Open-source Software for Demand Forecasting of Clinical Laboratory Test Volumes Using Time-series Analysis

Abstract

Background:Demand forecasting is the area of predictive analytics devoted to predicting future volumes of services or consumables. Fair understanding and estimation of how demand will vary facilitates the optimal utilization of resources. In a medical laboratory, accurate forecasting of future demand, that is, test volumes, can increase efficiency and facilitate long-term laboratory planning. Importantly, in an era of utilization management initiatives, accurately predicted volumes compared to the realized test volumes can form a precise way to evaluate utilization management initiatives. Laboratory test volumes are often highly amenable to forecasting by time-series models; however, the statistical software needed to do this is generally either expensive or highly technical.Method:In this paper, we describe an open-source web-based software tool for time-series forecasting and explain how to use it as a demand forecasting tool in clinical laboratories to estimate test volumes.Results:This tool has three different models, that is, Holt-Winters multiplicative, Holt-Winters additive, and simple linear regression. Moreover, these models are ranked and the best one is highlighted.Conclusion:This tool will allow anyone with historic test volume data to model future demand.

Upper extremity muscular strength in wrist‐twisting tasks: Model approach towards task design

“…Time-series forecasting was carried out using Holt–Winters model, which estimates the predicted values on previous event data. The model work flow for the time-series forecasting[12] is depicted in Figure 2. The forecast modeling was performed on WEKA 3.8.2 environment.…”

Prediction of Daily Blood Sampling Room Visits Based on ARIMA and SES Model

“…The simple exponential smoothing (SES) model is a forecasting model that likewise estimates the historical forecast value based on the time series data. It assumes that the historical data and forecast data of the time series data are relatively continuous and have a common repeating pattern, and thus they can be matched well with short-term forecasts [ 19 ]. The model is simple to understand, reducing the work and expertise required to identify the appropriate model, which is particularly suited for busy hospital managers [ 20 ].…”

“…For time series data of different features, different prediction models ought to be used [ 31 ]. The daily number of blood collections, our research object, is a seasonal nonstationary time series with trend and periodicity [ 5 , 19 ]. The prediction method must accurately capture the characteristics of trend, periodicity, and randomness.…”

“…In this article, we assume that historical data and forecast data of time series data have relative continuity and repetitive patterns in the medium and long-term range. Therefore, a SES model [ 19 ] will be used to capture the day-of-the-week effects of the daily number of blood collection time series, analyzing the complex uncertainty of medical service demand. Finally, we will form a combined forecasting model based on the above two models.…”