Name
Title | ||
|---|---|---|
Comparing Clinical Judgment with MySurgeryRisk Algorithm for Preoperative Risk Assessment: A Pilot Study. |
Abstract | ||
|---|---|---|
Background: Major postoperative complications are associated with increased short and long-term mortality, increased healthcare cost, and adverse long-term consequences. The large amount of data contained in the electronic health record (EHR) creates barriers for physicians to recognize patients most at risk. We hypothesize, if presented in an optimal format, information from data-driven predictive risk algorithms for postoperative complications can improve physician risk assessment. Methods: Prospective, non-randomized, interventional pilot study of twenty perioperative physicians at a quarterly academic medical center. Using 150 clinical cases we compared physiciansu0027 risk assessment before and after interaction with MySurgeryRisk, a validated machine-learning algorithm predicting preoperative risk for six major postoperative complications using EHR data. Results: The area under the curve (AUC) of MySurgeryRisk algorithm ranged between 0.73 and 0.85 and was significantly higher than physiciansu0027 risk assessments (AUC between 0.47 and 0.69) for all postoperative complications except cardiovascular complications. The AUC for repeated physicianu0027s risk assessment improved by 2% to 5% for all complications with the exception of thirty-day mortality. Physiciansu0027 risk assessment for acute kidney injury and intensive care unit admission longer than 48 hours significantly improved after knowledge exchange, resulting in net reclassification improvement of 12.4% and 16%, respectively. Conclusions: The validated MySurgeryRisk algorithm predicted postoperative complications with equal or higher accuracy than pilot cohort of physicians using available clinical preoperative data. The interaction with algorithm significantly improved physiciansu0027 risk assessment. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Human-Computer Interaction | Health care,Intensive care unit,Acute kidney injury,Computer science,Risk assessment,Algorithm,Perioperative,Medical record,Cohort,Area under the curve |
DocType | Volume | Citations |
Journal | abs/1804.03258 | 0 |
PageRank | References | Authors |
0.34 | 0 | 9 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Meghan Brennan | 1 | 0 | 0.34 |
| Sahil Puri | 2 | 1 | 0.71 |
| Tezcan Ozrazgat-Baslanti | 3 | 1 | 2.37 |
| Rajendra Rana Bhat | 4 | 77 | 2.92 |
| Zheng Feng | 5 | 0 | 1.01 |
| Petar Momcilovic | 6 | 93 | 12.28 |
| Xiaolin Li | 7 | 243 | 17.57 |
| Daisy Zhe Wang | 8 | 755 | 50.24 |
| Azra Bihorac | 9 | 50 | 8.63 |