A new computational fuzzy time series model to forecast number of outpatient visits

Forecasting number of outpatient visits is pre-eminent for patient planning, medical resource utilization and overall management of health care system to a certain extent. Aim of forecasting the outpatient visits can also be seemed in terms of individual care. In addition, accurate prediction of outpatient visits in hospitals can play a significant role in health insurance plans and for deciding reimbursement system. As such, main challenge in healthcare simulation is to produce a realistic model that must utilize efficient techniques for managing complex time series data and should be capable of generating forecasted value with almost negligible error. We proposed forecasting model based on fuzzy time series that rectifies the existing imperfections and overcome the drawbacks of previous approaches. Novice concept introduced to eliminate the inadequacies by way of defining the universe of discourse on historical data. Model also endeavors to pontificate the issue of improving forecasting accuracy through the new idea of event discretization function. This was quite encouraging as it highlights the impact of trend & seasonal components by yielding dynamic change of values from time t to t+1. This fuzzy computing time series model is designed by joint consideration of three key points (1) Event discretization of time series data (2) Frequency density based partitioning (3) Creation of Fuzzy logical relationships in optimized way. Subsequently, performance of the proposed model is demonstrated and compared with some of the pre-existing forecasting methods on same outpatient data. In general, findings of the study are interesting and superior in terms of least Average Forecasting Error Rate (AFER) and Mean Square Error (MSE) values.