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Macieira TGR, Chianca TCM, Smith MB, Yao Y, Bian J, Wilkie DJ, Dunn Lopez K, Keenan GM. Secondary use of standardized nursing care data for advancing nursing science and practice: a systematic review. J Am Med Inform Assoc 2021; 26:1401-1411. [PMID: 31188439 DOI: 10.1093/jamia/ocz086] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2019] [Revised: 05/04/2019] [Accepted: 05/09/2019] [Indexed: 11/14/2022] Open
Abstract
OBJECTIVE The study sought to present the findings of a systematic review of studies involving secondary analyses of data coded with standardized nursing terminologies (SNTs) retrieved from electronic health records (EHRs). MATERIALS AND METHODS We identified studies that performed secondary analysis of SNT-coded nursing EHR data from PubMed, CINAHL, and Google Scholar. We screened 2570 unique records and identified 44 articles of interest. We extracted research questions, nursing terminologies, sample characteristics, variables, and statistical techniques used from these articles. An adapted STROBE (Strengthening The Reporting of OBservational Studies in Epidemiology) Statement checklist for observational studies was used for reproducibility assessment. RESULTS Forty-four articles were identified. Their study foci were grouped into 3 categories: (1) potential uses of SNT-coded nursing data or challenges associated with this type of data (feasibility of standardizing nursing data), (2) analysis of SNT-coded nursing data to describe the characteristics of nursing care (characterization of nursing care), and (3) analysis of SNT-coded nursing data to understand the impact or effectiveness of nursing care (impact of nursing care). The analytical techniques varied including bivariate analysis, data mining, and predictive modeling. DISCUSSION SNT-coded nursing data extracted from EHRs is useful in characterizing nursing practice and offers the potential for demonstrating its impact on patient outcomes. CONCLUSIONS Our study provides evidence of the value of SNT-coded nursing data in EHRs. Future studies are needed to identify additional useful methods of analyzing SNT-coded nursing data and to combine nursing data with other data elements in EHRs to fully characterize the patient's health care experience.
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Affiliation(s)
| | - Tania C M Chianca
- Department of Basic Nursing, School of Nursing, Federal University of Minas Gerais, Belo Horizonte, Brazil
| | - Madison B Smith
- College of Nursing, University of Florida, Gainesville, Florida, USA
| | - Yingwei Yao
- Department of Biobehavioral Nursing Science, College of Nursing, University of Florida, Gainesville, Florida, USA
| | - Jiang Bian
- Department of Health Outcomes & Biomedical Informatics, College of Medicine, University of Florida, Gainesville, Florida, USA
| | - Diana J Wilkie
- Department of Biobehavioral Nursing Science, College of Nursing, University of Florida, Gainesville, Florida, USA
| | - Karen Dunn Lopez
- Biomedical and Health Information Science, College of Applied Health Sciences, University of Illinois at Chicago, Chicago, Illinois, USA
| | - Gail M Keenan
- Department of Family, Community and Health Systems Science, College of Nursing, University of Florida, Gainesville, Florida, USA
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Macieira TGR, Smith MB, Davis N, Yao Y, Wilkie DJ, Lopez KD, Keenan G. Evidence of Progress in Making Nursing Practice Visible Using Standardized Nursing Data: a Systematic Review. AMIA ... ANNUAL SYMPOSIUM PROCEEDINGS. AMIA SYMPOSIUM 2018; 2017:1205-1214. [PMID: 29854189 PMCID: PMC5977718] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Subscribe] [Scholar Register] [Indexed: 06/08/2023]
Abstract
Nursing care documentation in electronic health records (EHRs) with standardized nursing terminologies (SNTs) can facilitate nursing's participation in big data science that involves combining and analyzing multiple sources of data. Before merging SNTs data with other sources, it is important to understand how such data are being used and analyzed to support nursing practice. The main purpose of this systematic review was to identify studies using SNTs data, their aims and analytical methods. A two-phase systematic process resulted in inclusion and review of 35 publications. Aims of the studies ranged from describing most popular nursing diagnoses, outcomes, and interventions on a unit to predicting outcomes using multi-site data. Analytical techniques varied as well and included descriptive statistics, correlations, data mining, and predictive modeling. The review underscored the value of developing a deep understanding of the meaning and potential impact of nursing variables before merging with other sources of data.
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Van Poucke S, Thomeer M, Heath J, Vukicevic M. Are Randomized Controlled Trials the (G)old Standard? From Clinical Intelligence to Prescriptive Analytics. J Med Internet Res 2016; 18:e185. [PMID: 27383622 PMCID: PMC4954919 DOI: 10.2196/jmir.5549] [Citation(s) in RCA: 34] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/21/2016] [Revised: 06/01/2016] [Accepted: 06/21/2016] [Indexed: 12/11/2022] Open
Abstract
Despite the accelerating pace of scientific discovery, the current clinical research enterprise does not sufficiently address pressing clinical questions. Given the constraints on clinical trials, for a majority of clinical questions, the only relevant data available to aid in decision making are based on observation and experience. Our purpose here is 3-fold. First, we describe the classic context of medical research guided by Poppers' scientific epistemology of "falsificationism." Second, we discuss challenges and shortcomings of randomized controlled trials and present the potential of observational studies based on big data. Third, we cover several obstacles related to the use of observational (retrospective) data in clinical studies. We conclude that randomized controlled trials are not at risk for extinction, but innovations in statistics, machine learning, and big data analytics may generate a completely new ecosystem for exploration and validation.
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Affiliation(s)
- Sven Van Poucke
- Department of Anesthesiology, Critical Care, Emergency Medicine, Pain Therapy, Ziekenhuis Oost-Limburg, Genk, Belgium.
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Van Poucke S, Boer W. Acetaminophen in critically ill patients, a therapy in search for big data analytics. J Thorac Dis 2016; 8:E109-10. [PMID: 26904235 DOI: 10.3978/j.issn.2072-1439.2015.12.69] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/14/2022]
Affiliation(s)
- Sven Van Poucke
- Department of Anesthesiology, Critical Care, Emergency Medicine and Pain Therapy, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
| | - Willem Boer
- Department of Anesthesiology, Critical Care, Emergency Medicine and Pain Therapy, Ziekenhuis Oost-Limburg, 3600 Genk, Belgium
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Poucke SV, Zhang Z, Schmitz M, Vukicevic M, Laenen MV, Celi LA, Deyne CD. Scalable Predictive Analysis in Critically Ill Patients Using a Visual Open Data Analysis Platform. PLoS One 2016; 11:e0145791. [PMID: 26731286 PMCID: PMC4701479 DOI: 10.1371/journal.pone.0145791] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2015] [Accepted: 12/08/2015] [Indexed: 02/07/2023] Open
Abstract
With the accumulation of large amounts of health related data, predictive analytics could stimulate the transformation of reactive medicine towards Predictive, Preventive and Personalized (PPPM) Medicine, ultimately affecting both cost and quality of care. However, high-dimensionality and high-complexity of the data involved, prevents data-driven methods from easy translation into clinically relevant models. Additionally, the application of cutting edge predictive methods and data manipulation require substantial programming skills, limiting its direct exploitation by medical domain experts. This leaves a gap between potential and actual data usage. In this study, the authors address this problem by focusing on open, visual environments, suited to be applied by the medical community. Moreover, we review code free applications of big data technologies. As a showcase, a framework was developed for the meaningful use of data from critical care patients by integrating the MIMIC-II database in a data mining environment (RapidMiner) supporting scalable predictive analytics using visual tools (RapidMiner's Radoop extension). Guided by the CRoss-Industry Standard Process for Data Mining (CRISP-DM), the ETL process (Extract, Transform, Load) was initiated by retrieving data from the MIMIC-II tables of interest. As use case, correlation of platelet count and ICU survival was quantitatively assessed. Using visual tools for ETL on Hadoop and predictive modeling in RapidMiner, we developed robust processes for automatic building, parameter optimization and evaluation of various predictive models, under different feature selection schemes. Because these processes can be easily adopted in other projects, this environment is attractive for scalable predictive analytics in health research.
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Affiliation(s)
- Sven Van Poucke
- Department of Anesthesiology, Intensive Care, Emergency Medicine and Pain Therapy, Ziekenhuis Oost-Limburg, Genk, Belgium
- * E-mail:
| | - Zhongheng Zhang
- Department of Critical Care Medicine, Jinhua Hospital of Zhejiang University, Zhejiang, P.R. China
| | | | - Milan Vukicevic
- Department of Organizational Sciences, University of Belgrade, Belgrade, Serbia
| | - Margot Vander Laenen
- Department of Anesthesiology, Intensive Care, Emergency Medicine and Pain Therapy, Ziekenhuis Oost-Limburg, Genk, Belgium
| | - Leo Anthony Celi
- MIT Institute for Medical Engineering and Science, Massachusetts Institute of Technology, Cambridge, Massachusetts, United States
| | - Cathy De Deyne
- Department of Anesthesiology, Intensive Care, Emergency Medicine and Pain Therapy, Ziekenhuis Oost-Limburg, Genk, Belgium
- Limburg Clinical Research Program, Faculty of Medicine, University Hasselt UH, Hasselt, Belgium
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FitzHenry F, Resnic FS, Robbins SL, Denton J, Nookala L, Meeker D, Ohno-Machado L, Matheny ME. Creating a Common Data Model for Comparative Effectiveness with the Observational Medical Outcomes Partnership. Appl Clin Inform 2015; 6:536-47. [PMID: 26448797 DOI: 10.4338/aci-2014-12-cr-0121] [Citation(s) in RCA: 56] [Impact Index Per Article: 6.2] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2014] [Accepted: 07/17/2015] [Indexed: 12/22/2022] Open
Abstract
BACKGROUND Adoption of a common data model across health systems is a key infrastructure requirement to allow large scale distributed comparative effectiveness analyses. There are a growing number of common data models (CDM), such as Mini-Sentinel, and the Observational Medical Outcomes Partnership (OMOP) CDMs. OBJECTIVES In this case study, we describe the challenges and opportunities of a study specific use of the OMOP CDM by two health systems and describe three comparative effectiveness use cases developed from the CDM. METHODS The project transformed two health system databases (using crosswalks provided) into the OMOP CDM. Cohorts were developed from the transformed CDMs for three comparative effectiveness use case examples. Administrative/billing, demographic, order history, medication, and laboratory were included in the CDM transformation and cohort development rules. RESULTS Record counts per person month are presented for the eligible cohorts, highlighting differences between the civilian and federal datasets, e.g. the federal data set had more outpatient visits per person month (6.44 vs. 2.05 per person month). The count of medications per person month reflected the fact that one system's medications were extracted from orders while the other system had pharmacy fills and medication administration records. The federal system also had a higher prevalence of the conditions in all three use cases. Both systems required manual coding of some types of data to convert to the CDM. CONCLUSIONS The data transformation to the CDM was time consuming and resources required were substantial, beyond requirements for collecting native source data. The need to manually code subsets of data limited the conversion. However, once the native data was converted to the CDM, both systems were then able to use the same queries to identify cohorts. Thus, the CDM minimized the effort to develop cohorts and analyze the results across the sites.
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Affiliation(s)
- F FitzHenry
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center , Nashville, TN ; Department of Biomedical Informatics; Vanderbilt University, Nashville , TN
| | - F S Resnic
- Division of Cardiology, Brigham and Women's Hospital , Boston, MA
| | - S L Robbins
- Division of Cardiology, Brigham and Women's Hospital , Boston, MA
| | - J Denton
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center , Nashville, TN ; Division of General Internal Medicine and Public Health, Vanderbilt University , Nashville, TN
| | - L Nookala
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center , Nashville, TN ; Division of General Internal Medicine and Public Health, Vanderbilt University , Nashville, TN
| | - D Meeker
- Department of Health, RAND Corporation, Santa Monica , CA
| | - L Ohno-Machado
- Division of Biomedical Informatics, University of California , San Diego, CA
| | - M E Matheny
- Tennessee Valley Healthcare System, Veterans Affairs Medical Center , Nashville, TN ; Department of Biomedical Informatics; Vanderbilt University, Nashville , TN ; Division of General Internal Medicine and Public Health, Vanderbilt University , Nashville, TN ; Department of Biostatistics, Vanderbilt University , Nashville, TN
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