Matching Clinicians to Operative Cases

2013 ◽  
Vol 04 (03) ◽  
pp. 445-453
Author(s):  
J. Wanderer ◽  
A. Was
Keyword(s):  
Risk Stratification ◽  
Operating Room ◽  
Web Tool ◽  
Physical Status ◽  
Billing Data ◽  
Web Based ◽  
Physical Status Classification ◽  
Status Classification ◽  
One Year ◽  
The Web

Summary Background: Patient and surgical case complexity are important considerations in creating appropriate clinical assignments for trainees in the operating room (OR). The American Society of Anesthesiologists (ASA) Physical Status Classification System is the most commonly used tool to classify patient illness severity, but it requires manual evaluation by a clinician and is highly variable. A Risk Stratification System for surgical patients was recently published which uses administrative billing codes to calculate four Risk Stratification Indices (RSIs) and provides an objective surrogate for patient complexity that does not require clinical evaluation. This risk score could be helpful when assigning operating room cases. Objective: This is a technical feasibility study to evaluate the process and potential utility of incorporating an automatic risk score calculation into a web-based tool for assigning OR cases. Methods: We created a web service implementation of the RSI model for one-year mortality and automatically calculated the RSI values for patients scheduled to undergo an operation the following day. An analysis was conducted on data availability for the RSI model and the correlation between RSI values and ASA physical status. Results: In a retrospective analysis of 46,740 patients who received surgery in the year preceding the web tool implementation, RSI values were generated for 20,638 patients (44%). The Spear-man’s rank correlation coefficient between ASA physical status classification and one-year mortality RSI values was 0.404. Conclusions: We have shown that it is possible to create a web-based tool that uses existing billing data to automatically calculate risk scores for patients scheduled to undergo surgery. Such a risk scoring system could be used to match patient acuity to physician experience, and to provide improved patient and clinician experiences. The web tool could be improved by expanding the input database or utilizing procedure booking codes rather than billing data.

Adding Examples to the ASA-Physical Status Classification Improves Correct Assignment to Patients

2017 ◽  
Vol 126 (4) ◽  
pp. 614-622 ◽  
Author(s):  
Erin E. Hurwitz ◽  
Michelle Simon ◽  
Sandhya R. Vinta ◽  
Charles F. Zehm ◽  
Sarah M. Shabot ◽  
...  
Keyword(s):  
Classification System ◽  
Questionnaire Study ◽  
Physical Status ◽  
Web Based ◽  
Physical Status Classification ◽  
Correct Assignment ◽  
Status Classification ◽  
American Society ◽  
American Society Of Anesthesiologists

Abstract Background Despite its widespread use, the American Society of Anesthesiologists (ASA)-Physical Status Classification System has been shown to result in inconsistent assignments among anesthesiologists. The ASA-Physical Status Classification System is also used by nonanesthesia-trained clinicians and others. In 2014, the ASA developed and approved examples to assist clinicians in determining the correct ASA-Physical Status Classification System assignment. The effect of these examples by anesthesia-trained and nonanesthesia-trained clinicians on appropriate ASA-Physical Status Classification System assignment in hypothetical cases was examined. Methods Anesthesia-trained and nonanesthesia-trained clinicians were recruited via email to participate in a web-based questionnaire study. The questionnaire consisted of 10 hypothetical cases, for which respondents were first asked to assign ASA-Physical Status using only the ASA-Physical Status Classification System definitions and a second time using the newly ASA-approved examples. Results With ASA-approved examples, both anesthesia-trained and nonanesthesia-trained clinicians improved in mean number of correct answers (out of possible 10) compared to ASA-Physical Status Classification System definitions alone (P < 0.001 for all). However, with examples, nonanesthesia-trained clinicians improved more compared to anesthesia-trained clinicians. With definitions only, anesthesia-trained clinicians (5.8 ± 1.6) scored higher than nonanesthesia-trained clinicians (5.4 ± 1.7; P = 0.041). With examples, anesthesia-trained (7.7 ± 1.8) and nonanesthesia-trained (8.0 ± 1.7) groups were not significantly different (P = 0.100). Conclusions The addition of examples to the definitions of the ASA-Physical Status Classification System increases the correct assignment of patients by anesthesia-trained and nonanesthesia-trained clinicians.

scholarly journals Pre-Anesthesia Assessment and Preparation

2020 ◽  
Vol 2 (2) ◽  
pp. 228-235
Author(s):  
Imai Indra ◽  
Kulsum Kulsum
Keyword(s):  
Risk Factors ◽  
Physical Examination ◽  
Operating Room ◽  
History Taking ◽  
Physical Status ◽  
Laboratory Examination ◽  
Treatment Room ◽  
Physical Status Classification ◽  
Status Assessment ◽  
Status Classification

The aims of the study is to find out pre – anesthesia assessment and preparation. The result of the study shows that Pre-anesthesia is an important first step in a series of anesthesia procedures performed on patients who are planned to undergo surgery. The things that need to be done include history taking, physical examination, laboratory examination, and physical status classification. Preparations are made at the clinic or at home for outpatients, in the treatment room, in the Central Surgery Installation (IBS) room, and in the operating room. Pre-anesthesia physical status assessment is very important. Anesthesia is not differentiated based on the size of the surgery but consideration of the choice of anesthesia techniques that will be given to patients is very complex and comprehensive considering all types of anesthesia have risk factors for complications that can threaten the patient's life

scholarly journals Preoperative predictions of in-hospital mortality using electronic medical record data

2018 ◽  
Author(s):  
Brian Hill ◽  
Robert Brown ◽  
Eilon Gabel ◽  
Christine Lee ◽  
Maxime Cannesson ◽  
...  
Keyword(s):  
Logistic Regression ◽  
Random Forest ◽  
Hospital Mortality ◽  
Risk Score ◽  
Surgical Mortality ◽  
Physical Status ◽  
Preoperative Risk ◽  
Physical Status Classification ◽  
Postoperative Risk ◽  
Status Classification

AbstractBackgroundPredicting preoperative in-hospital mortality using readily-available electronic medical record (EMR) data can aid clinicians in accurately and rapidly determining surgical risk. While previous work has shown that the American Society of Anesthesiologists (ASA) Physical Status Classification is a useful, though subjective, feature for predicting surgical outcomes, obtaining this classification requires a clinician to review the patient’s medical records. Our goal here is to create an improved risk score using electronic medical records and demonstrate its utility in predicting in-hospital mortality without requiring clinician-derived ASA scores.MethodsData from 49,513 surgical patients were used to train logistic regression, random forest, and gradient boosted tree classifiers for predicting in-hospital mortality. The features used are readily available before surgery from EMR databases. A gradient boosted tree regression model was trained to impute the ASA Physical Status Classification, and this new, imputed score was included as an additional feature to preoperatively predict in-hospital post-surgical mortality. The preoperative risk prediction was then used as an input feature to a deep neural network (DNN), along with intraoperative features, to predict postoperative in-hospital mortality risk. Performance was measured using the area under the receiver operating characteristic (ROC) curve (AUC).ResultsWe found that the random forest classifier (AUC 0.921, 95%CI 0.908-0.934) outperforms logistic regression (AUC 0.871, 95%CI 0.841-0.900) and gradient boosted trees (AUC 0.897, 95%CI 0.881-0.912) in predicting in-hospital post-surgical mortality. Using logistic regression, the ASA Physical Status Classification score alone had an AUC of 0.865 (95%CI 0.848-0.882). Adding preoperative features to the ASA Physical Status Classification improved the random forest AUC to 0.929 (95%CI 0.915-0.943). Using only automatically obtained preoperative features with no clinician intervention, we found that the random forest model achieved an AUC of 0.921 (95%CI 0.908-0.934). Integrating the preoperative risk prediction into the DNN for postoperative risk prediction results in an AUC of 0.924 (95%CI 0.905-0.941), and with both a preoperative and postoperative risk score for each patient, we were able to show that the mortality risk changes over time.ConclusionsFeatures easily extracted from EMR data can be used to preoperatively predict the risk of in-hospital post-surgical mortality in a fully automated fashion, with accuracy comparable to models trained on features that require clinical expertise. This preoperative risk score can then be compared to the postoperative risk score to show that the risk changes, and therefore should be monitored longitudinally over time.Author summaryRapid, preoperative identification of those patients at highest risk for medical complications is necessary to ensure that limited infrastructure and human resources are directed towards those most likely to benefit. Existing risk scores either lack specificity at the patient level, or utilize the American Society of Anesthesiologists (ASA) physical status classification, which requires a clinician to review the chart. In this manuscript we report on using machine-learning algorithms, specifically random forest, to create a fully automated score that predicts preoperative in-hospital mortality based solely on structured data available at the time of surgery. This score has a higher AUC than both the ASA physical status score and the Charlson comorbidity score. Additionally, we integrate this score with a previously published postoperative score to demonstrate the extent to which patient risk changes during the perioperative period.

scholarly journals Safety and Efficacy of Nonanesthesiologist-Administrated Propofol during Endoscopic Submucosal Dissection of Gastric Epithelial Tumors

2019 ◽  
Vol 2019 ◽  
pp. 1-7 ◽  
Author(s):  
Keiichiro Abe ◽  
Keiichi Tominaga ◽  
Akira Kanamori ◽  
Tsunehiro Suzuki ◽  
Hitoshi Kino ◽  
...  
Keyword(s):  
Adverse Events ◽  
Endoscopic Submucosal Dissection ◽  
University Hospital ◽  
Physical Status ◽  
Safety And Efficacy ◽  
Submucosal Dissection ◽  
Epithelial Tumors ◽  
Physical Status Classification ◽  
Significant Difference ◽  
Status Classification

Objective. There is no consensus regarding administration of propofol for performing endoscopic submucosal dissection (ESD) in patients with comorbidities. The aim of this study was to evaluate the safety and efficacy of propofol-induced sedation administered by nonanesthesiologists during ESD of gastric cancer in patients with comorbidities classified according to the American Society of Anesthesiologists (ASA) physical status. Methods. Five hundred and twenty-two patients who underwent ESD for gastric epithelial tumors under sedation by nonanesthesiologist-administrated propofol between April 2011 and October 2017 at Dokkyo Medical University Hospital were enrolled in this study. The patients were divided into 3 groups according to the ASA physical status classification. Hypotension, desaturation, and bradycardia were evaluated as the adverse events associated with propofol. The safety of sedation by nonanesthesiologist-administrated propofol was measured as the primary outcome. Results. The patients were classified according to the ASA physical status classification: 182 with no comorbidity (ASA 1), 273 with mild comorbidity (ASA 2), and 67 with severe comorbidity (ASA 3). The median age of the patients with ASA physical status of 2/3 was higher than the median age of those with ASA physical status of 1. There was no significant difference in tumor characteristics, total amount of propofol used, or ESD procedure time, among the 3 groups. Adverse events related to propofol in the 522 patients were as follows: hypotension (systolic blood pressure<90 mmHg) in 113 patients (21.6%), respiratory depression (SpO2<90%) in 265 patients (50.8%), and bradycardia (pulse rate<50 bpm) in 39 patients (7.47%). There was no significant difference in the incidences of adverse events among the 3 groups during induction, maintenance, or recovery. No severe adverse event was reported. ASA 3 patients had a significantly longer mean length of hospital stay (8 days for ASA 1, 9 days for ASA 2, and 9 days for ASA 3, P=0.003). However, the difference did not appear to be clinically significant. Conclusions. Sedation by nonanesthesiologist-administrated propofol during ESD is safe and effective, even for at-risk patients according to the ASA physical status classification.

Classification of comorbidity in trauma: The reliability of pre-injury ASA physical status classification

Injury ◽  
2013 ◽  
Vol 44 (1) ◽  
pp. 29-35 ◽  
Author(s):  
Kjetil G. Ringdal ◽  
Nils Oddvar Skaga ◽  
Petter Andreas Steen ◽  
Morten Hestnes ◽  
Petter Laake ◽  
...  
Keyword(s):  
Physical Status ◽  
Physical Status Classification ◽  
Status Classification
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