Standardizing Physiologic Assessment Data to Enable Big Data Analytics

Disparate data must be represented in a common format to enable comparison across multiple institutions and facilitate Big Data science. Nursing assessments represent a rich source of information. However, a lack of agreement regarding essential concepts and standardized terminology prevent their use for Big Data science in the current state. The purpose of this study was to align a minimum set of physiological nursing assessment data elements with national standardized coding systems. Six institutions shared their 100 most common electronic health record nursing assessment data elements. From these, a set of distinct elements was mapped to nationally recognized Logical Observations Identifiers Names and Codes (LOINC®) and Systematized Nomenclature of Medicine–Clinical Terms (SNOMED CT®) standards. We identified 137 observation names (55% new to LOINC), and 348 observation values (20% new to SNOMED CT) organized into 16 panels (72% new LOINC). This reference set can support the exchange of nursing information, facilitate multi-site research, and provide a framework for nursing data analysis.

[1]  Stanley M. Huff,et al.  Representing nursing assessments in clinical information systems using the logical observation identifiers, names, and codes database , 2003, J. Biomed. Informatics.

[2]  M. Naylor,et al.  Conducting Research Using the Electronic Health Record Across Multi–Hospital Systems: Semantic Harmonization Implications for Administrators , 2013, The Journal of nursing administration.

[3]  Bonnie L. Westra,et al.  A call to action: Engage in big data science , 2014 .

[4]  Virginia K. Saba,et al.  Clinical Care Classification (CCC) System Manual: A Guide to Nursing Documentation , 2006 .

[5]  Clement J. McDonald,et al.  Development of the Logical Observation Identifier Names and Codes (LOINC) vocabulary. , 1998, Journal of the American Medical Informatics Association : JAMIA.

[6]  PhD Faan Facmi Patricia Flatley Brennan Rn,et al.  Nursing Needs Big Data and Big Data Needs Nursing , 2015 .

[7]  Jung In Park,et al.  A national action plan for sharable and comparable nursing data to support practice and translational research for transforming health care , 2015, J. Am. Medical Informatics Assoc..

[8]  Suzanne Bakken,et al.  An evaluation of the usefulness of two terminology models for integrating nursing diagnosis concepts into SNOMED Clinical Terms® , 2002, Int. J. Medical Informatics.

[9]  Lemuel R Waitman,et al.  Expressing observations from electronic medical record flowsheets in an i2b2 based clinical data repository to support research and quality improvement. , 2011, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[10]  Daniel J Vreeman,et al.  Representing Patient Assessments in LOINC®. , 2010, AMIA ... Annual Symposium proceedings. AMIA Symposium.

[11]  Patricia C. Dykes,et al.  Harmonizing and extending standards from a domain-specific and bottom-up approach: an example from development through use in clinical applications , 2015, J. Am. Medical Informatics Assoc..

[12]  Amy Sheide,et al.  Communicating Nursing Care Using the Health Level Seven Consolidated Clinical Document Architecture Release 2 Care Plan , 2016, Computers, informatics, nursing : CIN.

[13]  C. McDonald,et al.  Logical observation identifier names and codes (LOINC) database: a public use set of codes and names for electronic reporting of clinical laboratory test results. , 1996, Clinical chemistry.