AI-Driven Clinical Decision Support for Surgery

(SPATH Trial)

Overseen By Sunny S Lou, MD, PhD

Age: 18+

Sex: Any

Trial Phase: Academic

Sponsor: Washington University School of Medicine

Disqualifiers: Pregnant, Red cell alloantibodies

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

Compensation: Varies

Number of Visits: 1

50 Participants Needed

What You Need to Know Before You Apply

What is the purpose of this trial?

This trial tests a smart system called S-PATH, a clinical decision support system, to determine if it can assist doctors in better preparing for blood transfusions during surgery. The goal is to ensure blood availability for those who truly need it, reducing waste and unnecessary costs. Participants will have their surgery planning guided either by S-PATH or by the usual methods doctors use. Patients undergoing surgery at Barnes Jewish Hospital who have completed a specific pre-surgery check-up might be suitable for this trial. As an unphased trial, this study offers patients the chance to contribute to innovative healthcare solutions that could enhance surgical outcomes.

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 the S-PATH clinical decision support system is safe for presurgical blood preparation?

Research has shown that tools like the S-PATH, known as clinical decision support systems (CDSS), assist doctors in making better medical decisions through technology. These systems have existed for some time and are generally safe, as they primarily offer information and suggestions rather than directly treating patients.

Studies have found that CDSS can enhance healthcare by providing doctors with helpful information during treatments, leading to improved patient care and fewer mistakes. These systems use advanced technology, often involving artificial intelligence, to predict patient needs or responses to treatment.

There are no known side effects or direct risks to patients from using a CDSS like S-PATH. These systems are designed to support doctors in decision-making. Based on available research, the S-PATH system is expected to be safe and well-tolerated for guiding surgical care decisions. ^{1 2 3 4 5}

Why are researchers excited about this trial?

Researchers are excited about the S-PATH clinical decision support system because it offers a fresh approach to surgical planning. Unlike the usual care, which relies on traditional schedules like the Maximum Surgical Blood Ordering Schedule (MSBOS), S-PATH employs artificial intelligence to tailor blood order decisions specifically for each patient. This AI-driven customization could lead to more efficient and safer surgeries by reducing unnecessary blood orders and optimizing resource use. By providing surgeons with precise, data-driven recommendations, S-PATH has the potential to significantly enhance surgical outcomes and resource management.

What evidence suggests that the S-PATH clinical decision support system is effective for presurgical blood preparation?

This trial will compare the S-PATH clinical decision support system with usual care for determining presurgical blood orders. Research has shown that clinical decision support systems (CDSS), such as S-PATH, help doctors adhere to medical guidelines and reduce medication errors, enhancing patient safety. Studies have found that CDSS can assist doctors in making better choices by providing key medical information. For instance, S-PATH can predict which patients might need a blood transfusion during surgery, ensuring the right amount of blood is ready, which reduces waste and costs. Overall, CDSS tools are linked to better healthcare outcomes, suggesting that S-PATH could improve surgical preparation and safety. ^{4 6 7 8 9}

Are You a Good Fit for This Trial?

This trial is for patients scheduled for surgery who may require blood transfusions. It's designed to help those who need efficient and accurate blood preparation before their operation, aiming to reduce waste and costs.

Inclusion Criteria

Patient evaluated in-person at one of the preoperative assessment clinics affiliated with BJC Healthcare

Patient has a valid S-PATH model prediction prior to their preoperative assessment clinic visit

I am scheduled for surgery at Barnes Jewish Hospital's main operating room.

Exclusion Criteria

Patient is pregnant

Patient has presence or history of red cell alloantibodies

Timeline for a Trial Participant

Screening

Participants are screened for eligibility to participate in the trial

2-4 weeks

Preoperative Assessment

Decision support system is used to determine presurgical blood orders during the preoperative clinic assessment visit

1 visit

1 visit (in-person)

Surgery

Blood transfusion management and monitoring during surgery, including type and screen orders and transfusion reactions

During surgery

Follow-up

Participants are monitored for transfusion reactions and other outcomes from time of surgery to hospital discharge or 30 days after surgery

Up to 30 days

What Are the Treatments Tested in This Trial?

Interventions

• S-PATH clinical decision support system

Trial Overview The study tests an AI system called S-PATH against usual care methods in managing presurgical blood orders. The goal is to see if S-PATH can better predict the need for transfusion, thus optimizing blood use.

How Is the Trial Designed?

Group I: S-PATH Experimental Treatment 1 Intervention

Access to the S-PATH clinical decision support system

Group II: Usual care Active Control 1 Intervention

Usual care for determining presurgical blood orders, including use of the institutional Maximum Surgical Blood Ordering Schedule (MSBOS)

Who Is Running the Clinical Trial?

Washington University School of Medicine

Lead Sponsor

Trials

2,027

Recruited

2,353,000+

National Heart, Lung, and Blood Institute (NHLBI)

Collaborator

Trials

3,987

Recruited

47,860,000+

Citations

1. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC11634111/

Outcomes of clinical decision support systems in real-world ... Most perioperative CDSS use was associated with improved guideline adherence, decreased medication errors, and some improvements in patient safety measures.

2. nature.com nature.com/articles/s41746-020-0221-y

An overview of clinical decision support systems: benefits ... A clinical decision support system (CDSS) is intended to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge.

3. i-jmr.org i-jmr.org/2024/1/e58036

Benefits of Clinical Decision Support Systems for the ... Conclusions: Overall, both EMR-integrated and stand-alone CDSSs showed positive results, suggesting their benefits to health care providers and ...

4. clinicaltrials.gov clinicaltrials.gov/study/NCT07223853

AI-driven Clinical Decision Support for Perioperative Blood ... This study aims to evaluate an intelligent clinical decision support system that helps clinicians prepare blood for patients who are likely to need it, while ...

5. journalofsurgicalresearch.com journalofsurgicalresearch.com/article/S0022-4804(25)00660-2/fulltext

Surgeon-Informed Clinical Decision Support Software for ... This modified-Delphi study provides critical insights into surgeon-specific preferences, feasibility considerations, and contextual factors ...

6. pmc.ncbi.nlm.nih.gov pmc.ncbi.nlm.nih.gov/articles/PMC7005290/

An overview of clinical decision support systems: benefits ... A clinical decision support system (CDSS) is intended to improve healthcare delivery by enhancing medical decisions with targeted clinical knowledge.

7. openheart.bmj.com openheart.bmj.com/content/10/2/e002432

Harnessing the power of clinical decision support systems This review aims to provide an overview of the current state of CDSS, discussing their development, implementation, benefits, limitations and future directions.

8. sciencedirect.com sciencedirect.com/science/article/pii/S1470211824024096

Decision time for clinical decision support systems Complex CDSSs use artificial intelligence, data mining or statistical methods to reason about the classification or prediction of a disease or patient state.

9. rethinkingclinicaltrials.org rethinkingclinicaltrials.org/chapters/conduct/real-world-evidence-clinical-decision-support/definitions-and-uses-for-cds/

Real World Evidence: Clinical Decision Support What Is Clinical Decision Support? CDS encompasses a wide variety of tools, such as alerts and reminders, clinical practice guidelines, customized order ...