A grey approach to predicting healthcare performance. (February 2019)

• Record Type: Journal Article
• Authors: Rahman, Md. Hafizur; Tumpa, Tasmia Jannat; Ali, Syed Mithun; Paul, Sanjoy Kumar
• Is Part Of: Measurement. Volume 134(2019)
• Page Start: 307
• Page End: 325
• Publication Date: 2019-02
• Subjects: Healthcare performance -- Improved grey prediction model -- Hospital key performance indicators; Weights and measures -- Periodicals; Measurement -- Periodicals; Measurement; Weights and measures; Periodicals; 530.8
• DOI: 10.1016/j.measurement.2018.10.055
• Languages: English
• ISSNs: 0263-2241

Abstract

Highlights: A grey approach to predicting key healthcare performance indicators is used. Key performance indicators for healthcare units are proposed. Sensitivity analysis is performed to check the robustness of the approach. Managerial implications are suggested. Abstract: The success of an organization or a particular activity is evaluated through the measurement of key performance indicators (KPIs). The aim of this paper is to analyze and predict the indicators of healthcare performance using grey systems theory. Recent advancements in science and technology have made the healthcare industry extremely efficient at collecting data using electronic claims systems such as electronic health records. Therefore, collecting field level primary data becomes easier and accumulate them to generate secondary data for research purpose and to get an insight of the organization performance is absolutely necessary. Our research analyzes the KPIs of a hospital based on a secondary data source. Since, secondary data contains uncertainty and sometimes poor information, grey prediction model suits best to make a prediction model in this regard. Conventional grey model has considerable drawbacks while making a rigorous prediction model. For this, we apply an improved grey prediction model to predict the KPIs of the healthcare performance indicators. Several error measures in our model give a best fit of the data and allow prediction of the KPIs. The prediction model gives good estimates of the quantitative indicators and produced error rate within an acceptable range. We observe that the KPIs of bed turnover rate (BTR) and bed occupancy rate (BOR) have an increasing trend, whereas the KPIs of average length of stay (ALOS), hospital death rate (HDR) and hospital infection rate (HIR) show a decreasing trend over time. The main contribution of this research is a grey-based prediction model that can provide managers with the information they need to evaluate and predict the performance of a hospital. The research indicates that managers should give greater priority to the indicators which will result in better patients' satisfaction and improved profit margin. Healthcare managers striving towards better performance will now have an empirical basis upon which to formulate and adjust their strategies, after analyzing the predicted value.