A New Data Mining Approach For Healthcare Center Operations

A New Data Mining Approach For Healthcare Center Operations
February 14, 2020

With constant growth of world population, demand for healthcare services is growing as well, increasing the healthcare costs by 2.4-7.5% per year until 2020 in various countries. Healthcare market in the United Arab Emirates (U.A.E.) is expected to grow 12.7% per year, until 2020 and will possibly grow even further due to development of medical tourism, an economic priority for U.A.E.

Analytical tools, namely, data mining and optimization can contribute to a large degree in achieving operational efficiencies.

Dr. Gurdal Ertek and Dr. Salam Abdallah from Abu Dhabi University, together with Dr. Mohsin Malik from The University of Melbourne, have developed a new data mining approach for healthcare operations. The approach has been applied using real-world data from one of the largest public hospitals in the U.A.E, demonstrating its applicability and benefits.

Among other key points, the study quantifies and analyzes the timing of patient arrivals in order to improve the patient admission process step through elimination of wasted time. It is crucial to point out the main performance measure in the study was lateness and the association of lateness with various factors.

The importance of this segment of the study is immense, as there are no academic studies on predicting lateness as a function of schedule day and time. There is, also, a significant gap of knowledge regarding the understanding of the lateness and the factors underlying lateness in healthcare centers. Dr. Ertek, Dr. Abdallah and Dr. Malik focused on the way patient arrivals into healthcare centers can be analyzed in order to come up with insights into lateness. They researched about factors associated with lateness and developed suggestions on how patient appointments can be scheduled to minimize this issue.

Study also presents an appointment scheduling approach, based on the fact that that lateness is dependent on the scheduled time, rather than being independent of it. One of the key contributions of the research study is a subsequent optimization model for scheduling appointments that may improve operational efficiency.

Some of the essential insights of this data mining approach are precious and include presentation of hidden patterns that generate information about patient arrival patterns, identification of independent variables that affect lateness, and determination of form and parameters of the best regression function. The three experts have concluded that the approach they developed can serve as an engine for determining optimal appointment day and times for healthcare centers.

Compared to other closely-related researches, this one is at least 10-fold larger, providing, so far, the most relevant and most detailed information regarding this subject. The study can be downloaded online and the approach can be applied by healthcare practitioners to analyze data from their own organizations.