What is it about?

This paper presents a data mining framework for analyzing patient arrivals in healthcare centers. The framework utilizes various methods such as association mining, text cloud analysis, Pareto analysis, cross-tabular analysis, and regression analysis. Real-world data from a large public hospital in the UAE is used to demonstrate the applicability and potential benefits of the framework. The dataset used is significantly larger than previous studies in this area. The research aims to optimize appointment scheduling based on patient arrival patterns.

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Why is it important?

This research is important because it addresses the need for efficient patient management in healthcare centers. By analyzing patient arrival patterns, the framework can help optimize appointment scheduling, leading to improved operational efficiency and patient satisfaction. The use of data mining techniques and the large dataset make this work unique and timely. The findings can benefit healthcare practitioners by providing insights into patient flow and enabling better resource allocation.

Perspectives

This research publication opens up new avenues for studying patient arrivals in healthcare centers. It introduces a data mining framework that can be applied to analyze and optimize appointment scheduling based on patient arrival patterns. This work can motivate further research on the application of data mining techniques in healthcare management, exploring other factors that impact patient flow and developing more sophisticated optimization models. It also encourages the integration of data analytics and informatics in healthcare information systems to enhance decision-making processes.

Gurdal Ertek

Read the Original

This page is a summary of: A Data Mining Framework for the Analysis of Patient Arrivals into Healthcare Centers, December 2017, ACM (Association for Computing Machinery),
DOI: 10.1145/3176653.3176740.
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