Decision Support Software for Wounds and Injuries

Overseen By Iain Kehoe

Age: 18+

Sex: Any

Trial Phase: Phase 1

Sponsor: Andrew Tomas Reisner

Disqualifiers: Prisoners, Pregnant, EFIC Opt-Out, others

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

Compensation: Varies

Number of Visits: Unknown

Duration: 30 minutes per session

20 Participants Needed

Trial Summary

What is the purpose of this trial?

This is a pilot evaluation of the APPRAISE trauma decision-support software system ("the System"). The specific objections are as follows: 1. Evaluate the robustness of the System (i.e., whether the software performs in real-time in accordance with a priori technical specifications during real-time clinical use); 2. Evaluate whether the real-time display of the System causes distraction or confusion to clinicians treating the trauma patient such that its risks exceed its benefits; 3. Collect pilot data to allow for a statistical power analysis to design a future clinical trial evaluating efficacy.

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 APPRAISE Trauma Clinical Decision Support System?

Clinical decision support systems, like the APPRAISE Trauma Clinical Decision Support System, help healthcare providers make better decisions by using computerized information to follow evidence-based guidelines, which can improve patient care and outcomes, especially in complex situations like trauma management. ^{1 2 3 4 5}

How does the Decision Support Software for Wounds and Injuries treatment differ from other treatments for this condition?

This treatment is unique because it uses a computerized decision support system to assist healthcare providers in making informed decisions about wound and injury management. Unlike traditional treatments, it leverages artificial intelligence to predict critical situations and suggest appropriate next steps, enhancing the accuracy and efficiency of patient care. ^{1 2 5 6 7}

Research Team

Andrew Reisner, MD

Principal Investigator

Mass. General Hospital

Eligibility Criteria

This trial is for adults (18+) who arrive at the Emergency Department and are triaged to the 'Acute' area due to concerns of acute injury. This could be because they've reported an injury or the clinical team suspects one based on their symptoms.

Inclusion Criteria

I was directed to the critical care section of the emergency department.

I am being evaluated for a recent injury.

I am 18 years or older and currently in the emergency department.

Exclusion Criteria

Patients known to be pregnant

Prisoners

Patients wearing an 'EFIC Opt-Out' bracelet

Timeline

Screening

Participants are screened for eligibility to participate in the trial

1 week

Treatment

Evaluation of the APPRAISE trauma decision-support software system during real-time clinical use

30 minutes per session

Follow-up

Participants are monitored for safety and effectiveness after treatment

1 week

Treatment Details

Interventions

• APPRAISE Trauma Clinical Decision Support System

Trial Overview The APPRAISE Trauma Clinical Decision Support System is being tested. It's a software designed to help clinicians make decisions in treating trauma patients, and this trial checks if it works as intended without distracting doctors.

Participant Groups

Group I: APPRAISE cases Experimental Treatment 1 Intervention

Trauma patients for which APPRAISE system was used

Who Is Running the Clinical Trial?

Andrew Tomas Reisner

Lead Sponsor

Trials

2

Recruited

40+

Findings from Research

Clinical decision support systems, like Project IMPACT and APACHE III, are essential for managing patient outcomes in critical care by enabling data analysis and predictive modeling.

These tools help clinicians compare actual patient outcomes with predicted outcomes, which can improve quality control and process improvement in healthcare settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Clinical decision support systems for outcome measurement and management. Sakallaris, BR., Jastremski, CA., Von Rueden, KT.[2019]

Computerized clinical decision support (CCDS) can effectively implement and refine evidence-based guidelines (EBGs) in healthcare, helping to ensure that patients receive appropriate care based on proven therapies.

By utilizing CCDS, clinicians can decrease variability in patient care, test new interventions, and validate improvements in the quality of care, particularly in complex environments like intensive care units.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Computerized clinical decision support: a technology to implement and validate evidence based guidelines. Sucher, JF., Moore, FA., Todd, SR., et al.[2022]

A dual-method usability evaluation combining user-driven and expert-driven approaches identified 79 usability issues in a rib fracture clinical decision support system, with experts uncovering 63% of the issues, including 58% of the severe ones, highlighting the importance of both perspectives in usability testing.

Five guiding themes for designing effective clinical decision support systems were identified: transparency, functionality and integration into workflow, automated and noninterruptive features, flexibility, and layout and appearance, which can enhance usability and adherence to evidence-based practices.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Combined Expert and User-Driven Usability Assessment of Trauma Decision Support Systems Improves User-Centered Design. Jones, EK., Hultman, G., Schmoke, K., et al.[2022]

A clinical decision support system (CDSS) was developed to assist clinicians in managing post-cardiac surgery patients by modeling decision-making strategies based on key cardiovascular parameters like blood pressure and cardiac output.

The CDSS successfully replicated expert anesthetists' therapeutic interventions in simulated patient scenarios, effectively controlling hemodynamic parameters to target values, demonstrating its potential efficacy in clinical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part I: Physiological modelling and decision support system design. Denaï, MA., Mahfouf, M., Ross, JJ.[2015]

Computer-assisted decision-making systems have the potential to improve outcomes for trauma patients by providing real-time predictions and management suggestions based on continuously acquired patient data.

Despite their promise, widespread implementation of these systems faces challenges, including technological barriers, data protection regulations, and the need for large multicenter studies to validate their effectiveness.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

[Computer-assisted decision-making for trauma patients]. Osterhoff, G., Pförringer, D., Scherer, J., et al.[2020]

The study introduces LogReg, a handheld decision-support system designed to enhance clinical decision-making at the point of care, addressing the challenge of needing computing power directly where patient interactions occur.

LogReg was successfully applied to predict clinical outcomes in crush injury cases, demonstrating its practical utility in real-world medical settings.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Decisions at hand: a decision support system on handhelds. Zupan, B., Porenta, A., Vidmar, G., et al.[2008]

A computerized decision support system was developed to assist ATLS-trained surgeons in managing patients with penetrating abdominal injuries, showing promise in providing accurate advice on diagnoses and treatments.

In a comparison with 13 medical trainees, the expert system's management suggestions were ranked higher than those of individual trainees, indicating its potential to enhance decision-making in trauma care.

NCBI (Pubmed)

pubmed.ncbi.nlm.nih.gov

Artificial intelligence: a computerized decision aid for trauma. Clarke, JR., Cebula, DP., Webber, BL.[2019]

References

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

Clinical decision support systems for outcome measurement and management. [2019]

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

Computerized clinical decision support: a technology to implement and validate evidence based guidelines. [2022]

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

Combined Expert and User-Driven Usability Assessment of Trauma Decision Support Systems Improves User-Centered Design. [2022]

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

A hybrid hierarchical decision support system for cardiac surgical intensive care patients. Part I: Physiological modelling and decision support system design. [2015]

5. Germany pubmed.ncbi.nlm.nih.gov

[Computer-assisted decision-making for trauma patients]. [2020]

6. Netherlands pubmed.ncbi.nlm.nih.gov

Decisions at hand: a decision support system on handhelds. [2008]

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

Artificial intelligence: a computerized decision aid for trauma. [2019]