Forecasting Cardiology Admissions from Catheterization Laboratory

TL;DR

This study applies various time series models to forecast weekly cardiology admissions from a catheterization lab, identifying ARIMA as the best model to aid hospital resource planning and management.

Contribution

It introduces a comprehensive comparison of multiple forecasting methods for cardiology admissions, highlighting ARIMA's effectiveness in this context.

Findings

ARIMA (2,0,2)(1,1,1) was the best fit model.

The model indicated non-normality and stationarity in the data.

Forecasting can improve hospital resource management.

Abstract

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, na\"ive method, seasonal na\"ive, 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.