Inpatient Clinical Care Teams General Use Tools Such as Bed Alarms, Gait Belts, Closer Nursing Station Placement and More to Prevent Patient Falls.
But there interventions can hinder clinical workflows and alert fatigue can make them less effective than they’re meant to be.
To help ensure only high-Risk Patients Receive Intensive Precaities, and Clinicians’ Alarm Fatigue is reduced, aurora, colorado-based uchealth have develooped a novel useer interactions that are found Specific Fall Interventions Based on a Patient’s Unique Risk Profile.
The tool uses mobileity data, behavioral health indicators and other risk factor to predict the risk of innovative falls with injury and leverage cloud tools to Integrate the Predicts Right Into The EPIC Electronic Health records.
The strategy takes “a great deal of data being entered into the ehr in other work streams and clearing out the noise to brings the clinician a clea Inpatient operations, and brittany cyriacks, a clinical informaticist at the health system.
We Caught Up with Drew and Cyriacks About their Efforts to Leverage Artificial Intelligence and Cloud Tools. They’ll explain more during an education session at Himss25 in las vegas next month.
Q. How was the model created, and what are its key objectives?
A. Our Multidisciplinary Team Conducted a Literature Review that Identated Risk Variables – 12 Risk Domains, 92 Potential Variables – and Mapped Them to EPIC DATA Elements. They sampled data from more than 181,000 inpatient admissions, include more than 200 features per admission.
Initially, both xgboost and a regularized logistic regression model was tested. Ultimately, Top Features were re-enginered and combined into a Simplified Logistic Regression Model.
The model runs every four hours, ingesting the most recent clinical documentation. Rather than a simple high/low indicator, a three-tied classification-Highest Risk, Elevated Risk and Universal Risk-Gives More Clinically Actionable Results.
Classification Generates A List of Risk-Level and Patient-Specific Precattions that Can Keep Patients Safe.
Q. What was some challenges of integrating the model into clinical workflows?
A. The team Needed to Define Clear, Tiered Precautions (EG, ‘Highest Risk’ Patients Should have Chair/BED Alarms, Be Kept With With With ARM’s Reach, etc. Each Risk Level.
In addition to standardizing precattions, the presentation will also also highlight the following key challenges:
- Meaningful Display of Risk: Merely showing a numeric score was not sufficient. Clinicians needed to see why a patient was flagged as high risk and which precautions were recommended.
- Data availability in the first 12 hours: the model requires Sufficient Clinical data entry and existence. During the first 12 hours, risk categorizations uncomplete, so staff must exercise clinical judgment to decide if the patient benefit from implements or admissions Precautions without relaying soly on the model.
- User Trust and Adoption: ENSURING Nurses and Other Care Team Members Trusted The Ai recommendations was critical. Ongoing Communication, Training and a User-Friendly Interface Helped Build Confidence. Data Surrounding Fall events have helped drive the conversation Away from Trust in the Ai Recommendation and More Towards Barriers to Implementing Precautions for the Patient.
- Workflow Integration: Rather than adding an extra step for clinicians, the model’s outputs and recommended precattions were built into the existing EPIC Flowsheets, Pop -ups and Care Plans SO Staffou Use them naturally.
Q. will the session offer insurance into lessons learned using ai to Reduce Fall Injury Risks?
A. Yes. The presentation include strategies for the successful adoption of predictive models, measuring model performance, addressing disparities among different population (Spanish-SPEAPLENTS, Forceaking Patients) Ensuring Continuous Improvement.
It also highlights Real-WORLD ‘What we have Learned So far’ Themes, Offering Attendes Practical Takeaways on How to Deploy, Monitor and Refine a tools for Fall Prevention. We will emphasize the engagement that is needed by the organization and nurses along the way.
Drew and Cyriacks’ Session, “Fall Injury Risk Model: From Ai To Clinical Interventions,” Is Scheduled For Tuesday, March 4, from 3: 15-4: 15 PM at Heimss25.
