Physicians' Perceptions of Clinical Decision Support to Treat Patients With Heart Failure in the ED. [2023]

5. England pubmed.ncbi.nlm.nih.gov

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

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

The Effect of Laboratory Test-Based Clinical Decision Support Tools on Medication Errors and Adverse Drug Events: A Laboratory Medicine Best Practices Systematic Review. [2022]

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

How usability of a web-based clinical decision support system has the potential to contribute to adverse medical events. [2022]

8. England pubmed.ncbi.nlm.nih.gov

Patient-specific electronic decision support reduces prescription of excessive doses. [2012]

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

Improving management of chronic diseases with documentation-based clinical decision support: results of a pilot study. [2019]

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

Clinical decision support software for chronic heart failure. [2007]

11. Germany pubmed.ncbi.nlm.nih.gov

Lessons Learned from a National Initiative Promoting Publicly Available Standards-Based Clinical Decision Support. [2023]