Presentation Details
| A Novel Pre-Operative Predictive Triage Tool: Stratifying Patients to the Right Preoperative and Surgical Pavilions Renuka Shenoy, Michael Guertin, Poorvi Hardman, Jarrett Heard, Barbara Rogers, Sheila Heising, Julie Lewis, William Falk. The Ohio State University Wexner Medical Center, Columbus, OH, USA |
Abstract
BACKGROUND: With increasing patient complexity including multimorbidity, polypharmacy, and frailty, the need for efficient preoperative assessment is critical to ensure safety, throughput, and satisfaction. These assessments reduce delays, cancellations and associated costs, and improve scheduling/utilization. While telehealth has improved access for low-risk patients, duplicative workflows occur when telehealth is followed by in-person visits. Our institution developed and implemented a predictive scoring tool that uses patient medical history and surgical risk to identify patients needing in-person evaluation while streamlining others through telehealth. This reduces redundancies and expedites the in-person assessments for those who need it.
PURPOSE: Two iterations of this tool were created: a manual iteration which then led to the development of an EPIC based automated score. A group of perioperative experts created a list of comorbidities with assigned point values, including objective variables such as valve area for aortic and mitral stenosis and RVSP cut off on ECHO for pulmonary hypertension. This list represented high risk conditions that would necessitate a more thorough in-person evaluation. We partnered with our IHIS build team to extract discrete data from patients’ charts to create a score that was representative of comorbid conditions. We reviewed CPT codes to stratify surgeries/procedures into low risk, intermediate risk, or high-risk categories. A numerical value was assigned.
RESULTS: With the manual iteration, only 7% of patients were being routed to a telehealth appointment even if their score qualified them for a telehealth appointment. We are in the process of collecting prospective data on the predictive scoring tool.
CONCLUSIONS: The predictive tool decreases preoperative redundancy. Stratifying patients by comorbidities and surgical risk reduces duplicate appointments and improves efficiency. The tool enables prompt scheduling for high-risk patients while preserving telehealth access for others. Future plans include integrating the risk score into a readmission risk model to guide surgical pavilion placement: outpatient with or without extended recovery, or inpatient admission.
No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author.
PURPOSE: Two iterations of this tool were created: a manual iteration which then led to the development of an EPIC based automated score. A group of perioperative experts created a list of comorbidities with assigned point values, including objective variables such as valve area for aortic and mitral stenosis and RVSP cut off on ECHO for pulmonary hypertension. This list represented high risk conditions that would necessitate a more thorough in-person evaluation. We partnered with our IHIS build team to extract discrete data from patients’ charts to create a score that was representative of comorbid conditions. We reviewed CPT codes to stratify surgeries/procedures into low risk, intermediate risk, or high-risk categories. A numerical value was assigned.
RESULTS: With the manual iteration, only 7% of patients were being routed to a telehealth appointment even if their score qualified them for a telehealth appointment. We are in the process of collecting prospective data on the predictive scoring tool.
CONCLUSIONS: The predictive tool decreases preoperative redundancy. Stratifying patients by comorbidities and surgical risk reduces duplicate appointments and improves efficiency. The tool enables prompt scheduling for high-risk patients while preserving telehealth access for others. Future plans include integrating the risk score into a readmission risk model to guide surgical pavilion placement: outpatient with or without extended recovery, or inpatient admission.
No part of this publication may be reproduced, distributed, or transmitted in any form or by any means, including photocopying, recording, or other electronic or mechanical methods, without the prior written permission of the author.
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