Time series forecasting has been an important field in science with widespread application in Healthcare. The applications can be broadly categorized into financial, operations and clinical. The purpose of this session will be to walk through the lifecycle of timeseries forecasting project discussed with some of the Healthcare IT use cases in Cerner. The session will brief on the various forecasting methods, each type of which excels in different situations and has very different assumptions about the variation and evolution of the systems over the time. The session will further discuss on the metric selection of the performance measure of timeseries forecasts and how to interpret the results.
Time series forecasting can be defined as the estimation of future values of temporal or time related measurements which are built based on mathematical and statistical models with specific assumptions about the underlying system. Thus, this method can be explained as transforming past values or measurements into the estimates of the future. This technique provides near accurate assumptions about future trends based on historical time-series data. It allows one to analyze major patterns such as trends, seasonality, cyclicity and irregularity.
In the healthcare IT domain, one deals with such data regularly and having an accurate prediction in the below-mentioned field can help an organization to make better data-driven decisions.
1. Financial: Revenue Cycle Management
a. Cash, Revenue, Account Receivables Forecasting
b. Optimize inventory of high-cost medicines.
2. Clinical:
a. An early prognosis mechanism in telehealth systems.
b. Forecasting incidence of hemorrhagic fever with renal syndrome
3. Operations:
a. Emergency Department Patient Volume Prediction
b. Forecasting monthly patient volume at a hospital level.

References:
1. An Optimization of Inventory Demand Forecasting in University Healthcare Centre
https://iopscience.iop.org/article/10.1088/1757-899X/166/1/012035
2. Time series model for forecasting the number of new admission inpatients
https://bmcmedinformdecismak.biomedcentral.com/articles/10.1186/s12911-018-0616-8
3. Forecasting Daily Volume and Acuity of Patients in the Emergency Department. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5048091/
4. Time Series Analysis for Forecasting Hospital Census: Application to the Neonatal Intensive Care Unit
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4941839/
5. Employing time-series forecasting to historical medical data: an application towards early prognosis within elderly health monitoring environments
http://ceur-ws.org/Vol-1213/paper7.pdf
6. Forecasting incidence of hemorrhagic fever with renal syndrome in China using ARIMA model
https://bmcinfectdis.biomedcentral.com/articles/10.1186/1471-2334-11-218

7. Time Series Forecasting for Healthcare Diagnosis and Prognostics with the Focus on Cardiovascular Diseases.
https://link.springer.com/chapter/10.1007/978-981-10-4361-1_138