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|Title:||Automatic message triage: a decision support system for patient-provider messages|
Archer, Norman P.
McMaster eBusiness Research Centre (MeRC)
|Keywords:||Text mining;Classification;Triage;Personal health records;Information systems;Decision support systems;Business;E-Commerce;Health and medical administration;Health information technology;Medicine and health sciences;Business|
|Series/Report no.:||MeRC working paper|
|Abstract:||<p><em>Background:</em> Email communication between patients and healthcare providers is gammg popularity. However, healthcare providers are concerned about being inundated with patient messages and their inability to respond to messages in a timely manner. This work provides a text mining decision support system to overcome some of the challenges presented by email communication between patients and healthcare providers.</p> <p><em>Method:</em> A decision support system based on text mining algorithms was developed and tested to triage real world email messages into medium and highly urgent messages that are routed to health provider staff, or low urgency messages that could be routed to an automated response system, responding to the messages in a timely and appropriate way.</p> <p><em>Results</em>: Due to the length of email messages, feature reduction algorithms are inadequate in this context. Therefore, in this work, several different classifiers were combined and tailored to build a high performance classifier that supports this type of classification. The system was tested and proved to perform well with real-world patient messages that were exchanged with healthcare providers during a hypertension management study.</p>|
|Description:||<p>1 v. (unpaged) ; Includes bibliographical references. ; "August 2012."</p> <p>This work was developed based on a thesis titled "Automatic Message Triage" submitted to School of Graduate Studies at McMaster University by the primary author in partial fulfillment of the requirements for the M.Sc. Degree in Computer Science. The second author of this work supervised the development of the thesis and this article.</p> <p>This research was supported through a grant from the Natural Sciences and Engineering Council of Canada. The authors would like to acknowledge the cooperation of the MyOSCAR research team in the Department of Family Medicine at McMaster University, including Dr. David Chan, Christine Rodrigues, and Dr. Lisa Dolovich, for providing the patient messages, triage levels, help in triaging the messages. Their help is greatly appreciated.</p>|
|Appears in Collections:||MeRC (McMaster eBusiness Research Centre) Working Paper Series|
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