Predicting Medicine Administration Times in the Inpatient Ward Using Data Analytics

Cristian Andrey Jaimez Olarte () and William J. Guerrero ()
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Cristian Andrey Jaimez Olarte: Universidad de La Sabana
William J. Guerrero: Universidad de La Sabana

A chapter in Operations Research and Analytics in Latin America, 2023, pp 169-178 from Springer

Abstract: Abstract Today's hospital systems are looking more than ever to make improvements in their internal processes to provide the best possible care for patients in their facilities and are turning to various management techniques such as Lean Healthcare and Analytics. This study describes the process of analyzing data about activity times and wastes for medicine administration, which is one of the most important activities of nursing care, and the construction of a predictive model. The methodology carried out in this research is a data collection of the medicines administration process and the patient to whom they are administered, observing whether there is waste or not. Subsequently, this information is integrated into a linear regression model for the estimation of working time, validating the corresponding assumptions. All this in order to develop better management in hospital wards. Regression models were constructed from a sample collected in the hospital wards with the epidemiological profile of the patients, and it is shown that the variables of the same come to generate an impact on the execution of the work. Finally, for future work, these models can be refined by including variables from the care environment and other activities, validated by professional nurses and linked to moments of nursing care.

Keywords: Lean healthcare; Analytics; Nursing care; Linear regression; Hospital logistics (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnopch:978-3-031-28870-8_14

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DOI: 10.1007/978-3-031-28870-8_14

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