Integrating Data, Algorithms and Clinical Reasoning for Surgical Risk Assessment
| Status: | Recruiting |
|---|---|
| Conditions: | Hospital |
| Therapuetic Areas: | Other |
| Healthy: | No |
| Age Range: | 18 - 100 |
| Updated: | 11/22/2018 |
| Start Date: | October 2016 |
| End Date: | May 2022 |
| Contact: | Sandhya A Chheda, JD |
| Email: | sandhya.chheda@medicine.ufl.edu |
| Phone: | 352-273-8820 |
Integrating Data, Algorithms and Clinical Reasoning for Surgical Risk Assessment (IDEALIST)
Brief Summary: The goal of this study is to implement and test an intelligent perioperative
system (IPS) that in real-time predicts risk for postoperative complications using routine
clinical data collected in electronic health records. The accuracy of computer-generated risk
scores will be compared to physician's risk scores for the same patients. Physicians will be
also asked to provide the opinion regarding the computer-generated risk scores using
interactive interface with the program. The information regarding the risk scores performance
will be collected during the two 6-month periods. The accuracy of IPS and physicians will be
compared at the end at those two time periods.
system (IPS) that in real-time predicts risk for postoperative complications using routine
clinical data collected in electronic health records. The accuracy of computer-generated risk
scores will be compared to physician's risk scores for the same patients. Physicians will be
also asked to provide the opinion regarding the computer-generated risk scores using
interactive interface with the program. The information regarding the risk scores performance
will be collected during the two 6-month periods. The accuracy of IPS and physicians will be
compared at the end at those two time periods.
Postoperative complications significantly increase morbidity, mortality and cost after
surgery. In the current clinical practice the prediction of the risk for developing
complications after surgery is manly based on physicians' clinical judgment. The predictive
accuracy of that judgment is limited and poorly studied. The investigators will design an
intelligent perioperative system (IPS) as the set of computer software and algorithms that in
real-time predict risk for postoperative complications using routine clinical data in
electronic health records. The system is designed as the self-learning system with the
ability to interact with physicians and solicit their feedback. This study will compare the
clinical judgment of physicians with computer generated risk scores for patients undergoing
major surgery. All surgeons and anesthesiologists at large single-center tertiary academic
center will be recruited to participate in this study. The IPS system will be implemented in
real time and will generate risk scores for postoperative complications for patients planned
to undergo surgery performed by the physicians enrolled in the study. Physicians will be
asked to provide their risk scores (using visual analog risk scale from 0-100) for the same
patients before and after interacting with the IPS. They will also have the opportunity to
review computer-generated risk scores and provide their feedback. The information will be
collected during two six-month periods. At the end of each 6-months period predicted risk
estimates will be compared to the true occurrence of the complications. Predictive
performance of physicians' risk scores will be compared to IPS generated risk scores using
the comparison between area under the receiver-operating curve (AUC), sensitivity,
specificity and positive and negative predicted values.
surgery. In the current clinical practice the prediction of the risk for developing
complications after surgery is manly based on physicians' clinical judgment. The predictive
accuracy of that judgment is limited and poorly studied. The investigators will design an
intelligent perioperative system (IPS) as the set of computer software and algorithms that in
real-time predict risk for postoperative complications using routine clinical data in
electronic health records. The system is designed as the self-learning system with the
ability to interact with physicians and solicit their feedback. This study will compare the
clinical judgment of physicians with computer generated risk scores for patients undergoing
major surgery. All surgeons and anesthesiologists at large single-center tertiary academic
center will be recruited to participate in this study. The IPS system will be implemented in
real time and will generate risk scores for postoperative complications for patients planned
to undergo surgery performed by the physicians enrolled in the study. Physicians will be
asked to provide their risk scores (using visual analog risk scale from 0-100) for the same
patients before and after interacting with the IPS. They will also have the opportunity to
review computer-generated risk scores and provide their feedback. The information will be
collected during two six-month periods. At the end of each 6-months period predicted risk
estimates will be compared to the true occurrence of the complications. Predictive
performance of physicians' risk scores will be compared to IPS generated risk scores using
the comparison between area under the receiver-operating curve (AUC), sensitivity,
specificity and positive and negative predicted values.
Inclusion criteria:
Surgeons and anesthesiologists working in adult inpatient operative practices.
Exclusion criteria:
Surgeons and anesthesiologists working in obstetric and pediatric practices.
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