Partners healthcare order set schema: An information model for management of clinical content

Margarita Sordo, Tonya Hongsermeier, Vipul Kashyap, Robert Greenes

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)

Abstract

Developed by the Clinical Knowledge Management and Decision Support Group at Partners HealthCare and the Decision Systems Group at Harvard Medical School, the XML-based Order Set Schema presented in this chapter is the result of a broader enterprise-wide knowledge management effort to enhance quality, safety, and efficiency of provided care at Partners HealthCare while maximizing the use of new clinical information technology. We are in the process of deploying the Order Set Schema at two Partners-based, Harvard-affiliated academic medical centers the Brigham & Women's Hospital (BWH) and Massachusetts General Hospital (MGH), Boston, MA, so that existing content in the Computerized Physician Order Entry (CPOE) systems at these two institutions can be successfully extracted and mapped into the proposed schema. In this way, "hardwired" knowledge could be mapped into taxonomies of relevant terms, definitions and associations, resulting in formalized conceptual models and ontologies with explicit, consistent, user-meaningful relationships among concepts to support collaboration, and content management that will promote systematic (a) conversion of reference content into a form that approaches specifications for decision support content; (b) development and reuse of clinical content while ensuring consistency in the information; and (c) support an open and distributed review process among leadership, content matter experts, and end-users. Further, incorporating metadata into our unified content strategy will improve workflow by enabling timely review and updating of content, knowledge life-cycle management, and knowledge encoding; reduce costs and; aid authors to identify relevant elements for reuse while reducing redundant and spurious content. Ultimately, we view our knowledge management infrastructure as a key element for knowledge discovery.

Original languageEnglish (US)
Title of host publicationStudies in Computational Intelligence
Pages1-25
Number of pages25
Volume48
DOIs
StatePublished - 2007
Externally publishedYes

Publication series

NameStudies in Computational Intelligence
Volume48
ISSN (Print)1860949X

Fingerprint

Knowledge management
Taxonomies
Metadata
XML
Information technology
Data mining
Ontology
Life cycle
Specifications
Costs
Industry

ASJC Scopus subject areas

  • Artificial Intelligence

Cite this

Sordo, M., Hongsermeier, T., Kashyap, V., & Greenes, R. (2007). Partners healthcare order set schema: An information model for management of clinical content. In Studies in Computational Intelligence (Vol. 48, pp. 1-25). (Studies in Computational Intelligence; Vol. 48). https://doi.org/10.1007/978-3-540-47527-9_1

Partners healthcare order set schema : An information model for management of clinical content. / Sordo, Margarita; Hongsermeier, Tonya; Kashyap, Vipul; Greenes, Robert.

Studies in Computational Intelligence. Vol. 48 2007. p. 1-25 (Studies in Computational Intelligence; Vol. 48).

Research output: Chapter in Book/Report/Conference proceedingChapter

Sordo, M, Hongsermeier, T, Kashyap, V & Greenes, R 2007, Partners healthcare order set schema: An information model for management of clinical content. in Studies in Computational Intelligence. vol. 48, Studies in Computational Intelligence, vol. 48, pp. 1-25. https://doi.org/10.1007/978-3-540-47527-9_1
Sordo M, Hongsermeier T, Kashyap V, Greenes R. Partners healthcare order set schema: An information model for management of clinical content. In Studies in Computational Intelligence. Vol. 48. 2007. p. 1-25. (Studies in Computational Intelligence). https://doi.org/10.1007/978-3-540-47527-9_1
Sordo, Margarita ; Hongsermeier, Tonya ; Kashyap, Vipul ; Greenes, Robert. / Partners healthcare order set schema : An information model for management of clinical content. Studies in Computational Intelligence. Vol. 48 2007. pp. 1-25 (Studies in Computational Intelligence).
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