Forecasting the Number of New Patient Visits in Hospital Inpatient Services using the ARIMA Model - Luh Gde Nita Sri Wahyuningsih; Ni Luh Putu Dina Susanti; Desak Kadek Sastrawati (GLOBAL HEALTH SCIENCE GROUP, 2025)
Forecasting the number of inpatient visits in a hospital is the process of predicting how many patients are expected to be admitted and hospitalized in the future. These predictions are made by analyzing historical data from previous inpatient visits to assist hospitals in planning and allocating resources more effectively. The purpose of this literature review is to explore studies related to forecasting the number of new patient visits in hospitals with ARIMA models. A search of international and national articles was conducted using PubMed, Researchgate, Elsevier and Google Scholar databases published in 2015 - 2025, 10 articles met the article selection process and were considered relevant. Forecasting the number of new patient visits in hospital inpatient services is a crucial step for capacity management and efficient resource allocation. which is a search for international and national literature conducted using the PubMed, Researchgate, Elsevier and Google Scholar databases. In the initial stage of searching journal articles, 2,562 articles were obtained from 2015 to 2025 using the keywords “Forecasting”, “ARIMA Model”. Of the 2,562 articles selected during the search, 10 articles met the article selection process and were considered relevant. Based on the results of the analysis using the ARIMA (AutoRegressive Integrated Moving Average) Model, it can be concluded that this model shows strong potential and accuracy in predicting the pattern of new patient visits.