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Automatic message triage: a decision support system for patient-provider messages

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dc.contributor.authorTavasoli, Amiren_US
dc.contributor.authorArcher, Norman P.en_US
dc.contributor.authorMcMaster eBusiness Research Centre (MeRC)en_US
dc.date.accessioned2014-06-17T20:44:22Z
dc.date.available2014-06-17T20:44:22Z
dc.date.created2013-12-23en_US
dc.date.issued2012-08en_US
dc.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>en_US
dc.description.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>en_US
dc.identifier.othermerc/1en_US
dc.identifier.other1000en_US
dc.identifier.other4943335en_US
dc.identifier.urihttp://hdl.handle.net/11375/5312
dc.relation.ispartofseriesMeRC working paperen_US
dc.relation.ispartofseriesno. 42en_US
dc.subjectText miningen_US
dc.subjectClassificationen_US
dc.subjectTriageen_US
dc.subjectPersonal health recordsen_US
dc.subjectInformation systemsen_US
dc.subjectDecision support systemsen_US
dc.subjectBusinessen_US
dc.subjectE-Commerceen_US
dc.subjectHealth and medical administrationen_US
dc.subjectHealth information technologyen_US
dc.subjectMedicine and health sciencesen_US
dc.subjectBusinessen_US
dc.subject.lccElectronic mail messagesen_US
dc.subject.lccUser interfaces (Computer systems)
dc.subject.lccPhysician and patient
dc.titleAutomatic message triage: a decision support system for patient-provider messagesen_US
dc.typearticleen_US

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