2026-09-19T14:40:42Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/330842022-01-13T07:53:45Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Henry Church.
Rosenthal, Daniel Todd
Harvard University--MIT Division of Health Sciences and Technology.
Harvard University--MIT Division of Health Sciences and Technology
2006-06-19T17:39:05Z
2006-06-19T17:39:05Z
2005
2005
http://hdl.handle.net/1721.1/33084
62171955
Thesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2005.
Includes bibliographical references (p. 36-37).
Failure to follow-up on abnormal tests is a common clinical concern comprising the quality of care. Although many clinicians track their patient follow-up by scheduling follow-up visits or by leaving physical reminders, most feel that automated, computerized systems to track abnormal test results would be useful. While existing clinical decision support systems and computerized clinical reminders focus on providing assistance with choosing the appropriate follow-up management, they fail by not tracking that follow-up effectively. We believe that clinicians do not want suggestions how to manage their patients, but instead want help tracking follow-up results once they have decided the management plan. We believe that a well-designed system can successfully track this follow-up and only require a small amount of information and time from the clinician. We have designed and implemented a complete tracking system including 1) an authoring tool to define tracking guidelines, 2) a query tool to search electronic medical records and identify patients without follow-up, and 3) a clinical tool to send reminders to clinicians and allow them to easily choose the follow-up management. Our tracking system has made improvements on previous reminder systems by 1) using our unique risk-management guideline model that more closely mirrors, yet does not attempt to replicate, the clinical decision process, 2) our use of massive population-based queries for tracking all patients simultaneously, and 3) our longitudinal approach that documents all steps in the patient follow-up cycle. With these developments, we are able to track 450 million pieces of clinical data for 1.8 million patients daily.
(cont.) Keyword follow-up tracking; reminder system; preventive medicine; computerized medical record system; practice guidelines; clinical decision support system
by Daniel Todd Rosenthal.
S.M.
37 p.
2956224 bytes
2955565 bytes
application/pdf
application/pdf
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission.
http://dspace.mit.edu/handle/1721.1/7582
Harvard University--MIT Division of Health Sciences and Technology.
A clinician-mediated, longitudinal tracking system for the follow-up of clinical results
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<Abstract>Failure to follow-up on abnormal tests is a common clinical concern comprising the quality of care. Although many clinicians track their patient follow-up by scheduling follow-up visits or by leaving physical reminders, most feel that automated, computerized systems to track abnormal test results would be useful. While existing clinical decision support systems and computerized clinical reminders focus on providing assistance with choosing the appropriate follow-up management, they fail by not tracking that follow-up effectively. We believe that clinicians do not want suggestions how to manage their patients, but instead want help tracking follow-up results once they have decided the management plan. We believe that a well-designed system can successfully track this follow-up and only require a small amount of information and time from the clinician. We have designed and implemented a complete tracking system including 1) an authoring tool to define tracking guidelines, 2) a query tool to search electronic medical records and identify patients without follow-up, and 3) a clinical tool to send reminders to clinicians and allow them to easily choose the follow-up management. Our tracking system has made improvements on previous reminder systems by 1) using our unique risk-management guideline model that more closely mirrors, yet does not attempt to replicate, the clinical decision process, 2) our use of massive population-based queries for tracking all patients simultaneously, and 3) our longitudinal approach that documents all steps in the patient follow-up cycle. With these developments, we are able to track 450 million pieces of clinical data for 1.8 million patients daily.</Abstract>
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