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Grace Xingxin Gao (M'12) received the B.S. degree in mechanical engineering and the M.S. degree in electrical engineering

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The purpose of this study was to create information models from flowsheet data using a data-driven consensus based method. Electronic health records contain a large volume of data about patient assessments and interventions captured in flowsheets that measure the same “thing,” but the names of these observations often differ, according to who performs documentation or the location of the service (e.g., pulse rate in an intensive care, the emergency department, or a surgical unit documented by a nurse or therapist or captured by automated monitoring). Flowsheet data are challenging for secondary use due to the existence of multiple semantically equivalent measures representing the same concepts. Ten information models were created in this study: five related to quality measures (falls, pressure ulcers, venous thrombosis embolism, genitourinary system including catheter associated urinary tract infection, and pain management) and five high volume physiological systems: cardiac, gastrointestinal, musculoskeletal, respiratory, and expanded vital signs/anthropometrics. The value of the information models is that flowsheet data can be extracted and mapped for semantically comparable flowsheet measures from a clinical data repository regardless of the time frame, discipline, or setting in which documentation occurred. The 10 information models simplify the representation of the content in flowsheet data, reducing 1,552 source measures to 557 concepts. The amount of representational reduction ranges from 3% for Falls to 78% for the Respiratory System. The information models provide a foundation for including nursing and interprofessional assessments and interventions in common data models, to support research within and across health systems.