Literature DB >> 30149939

Eye of the beholder: Risk calculators and barriers to adoption in surgical trainees.

Ira L Leeds1, Andrew J Rosenblum2, Paul E Wise3, Anthony C Watkins4, Matthew I Goldblatt5, Elliott R Haut6, Jonathan E Efron1, Fabian M Johnston7.   

Abstract

BACKGROUND: Accurate risk assessment before surgery is complex and hampered by behavioral factors. Underutilized risk-based decision-support tools may counteract these barriers. The purpose of this study was to identify perceptions of and barriers to the use of surgical risk-assessment tools and assess the importance of data framing as a barrier to adoption in surgical trainees.
METHODS: We distributed a survey and risk assessment activity to surgical trainees at four training institutions. The primary outcomes of this study were descriptive risk assessment practices currently performed by residents, identifiable influences and obstacles to adoption, and the variability of preference sets when comparing modified System Usability Scores of a current risk calculator to a purpose-built calculator revision. Risk calculator comparison responses were compared with simple and multivariable regression to identify predictors for preferentiality.
RESULTS: We collected responses from 124 surgical residents (39% response rate). Participants endorsed familiarity with direct verbal communication (100%), sketch diagrams (87%), and brochures (59%). The most contemporary risk communication frameworks, such as best-worst case scenario framing (38%), case-specific risk calculators (43%), and all-procedure calculators (52%) were the least familiar. Usage favored traditional models of communication with only 26% of residents regularly using a strategy other than direct verbal discussion or anatomic sketch diagrams. Barriers limiting routine use included lack of electronic and clinical workflow integration. The mean modified System Usability Scores domain scores were widely dispersed for all domains, and no domain demonstrated one calculator's superiority over another.
CONCLUSION: Risk assessment tools are underutilized by trainees. Of importance, preference sets of clinicians appear to be unpredictable and may benefit more from a customizable, bespoke approach.
Copyright © 2018 Elsevier Inc. All rights reserved.

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Mesh:

Year:  2018        PMID: 30149939     DOI: 10.1016/j.surg.2018.07.002

Source DB:  PubMed          Journal:  Surgery        ISSN: 0039-6060            Impact factor:   3.982


  12 in total

1.  Evaluating Discrimination of ACS-NSQIP Surgical Risk Calculator in Thyroidectomy Patients.

Authors:  Vivian Hsiao; Dawn M Elfenbein; Susan C Pitt; Kristin L Long; Rebecca S Sippel; David F Schneider
Journal:  J Surg Res       Date:  2021-12-10       Impact factor: 2.192

Review 2.  Artificial intelligence-enabled decision support in nephrology.

Authors:  Tyler J Loftus; Benjamin Shickel; Tezcan Ozrazgat-Baslanti; Yuanfang Ren; Benjamin S Glicksberg; Jie Cao; Karandeep Singh; Lili Chan; Girish N Nadkarni; Azra Bihorac
Journal:  Nat Rev Nephrol       Date:  2022-04-22       Impact factor: 42.439

3.  Performance of a Machine Learning Algorithm Using Electronic Health Record Data to Predict Postoperative Complications and Report on a Mobile Platform.

Authors:  Yuanfang Ren; Tyler J Loftus; Shounak Datta; Matthew M Ruppert; Ziyuan Guan; Shunshun Miao; Benjamin Shickel; Zheng Feng; Chris Giordano; Gilbert R Upchurch; Parisa Rashidi; Tezcan Ozrazgat-Baslanti; Azra Bihorac
Journal:  JAMA Netw Open       Date:  2022-05-02

4.  Intelligent, Autonomous Machines in Surgery.

Authors:  Tyler J Loftus; Amanda C Filiberto; Jeremy Balch; Alexander L Ayzengart; Patrick J Tighe; Parisa Rashidi; Azra Bihorac; Gilbert R Upchurch
Journal:  J Surg Res       Date:  2020-04-24       Impact factor: 2.192

Review 5.  Artificial Intelligence and Surgical Decision-making.

Authors:  Tyler J Loftus; Patrick J Tighe; Amanda C Filiberto; Philip A Efron; Scott C Brakenridge; Alicia M Mohr; Parisa Rashidi; Gilbert R Upchurch; Azra Bihorac
Journal:  JAMA Surg       Date:  2020-02-01       Impact factor: 14.766

6.  Decision analysis and reinforcement learning in surgical decision-making.

Authors:  Tyler J Loftus; Amanda C Filiberto; Yanjun Li; Jeremy Balch; Allyson C Cook; Patrick J Tighe; Philip A Efron; Gilbert R Upchurch; Parisa Rashidi; Xiaolin Li; Azra Bihorac
Journal:  Surgery       Date:  2020-06-13       Impact factor: 3.982

7.  Optimizing predictive strategies for acute kidney injury after major vascular surgery.

Authors:  Amanda C Filiberto; Tezcan Ozrazgat-Baslanti; Tyler J Loftus; Ying-Chih Peng; Shounak Datta; Philip Efron; Gilbert R Upchurch; Azra Bihorac; Michol A Cooper
Journal:  Surgery       Date:  2021-02-27       Impact factor: 4.348

8.  A web-based fuzzy risk predictive-decision model of de novo stress urinary incontinence in women undergoing pelvic organ prolapse surgery.

Authors:  Seyyde Yalda Moosavi; Taha Samad-Soltani; Sakineh Hajebrahimi
Journal:  Curr Urol       Date:  2021-08-09

9.  Attitudes about use of preoperative risk assessment tools: a survey of surgeons and surgical residents in an academic health system.

Authors:  Nisha Pradhan; Adam R Dyas; Michael R Bronsert; Anne Lambert-Kerzner; William G Henderson; Howe Qiu; Kathryn L Colborn; Nicholas J Mason; Robert A Meguid
Journal:  Patient Saf Surg       Date:  2022-03-17

Review 10.  Aligning Patient Acuity With Resource Intensity After Major Surgery: A Scoping Review.

Authors:  Tyler J Loftus; Jeremy A Balch; Matthew M Ruppert; Patrick J Tighe; William R Hogan; Parisa Rashidi; Gilbert R Upchurch; Azra Bihorac
Journal:  Ann Surg       Date:  2022-02-01       Impact factor: 13.787

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