Grace Gao

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.

A strengths-based data capture model: mining data-driven and person-centered health assets

Abstract With health care policy directives advancing value-based care, risk assessments and management have permeated health care discourse. The conventional problem-based infrastructure defines what data are employed to build this discourse and how it unfolds. Such a health care model tends to bias data for risk assessment and risk management toward problems and does not capture data about health assets or strengths. The purpose of this article is to explore and illustrate the incorporation of a strengths-based data capture model into risk assessment and management by harnessing data-driven and person-centered health assets using the Omaha System. This strengths-based data capture model encourages and enables use of whole-person data including strengths at the individual level and, in aggregate, at the population level. When aggregated, such data may be used for the development of strengths-based population health metrics that will promote evaluation of data-driven and person-centered care, outcomes, and value.

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.

Feasibility of Describing Wellbeing and Strengths at the Community Level Utilizing the Omaha System.

Capturing strengths at the community level offers an emergent perspective to a strength-based approach for population health. The Omaha System standardized terminology has been found feasible to describe individual strengths in patient care planning. This study depicts results of using the Omaha System to capture strengths at the community level. Descriptive statistics and visualization were used to examine patterns of strengths and signs/symptoms by Omaha System Problem concept based on the secondary data analysis from 118 student-generated community assessments. Results suggest that it is feasible to use the Omaha System as a method classifying strengths and problems at the community level. The relationship between strengths and signs/symptoms is consistent with the pattern observed at the individual-level. Utilizing a strength-based model may provide robust information about community strengths leading to new approaches to population health management in support of community wellbeing.

Documentation of Patient Problems and Strengths in Electronic Health Records

Background: A whole-person representation captures not only patient problems but also patient strengths. To better understand and inform practice of person-centered care and documentation using a whole-person representation, a critical review of literature was conducted of the current state of patient problems and strengths documentation in electronic health records. Methods: The informatics model of Data, Information, Knowledge and Wisdom is employed to develop this critical review. Two scientific databases were used to conduct a systematic search: CINAHL and Ovid Medline with the following search terms: strength*, problem*, whole person, wellbeing or well-being, electronic health record*, personal health record*, EHR*, and PHR*. 602 articles were returned. All articles were screened through review of titles, abstracts, or full texts. 24 articles were selected for this review. Results: Four themes have emerged from this critical review. They are individual or cross-institutional use of problem-oriented EHRs, extension of problem-based EHRs with other integration, patient-centered integration of the problem-oriented EHR build, and construction of a whole-person representation to include strengths in the EHR documentation. The vast majority of articles focus on problem-based diagnoses and practices. Early reports of strengths documentation were found using a standardized interface terminology and ontology, the Omaha System. Results of two studies demonstrated the feasibility of using the Omaha System for whole-person documentation to capture perception of both problems and strengths. Conclusion: Clinical information in EHRs is typically structured by problem-based diagnoses; however, there is emerging documentation of formalized strengths attributes using the Omaha System, which may promote a holistic approach to clinical practice and documentation using a person-centered, strength-based ontology.