Background: The healthcare needs of under-fives and infants create major public health and development challenges in Ghana. Though some level of progress has been achieved in the area, there still exist several challenges to be addressed with regard to the survival and development of these vulnerable age groups. One module used in this domain is to monitor, assess, and evaluate the healthcare needs of infants and under-fives in healthcare facilities. Although the literature has dealt greatly with child healthcare in Ghana very little has been done in the area of modelling and forecasting the prevalence of infant and under-five outpatients’ healthcare needs across hospitals in the country. Purpose: This study was conducted to assess, model, and probably predict the outpatient performance variable for infants and under-fives in the University of Cape Coast Hospital. Methodology: Quantitative methods and a longitudinal research design were used in this study. Monthly data of infant and under-five outpatients covering January 2012 through December 2021 was sourced from the District Health Information Management System (DHIMS II) constituting a total sample size of 120 observations. The classical Box-Jenkins method of applied univariate statistical modelling was used to analyze the data. Findings: This unbiased study revealed in its findings 100 competitive models for the data. A seasonal autoregressive moving average model SARIMA (2, 0, 3)(1, 0, 1)12 emerged as the best fitting model for the data with the lowest AIC value. The Ljung-Box Q-test for serial correlation indicated p-values > 0.05 meaning that the model exhibited white noise in its residuals. The findings revealed seasonality but no trend with seasonal peaks in March, July, and November every year. Unique contribution to theory and practice: A seasonal autoregressive moving average model SARIMA (2, 0, 3)(1, 0, 1)12 emerged as the best fitting model predicting under-five and infant visits to the outpatient department. This model will logistically guide the management of the hospital to prudently and effectively allocate human and material resources to the paediatric unit of the hospital. Further studies are recommended to investigate why there are seasonal peaks in the months of March, July, and November every year so that appropriate public health interventions can be applied to mitigate the situation.
Background: Modelling and forecasting demand for future emergency healthcare services is increasingly gaining wide attention in the emergency healthcare industry worldwide. This aids hospital managers in looking into various options to appropriately plan and allocate available scarce resources for optimal and swift performance. Despite its importance, our knowledge of daily patient flow into the Accident and Emergency Department (AED) of the University of Cape Coast Hospital is incomprehensive, and even the model that best explains its movements remains unknown. Methods: Using daily periodicity of 517 time-series observations on daily patient arrivals sourced from the AED register over January 2020 through May 2021 the autoregressive integrated moving average (ARIMA) of the classical Box-Jenkins methods of time series analysis was used to analyse the data. Results: This study revealed twenty-five non-seasonal candidate models for the hospital AED and ARIMA (0, 1, 2) emerged as the best fitting model. The study results showed that the daily patient arrivals at the AED section of the University Hospital witnessed a 50% decline on average within the study period and a further 33% decline in the forecast region. The findings also revealed very high volatility in daily patient arrivals with an average of eight patient arrivals per day. Conclusion Non-seasonal ARIMA (0, 1, 2) was identified as the best model. Thus, for policy, intervention, and future research direction, it is recommended that steps are taken to investigate the highly volatile nature of patient arrivals as well as the steady downward trending of the daily patient flow at the Accident and Emergency Department of the University Hospital
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