Man vs. machine: Predicting hospital bed demand from an emergency department.

BACKGROUND: The recent literature reports promising results from using intelligent systems to support decision making in healthcare operations. Using these systems may lead to improved diagnostic and treatment protocols and to predict hospital bed demand. Predicting hospital bed demand in emergency department (ED) attendances could help resource allocation and reduce pressure on busy hospitals. However, there is still limited knowledge on whether intelligent systems can operate as fully autonomous, user-independent systems. OBJECTIVE: Compare the performance of a computer-based algorithm and humans in predicting hospital bed demand (admissions and discharges) based on the initial SOAP (Subjective, Objective, Assessment, Plan) records of the ED. METHODS: This was a retrospective cohort study that compared the performance of humans and machines in predicting hospital bed demand from an ED. It considered electronic medical records (EMR) of 9030 patients (230 used as a testing set, and hence evaluated both by humans and by an algorithm, and 8800 used as a training set exclusively by the algorithm) who visited the ED of a tertiary care and teaching public hospital located in Porto Alegre, Brazil between January and December 2014. The machine role was played by Support Vector Machine Classifier and the human prediction was performed by four ED physicians. Predictions were compared in terms of sensitivity, specificity, accuracy, and area under the receiver operating characteristic curve (AUROC). RESULTS: All graders achieved similar accuracies. The accuracy by AUROC for the testing set was 0.82 [95% confidence interval (CI) of 0.77-0.87], 0.80 (95% CI: 0.75-0.85), 0.76 (95% CI: 0.71-0.81) for novice physicians, machine, experienced physicians, respectively. Processing time per test EMR was 0.00812±0.0009 seconds. In contrast, novice physicians took on average 156.80 seconds per test EMR, while experienced physicians took on average 56.40 seconds per test EMR. CONCLUSIONS: Our data indicated that the system could predict patient admission or discharge states with 80% accuracy, which was similar the performance of novice and experienced physicians. These results suggested that the algorithm could operate as an autonomous and independent system to complete this task.

Diagnostic Accuracy of CD64 for Sepsis in Emergency Department.

INTRODUCTION: Sepsis is a systemic inflammatory response to suspected or confirmed infection. Clinical evaluations are essential for its early detection and treatment. Blood cultures may take as long as 2 days to yield a result and are not always reliable. However, recent studies have suggested that neutrophil CD64 expression may be a sensitive and specific alternative for the diagnosis of systemic infection. OBJECTIVE: The objective of the study was to analyze the difference in CD64 values between subjects with systemic inflammatory response syndrome (SIRS), suspected or confirmed sepsis, who meet diagnostic criteria for SIRS upon arriving at an emergency department. MATERIALS AND METHODS: This was a prospective observational cohort study, an accuracy study of CD64 prospectively evaluated. The sample consisted of 109 patients aged 18 years with criteria for SIRS on arrival to emergency department. CD64 expression was measured within 6 h of hospital admission and once again after 48 h. RESULTS: ROC curve analysis suggested that a cutoff of 1.45 for CD64 expression could diagnose sepsis with a sensitivity of 0.85, a specificity of 0.75, an accuracy of 82.08%, a positive predictive value of 0.96, a negative predictive value of 0.38 and a positive likelihood ratio of 3.33. The area under the curve was 0.83. CONCLUSION: CD64 seems to be a useful, sensitive, and specific biomarker in discriminating between SIRS and sepsis.