Forecasting Cardiology Admissions from Catheterization Laboratory
This work addresses scheduling and resource management for hospital staff and equipment in cardiology departments, but it is incremental as it applies standard time series methods to a specific dataset.
The study tackled the problem of forecasting weekly cardiology admissions from a catheterization laboratory using time series models, with ARIMA (2,0,2) (1,1,1) identified as the best fit based on error minimization and criteria like AIC and SBC, though it showed issues with stationarity, independence, and normality.
Emergent and unscheduled cardiology admissions from cardiac catheterization laboratory add complexity to the management of Cardiology and in-patient department. In this article, we sought to study the behavior of cardiology admissions from Catheterization laboratory using time series models. Our research involves retrospective cardiology admission data from March 1, 2012, to November 3, 2016, retrieved from a hospital in Iowa. Autoregressive integrated moving average (ARIMA), Holts method, mean method, naïve method, seasonal naïve, exponential smoothing, and drift method were implemented to forecast weekly cardiology admissions from Catheterization laboratory. ARIMA (2,0,2) (1,1,1) was selected as the best fit model with the minimum sum of error, Akaike information criterion and Schwartz Bayesian criterion. The model failed to reject the null hypothesis of stationarity, it lacked the evidence of independence, and rejected the null hypothesis of normality. The implication of this study will not only improve catheterization laboratory staff schedule, advocate efficient use of imaging equipment and inpatient telemetry beds but also equip management to proactively tackle inpatient overcrowding, plan for physical capacity expansion and so forth.