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ML-based report card for Surgery Complications

N/A

Recruiting

Led By Christopher R King, MD, PhD

Research Sponsored by Washington University School of Medicine

Eligibility Criteria Checklist

Specific guidelines that determine who can or cannot participate in a clinical trial

Must have

Planned non-ICU disposition ("floor" and "observation unit" collectively "ward" patients).

Be older than 18 years old

Timeline

Screening 3 weeks

Treatment Varies

Follow Up 8 hours postop

Awards & highlights

Study Summary

This trial will study whether showing report cards with machine-learned risk profiles to postoperative providers can help interpretation, communication, and workflow.

Who is the study for?

This trial is for surgeons in the Acute and Critical Care Surgery division or those with patients in the 16300 observation unit. It's specifically for a subset of TECTONICS study participants who are adults undergoing surgery at BJH South campus, excluding certain suites, and not planned for ICU after surgery.Check my eligibility

What is being tested?

The study tests how useful ML-based report cards are when given to postoperative providers. These report cards show risk profiles created from data collected before and during surgery to see if they help with understanding patient risks, fitting into workflow, and improving communication.See study design

What are the potential side effects?

Since this trial involves an information tool rather than a medical treatment, there aren't direct physical side effects like you'd expect with drugs or surgeries. However, it could impact decision-making processes in postoperative care.

Eligibility Criteria

Inclusion Criteria

You may be eligible if you check “Yes” for the criteria below

Select...

I am planned to be cared for in a regular hospital unit, not intensive care.

Timeline

Screening ~ 3 weeks3 visits

Treatment ~ Varies

Follow Up ~ 8 hours postop

Screening ~ 3 weeks

Treatment ~ Varies

Follow Up ~8 hours postop

This trial's timeline: 3 weeks for screening, Varies for treatment, and 8 hours postop for reporting.

Treatment Details

Study Objectives

Outcome measures can provide a clearer picture of what you can expect from a treatment.

Primary outcome measures

Provider self-reported handoff effectiveness.

Secondary outcome measures

Direct observation of handoff

Provider information value of ML report card

Workflow effectiveness of the interventions

Trial Design

2Treatment groups

Experimental Treatment

Active Control

Group I: InterventionExperimental Treatment1 Intervention

ML will be used to create a report card for each patient that summarizes the preoperative assessment and intraoperative data. Report card data will be made available to providers through multiple methods: integration into electronic health records workflows, electronic health records notifications, mobile device notifications, and print outs in the paper chart

Group II: Pre-interventionActive Control1 Intervention

The standard of care. The report card will be electronically generated (to determine eligibility) but it will not be visible to clinicians.

0 / 1Eligibility criteria met