Jung In Park

Big data science: A literature review of nursing research exemplars

BACKGROUND Big data and cutting-edge analytic methods in nursing research challenge nurse scientists to extend the data sources and analytic methods used for discovering and translating knowledge. PURPOSE The purpose of this study was to identify, analyze, and synthesize exemplars of big data nursing research applied to practice and disseminated in key nursing informatics, general biomedical informatics, and nursing research journals. METHODS A literature review of studies published between 2009 and 2015. There were 650 journal articles identified in 17 key nursing informatics, general biomedical informatics, and nursing research journals in the Web of Science database. After screening for inclusion and exclusion criteria, 17 studies published in 18 articles were identified as big data nursing research applied to practice. DISCUSSION Nurses clearly are beginning to conduct big data research applied to practice. These studies represent multiple data sources and settings. Although numerous analytic methods were used, the fundamental issue remains to define the types of analyses consistent with big data analytic methods. CONCLUSION There are needs to increase the visibility of big data and data science research conducted by nurse scientists, further examine the use of state of the science in data analytics, and continue to expand the availability and use of a variety of scientific, governmental, and industry data resources. A major implication of this literature review is whether nursing faculty and preparation of future scientists (PhD programs) are prepared for big data and data science.

Modeling flowsheet data to support secondary use

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 (eg, 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 because of 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 thromboembolism, 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 1552 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.

Applied nursing informatics research - state-of-the-art methodologies using electronic health record data.

With the pervasive implementation of electronic health records (EHR), new opportunities arise for nursing research through use of EHR data. Increasingly, comparative effectiveness research within and across health systems is conducted to identify the impact of nursing for improving health, health care, and lowering costs of care. Use of EHR data for this type of research requires use of national and internationally recognized nursing terminologies to normalize data. Research methods are evolving as large data sets become available through EHRs. Little is known about the types of research and analytic methods for applied to nursing research using EHR data normalized with nursing terminologies. The purpose of this paper is to report on a subset of a systematic review of peer reviewed studies related to applied nursing informatics research involving EHR data using standardized nursing terminologies.

Inclusion of Flowsheets from Electronic Health Records to Extend Data for Clinical and Translational Science Awards (CTSA) Research

Clinical data repositories increasingly are used for big data science; flowsheet data can extend current CDRs with rich, highly granular data documented by nursing and other healthcare professionals. Standardization of the data, however, is required for it to be useful for big data science. In this chapter, an example of one CDR funded by NIH’s CTSA demonstrates how flowsheet data can add data repositories for big data science. A specific example of pressure ulcers demonstrates the strengths of flowsheet data and also the challenges of using this data. Through standardization of this highly granular data documented by nurses, a more precise understanding about patient characteristics and tailoring of interventions provided by the health team and patient conditions and states can be achieved. Additional efforts by national workgroups to create information models from flowsheets and standardize assessment terms are described to support big data science.