Medication 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 support

Clinical 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 In addition, the knowledge base that produces the guidelines may be outdated and may not reflect real-world practice.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.

Department dc:contributor.department

Harvard University--MIT Division of Health Sciences and Technology.

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2009

Author dc:creator

• Scheufele, Elisabeth Lee

Advisor dc:contributor.advisor

• Anil K. Dubey.

• Harvard University--MIT Division of Health Sciences and Technology.

Statement dc:rights

• 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.

Licence dc:rights.uri

• http://dspace.mit.edu/handle/1721.1/7582

Language dc:language.iso

eng

Handle dc:identifier.uri

http://hdl.handle.net/1721.1/47854

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/47854