Evidence map›Paper›PMID 30792011›Full record

Trial reportSurgery2019

Comparing clinical judgment with the MySurgeryRisk algorithm for preoperative risk assessment: A pilot usability study.

Meghan Brennan, Sahil Puri, Tezcan Ozrazgat-Baslanti, Zheng Feng, Matthew Ruppert, Haleh Hashemighouchani, Petar Momcilovic, Xiaolin Li, Daisy Zhe Wang, Azra Bihorac

Open access · greenAbstract readClinical TrialComparative Study
PubMed Full text at PMC Publisher
In one paragraph

Trial report in Surgery, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 62 papers, 6 of them syntheses that pooled it.

0numbers the graph read from it
0cells of the map it votes in
62citing papers in PubMed, 6 pooled it
6.9field-weighted citation impact, top 2% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

62 citing papers in PubMed, 6 syntheses or guidelines pooled it, 124 citations in OpenAlex.

  1. Pooled it
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  13. Artificial intelligence in orthopaedics: Enhanced examinations, ambient intelligence and the future of clinical practice.Knee surgery, sports traumatology, arthroscopy : official journal of the ESSKA · 2026
    Review
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  20. German surgeons' perspective on the application of artificial intelligence in clinical decision-making.International journal of computer assisted radiology and surgery · 2025
    Article

2 more citing papers are in PubMed but not listed here.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

10 authors at 1 institution in 1 country.

Meghan BrennanPrecision and Intelligent Systems in Medicine (PRISMA(P)), Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville; Department of Anesthesiology, University of Florida College of Medicine, Gainesville.
Sahil PuriDepartment of Computer and Information Science and Engineering, University of Florida Herbert Wertheim College of Engineering, Gainesville.
Tezcan Ozrazgat-BaslantiPrecision and Intelligent Systems in Medicine (PRISMA(P)), Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville; Department of Medicine, University of Florida College of Medicine, Gainesville.
Zheng FengPrecision and Intelligent Systems in Medicine (PRISMA(P)), Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville; Department of Electrical and Computer Engineering, University of Florida Herbert Wertheim College of Engineering, Gainesville.
Matthew RuppertPrecision and Intelligent Systems in Medicine (PRISMA(P)), Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville; Department of Medicine, University of Florida College of Medicine, Gainesville.
Haleh HashemighouchaniPrecision and Intelligent Systems in Medicine (PRISMA(P)), Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville; Department of Medicine, University of Florida College of Medicine, Gainesville.
Petar MomcilovicPrecision and Intelligent Systems in Medicine (PRISMA(P)), Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville; Department of Industrial and Systems Engineering, University of Florida Herbert Wertheim College of Engineering, Gainesville.
Xiaolin LiPrecision and Intelligent Systems in Medicine (PRISMA(P)), Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville; Department of Electrical and Computer Engineering, University of Florida Herbert Wertheim College of Engineering, Gainesville.
Daisy Zhe WangPrecision and Intelligent Systems in Medicine (PRISMA(P)), Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville; Department of Computer and Information Science and Engineering, University of Florida Herbert Wertheim College of Engineering, Gainesville.
Azra BihoracPrecision and Intelligent Systems in Medicine (PRISMA(P)), Division of Nephrology, Hypertension and Transplantation, University of Florida, Gainesville; Department of Medicine, University of Florida College of Medicine, Gainesville. Electronic address: abihorac@ufl.edu.
University of Florida · US

Funding

Using social networks to map and evaluate team science across CTSA hubsUL1TR001427 · NCATS · UNIVERSITY OF FLORIDA · PI MITCHELL, DUANE A. · 2015 to 2024
$37.2M
UF Clinical and Translational Science AwardUL1TR000064 · NCATS · UNIVERSITY OF FLORIDA · PI NELSON, DAVID R · 2012 to 2014
$12.2M
PICS: A New Horizon for Surgical Critical CareP50GM111152 · NIGMS · UNIVERSITY OF FLORIDA · PI MOORE, FREDERICK A · 2014 to 2018
$10.7M
Integrating data, algorithms and clinical reasoning for surgical risk assessmentR01GM110240 · NIGMS · UNIVERSITY OF FLORIDA · PI BIHORAC, AZRA, RASHIDI, PARISA · 2016 to 2025
$5.1M
NCATS NIH HHS UL1 TR000064NCATS NIH HHS UL1 TR001427NIGMS NIH HHS P50 GM111152NIGMS NIH HHS R01 GM110240
6 · The paper itself

Abstract

backgroundMajor postoperative complications are associated with increased cost and mortality. The complexity of electronic health records overwhelms physicians' abilities to use the information for optimal and timely preoperative risk assessment. We hypothesized that data-driven, predictive-risk algorithms implemented in an intelligent decision-support platform simplify and augment physicians' risk assessments.

methodsThis prospective, nonrandomized pilot study of 20 physicians at a quaternary academic medical center compared the usability and accuracy of preoperative risk assessment between physicians and MySurgeryRisk, a validated, machine-learning algorithm, using a simulated workflow for the real-time, intelligent decision-support platform. We used area under the receiver operating characteristic curve to compare the accuracy of physicians' risk assessment for six postoperative complications before and after interaction with the algorithm for 150 clinical cases.

resultsThe area under the receiver operating characteristic curve of the MySurgeryRisk algorithm ranged between 0.73 and 0.85 and was significantly better than physicians' initial risk assessments (area under the receiver operating characteristic curve between 0.47 and 0.69) for all postoperative complications except cardiovascular. After interaction with the algorithm, the physicians significantly improved their risk assessment for acute kidney injury and for an intensive care unit admission greater than 48 hours, resulting in a net improvement of reclassification of 12% and 16%, respectively. Physicians rated the algorithm as easy to use and useful.

conclusionImplementation of a validated, MySurgeryRisk computational algorithm for real-time predictive analytics with data derived from the electronic health records to augment physicians' decision-making is feasible and accepted by physicians. Early involvement of physicians as key stakeholders in both design and implementation of this technology will be crucial for its future success.

Indexed as

Clinical CompetenceDecision Support TechniquesAdultAgedClinical Decision-MakingFeasibility StudiesFemaleHumansJudgmentMachine LearningMaleMiddle AgedPilot ProjectsPostoperative ComplicationsPreoperative CareProspective Studies

Identifiers

PMID30792011
PMCPMC6502657
OpenAlexW2797775685

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.