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Your LOS(S), Your Gain. Prediction tool for the hospital Length of Stay.

Brandt, L. van den (2013) Your LOS(S), Your Gain. Prediction tool for the hospital Length of Stay.

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Abstract:Hospitals in the Netherlands are forced to use their scarce resources as efficient as possible. One of these resources is the hospital bed capacity. An optimal admission planning uses hospital bed capacity as efficient as possible. In order to achieve such a planning, early predictions of the expected discharge moment of patients are needed. Expected discharge moments can be predicted if the expected duration of admissions is known. In other words, predictions of the expected length of stay (LOS) at admission are required. In this study, LOS is defined as the number of (semi-) days a patient is admitted to the hospital during an admission. Due to large variety in clinical course, it is a challenge to accurately predict LOS at admission. The Emma Children’s Hospital (ECH) of the Academic Medical Center Amsterdam (AMC) experiences difficulties in predicting LOS at admission. Interviews with the management of the ECH wards showed that LOS is currently not consequently predicted and registered. Prediction, when possible, is based on the physician’s medical experience. Physicians stated that they perceive 20% of the admissions as unpredictable due to large variation in clinical course. This research therefore aims to develop a prototype of a generic prediction tool that accurately predicts expected LOS based on historical data. Additionally, the accuracy of the LOS predictions made by physicians is measured. Method The prediction tool developed in this study was based on multiple regression. Regression analysis determines the predictive capacity of independent variables on a dependent outcome variable. It requires homogenous groups of sufficient size to prove statistical significance of the independent variables. The prediction tool consists of an LOS explanatory model and an application to prospective data. The explanatory model consists of four steps. First, admissions in the dataset are grouped on diagnosis. Second, groups are aggregated into classes when statistically comparable to meet minimally required class sizes for regression analysis. Third, the model performs regression analysis on all formed classes. Fourth, an LOS formula for each class based on the proven predictor variables resulting from regression analysis is created. The application to prospective data predicts the LOS for new admissions by matching the admission with the correct LOS formula. Results Not all proposed LOS predictor variables in literature were available in the ECH dataset (e.g. the weight of the patient and the presence of a secondary diagnosis). This was due to difficulties in combining various databases in the ECH. The location from where the patient was admitted (e.g. home, other hospital, ER) and the admission specialism had the highest predictive power on LOS. Gender and admission day (weekday or weekend day) were the poorest predictors of LOS. 5 The LOS prediction tool was able to predict the LOS of 40.7% of the admissions in the test set. The rest of the admissions were not predictable since too few admissions per diagnosis were available in the training set. Average absolute deviation between the tool’s predictions and observed LOS was 91.7%. This is an improvement in comparison to the average absolute deviation between the physician’s predictions and observed LOS, which was 147.6%. Conclusion The developed LOS prediction tool can predict the LOS of patients admitted to the ECH with higher accuracy than physicians can based on their medical experience. However, the number of admissions for which the tool can predict LOS, is limited. Recommendations Due to the large average absolute deviation between the tool’s predictions and observed LOS, it is not yet recommended to base the admission planning of the ECH on LOS predictions made by the tool. The dataset first needs to be enlarged and more influencing LOS variables need to be included in order to increase the accuracy of the predictions. Due to the generic character of the prediction tool, new or enlarged datasets are easily analyzed.
Item Type:Essay (Master)
Clients:
AMC Amsterdam, Amsterdam, Netherlands
Faculty:BMS: Behavioural, Management and Social Sciences
Subject:30 exact sciences in general, 31 mathematics, 44 medicine, 50 technical science in general
Programme:Industrial Engineering and Management MSc (60029)
Link to this item:http://purl.utwente.nl/essays/64235
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