• Optimizing OR Efficiency through Surgical Case Forecasting with ARIMA Averaging

    Nikhil Patel, Sandeep Trivedi (see profile)
    Forecasting, Time-series analysis
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    Operating rooms, often known as ORs, are among the most critical parts in hospitals, and their performance has a considerable bearing on how well the hospital functions as a whole. Uncertainty contributes significantly to the difficulty of an operating room. Credible forecasts are essential for operating room efficiency because they can provide signals for the monitoring of surgical overflows in periods of peak and trough demand for surgery; and minimize the related costs in equipment and workforce redundancy, and improve overall health care services. Optimizing the efficiency of the operating room has significant consequences for cost reductions, patient happiness, and the morale of the surgical department. Forecast averaging, also known as prediction combining, is a system for merging several predictions into a single prediction, which is often a better way than deciding which one forecast was best out of the available individual predictions. We applied the ARIMA Forecast Averaging method to demonstrate the surgical volume case predictions. We also showed that in forecasting surgical volume cases, the ARIMA models with lower AR and MA terms performed well in terms of different model selection criteria such as AIC. BC, and HQ. Medical care service problems are caused not just by a mismatch between resource demand and supply, but also by poor management. Operating rooms requires a significant investment of both time and money. Ineffective usage of operating rooms results in lost efforts and time, increased expenses, and a lower number of patients treated compared to what was originally anticipated. This cluster of problems leads to losses as well as a reduction in the level of satisfaction experienced by patients. We argued that the cost of usage of the operating room (OR) may be significantly decreased by increasing the predictive accuracy of the surgical case volume.
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    Journal article    
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    4 months ago
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