TY - CHAP
T1 - Partners healthcare order set schema
T2 - An information model for management of clinical content
AU - Sordo, Margarita
AU - Hongsermeier, Tonya
AU - Kashyap, Vipul
AU - Greenes, Robert A.
PY - 2007/5/8
Y1 - 2007/5/8
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=34247619492&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=34247619492&partnerID=8YFLogxK
U2 - 10.1007/978-3-540-47527-9_1
DO - 10.1007/978-3-540-47527-9_1
M3 - Chapter
AN - SCOPUS:34247619492
SN - 3540475230
SN - 9783540475231
T3 - Studies in Computational Intelligence
SP - 1
EP - 25
BT - Advanced Computational Intelligence Paradigms in Healthcare - 1:
A2 - Yoshida, Hiro
A2 - Jain, Ashlesha
A2 - Ichalkaranje, Ajita
A2 - Jain, Lakhmi
A2 - Ichalkaranje, Nikhil
ER -