The guideline recommendations showed that shorter delays indicates better outcomes. There was no evidence that 3 hours is safe; even very short delays adversely impact outcomes. Findings demonstrated a new approach to incorporate time t when analyzing the impact on outcomes and provide new evidence for clinical practice and research.
Foundational to integrating nursing data into clinical data repositories for big data and science, is the implementation of standardized nursing terminologies, common data models, and information structures within EHRs. The 2014 National Action Plan for Sharable and Comparable Nursing Data for Transforming Health and Healthcare builds on and leverages existing, but separate long standing efforts of many individuals and organizations. The plan is action focused, with accountability for coordinating and tracking progress designated.
Interprofessional education (IPE) and collaborative practice (CP) have been prolific areas of inquiry exploring research questions mostly concerned with local program and project assessment. The actual sphere of influence of this research has been limited. Often discussed separately, this article places IPE and CP in the same conceptual space. The interface of these form a nexus where new knowledge creation may be facilitated. Rigorous research on IPE in relation to CP that is relevant to and framed by health system reform in the U.S. is the ultimate research goal of the National Center for Interprofessional Practice and Education at the University of Minnesota. This paper describes the direction and scope for a focused and purposive IPECP research agenda linked to improvement in health outcomes, contextualized by health care reform in the U.S. that has provided a revitalizing energy for this area of inquiry. A research agenda articulates a focus, meaningful and robust questions, and a theory of change within which intervention outcomes are examined. Further, a research agenda identifies the practices the area of inquiry is interested in informing, and the types of study designs and analytic approaches amenable to carrying out the proposed work.
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.
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