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Journal of Computer Science & Systems Biology
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Research Article

Augmentation of Patient Health Care Choices for Morbid Obesity by Use of Computer Decision Analysis

Yaw Sarpong1, Jodi Ryder2 and Scott Litofsky N1*

1Division of Neurological Surgery, University of Missouri-Columbia School of Medicine, Columbia, Missouri, USA

2George Washington University School of Business, Washington, District of Columbia, USA

*Corresponding Author:
Scott Litofsky N
Division of Neurological Surgery, University of Missouri-Columbia School of Medicine
One Hospital Drive, MC 321, Columbia, Missouri 65212, USA
Tel: 573-882-4909
Fax: 573-884-5184
E-mail: [email protected]

Received Date: May 19, 2014; Accepted Date: June 10, 2014; Published Date: June 14, 2014

Citation: Sarpong Y, Ryder J, Litofsky NS (2014) Augmentation of Patient Health Care Choices for Morbid Obesity by Use of Computer Decision Analysis. J Comput Sci Syst Biol 7: 144-148. doi:10.4172/jcsb.1000149

Copyright: © 2014 Sarpong Y, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Abstract

Introduction: Many patients state that they frequently make the wrong choices when it comes to their healthcare treatment. These patients reported poor knowledge about the medical alternatives, physician biases, and lack of consideration of their goals and concerns in the treatment alternatives available led to these poor choices. However, current studies suggest that if patients are given aids that improve their knowledge and address their goals and concerns, they are able to make choices that are medically recommended as well as being right for them. We hypothesized that a computer model designed to improve knowledge and take into account patients’ concerns and goals will be able to aid patients in making such decisions. Methods: Using the Expert Choice Comparion system, we designed a program to assist morbidly obese patients in deciding which treatment options will be best suited for them. This system incorporated treatment objectives, treatment alternatives, pros and cons of each alternative, utility curves, and dynamic and performance sensitivity graphs to reach treatment recommendations. Patients were surveyed about their choices. Results: 8 patients from a convenience sample participated in decision analysis. Most chose reduction of co-morbidities, followed by treatment safety, followed by weight loss as their primary objectives. All patients were satisfied with their choice, all 8 felt their concerns were addressed and 7 of 8 were likely to follow recommendations. The program provided them with choices that meet national guidelines. Five of 8 patients described the ease of use of the program as moderate, 2 described it as excellent, and 1 described it as poor. Conclusions: Patients can use computer modeling to assist in making health choices for themselves.

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