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dc.contributor.advisorAnil K. Dubey.en_US
dc.contributor.authorScheufele, Elisabeth Leeen_US
dc.contributor.otherHarvard University--MIT Division of Health Sciences and Technology.en_US
dc.date.accessioned2009-10-01T15:52:55Z
dc.date.available2009-10-01T15:52:55Z
dc.date.copyright2009en_US
dc.date.issued2009en_US
dc.identifier.urihttp://hdl.handle.net/1721.1/47854
dc.descriptionThesis (S.M.)--Harvard-MIT Division of Health Sciences and Technology, 2009.en_US
dc.descriptionIncludes bibliographical references.en_US
dc.description.abstractClinical decision support systems (CDSS) are developed primarily from knowledge gleaned from evidence-based research, guidelines, trusted resources and domain experts. While these resources generally represent information that is research proven, time-tested and consistent with current medical knowledge, they lack some qualities that would be desirable in a CDSS. For instance, the information is presented as generalized recommendations that are not specific to particular patients and may not consider certain subpopulations. In addition, the knowledge base that produces the guidelines may be outdated and may not reflect real-world practice. Ideally, resources for decision support should be timely, patient-specific, and represent current practice. Patient-oriented clinical decision support is particularly important in the practice of pediatrics because it addresses a population in constant flux. Every age represents a different set of physiological and developmental concerns and considerations, especially in medication dosing patterns. Patient clinical data warehouses (CDW) may be able to bridge the knowledge gap. CDWs contain the collective intelligence of various contributors (i.e. clinicians, administrators, etc.) where each data entry provides information regarding medical care for a patient in the real world. CDWs have the potential to provide information as current as the latest upload, be focused to specific subpopulations and reflect current clinical practice. In this paper, I study the potential of a well-known patient clinical data warehouse to provide information regarding pediatric levothyroxine dosing as a form of clinical decision support. I study the state of the stored data, the necessary data transformations and options for representing the data to effectively summarize and communicate the findings.en_US
dc.description.abstract(cont.) I also compare the resulting transformed data, representing actual practice within this population, against established dosing recommendations. Of the transformed records, 728 of the 854 (85.2%, [95% confidence interval 82.7:87.6]) medication records contained doses that were under the published recommended range for levothyroxine. As demonstrated by these results, real world practice can diverge from established recommendations. Delivering this information on real-world peer practice medication dosing to clinicians in real-time offers the potential to provide a valuable supplement to established dosing guidelines, enhancing the general and sometimes static dosing recommendations.en_US
dc.description.statementofresponsibilityby Elisabeth Lee Scheufele.en_US
dc.format.extent66 leavesen_US
dc.language.isoengen_US
dc.publisherMassachusetts Institute of Technologyen_US
dc.rightsM.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.en_US
dc.rights.urihttp://dspace.mit.edu/handle/1721.1/7582en_US
dc.subjectHarvard University--MIT Division of Health Sciences and Technology.en_US
dc.titleMedication recommendations vs. peer practice in pediatric levothyroxine dosing : a study of collective intelligence from a clinical data warehouse as a potential model for clinical decision supporten_US
dc.title.alternativeStudy of collective intelligence form a clinical data warehouse as a potential model for clinical decision supporten_US
dc.typeThesisen_US
dc.description.degreeS.M.en_US
dc.contributor.departmentHarvard University--MIT Division of Health Sciences and Technology
dc.identifier.oclc430350117en_US


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