Publication Type

Conference Proceeding Article



Publication Date



Healthcare is a very important industry where analytics has been applied successfully to generate insights about patients, identify bottleneck and to improve the business efficiency. In this paper, we aim to look at the patient appointment process as the hospital is experiencing high volume of ?no shows. ?No shows have a high impact on longer appointment lead time for patients, poor patient satisfaction and loss of revenue for hospital. We use data analytics to identify pattern of ?no shows, develop a statistical model to predict the probability of ?no shows and finally operationalizing the model to embed the analytics solution in the business process to reduce the number of ?no shows in the hospital. Exploratory data analysis (EDA) was used to find out the major causes of no shows based on patient demographic information, patient appointment detail and SMS reminder response. Data mining techniques such as logistic regression and recursive partitioning were used on training, test and validation data to predict patients who have high probability of ?no show. We present the analytical outcomes and findings from our model. Our logistic regression model could predict around 70% of the ?no show cases correctly with a Kappa coefficient of 0.41 on validation data. Based on our finding, we have recommended different strategies to the operations staff for possible reduction of no show slots.


analytics, predictive model, appointment process, business process improvement, “no shows”


Computer Sciences | Operations Research, Systems Engineering and Industrial Engineering

Research Areas

Intelligent Systems and Decision Analytics


ICISA 2014: 2014 Fifth International Conference on Information Science and Applications: 6-9 May, 2014, Seoul, Korea

First Page


Last Page







IEEE Computer Society

City or Country

Los Alamitos, CA

Copyright Owner and License


Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Additional URL