Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar

Kay S. Lytle; Bonnie L. Westra; Luann Whittenburg; Mischa Adams; Mari Akre; Samira Ali; Meg Furukawa; Stephanie Hartleben; Mary Hook; Steven G. Johnson; Theresa Settergren; Mariaelena Thibodeaux

doi:10.1111/jnu.12646

Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar

Kay S. Lytle, Bonnie L. Westra, Luann Whittenburg, Mischa Adams, Mari Akre, Samira Ali, Meg Furukawa, Stephanie Hartleben, Mary Hook, Steven G. Johnson, Theresa Settergren, Mariaelena Thibodeaux

Institute for Health Informatics

Research output: Contribution to journal › Article › peer-review

9 Scopus citations

Abstract

Purpose: The rapid implementation of electronic health records (EHRs) resulted in a lack of data standardization and created considerable difficulty for secondary use of EHR documentation data within and between organizations. While EHRs contain documentation data (input), nurses and healthcare organizations rarely have useable documentation data (output). The purpose of this article is to describe a method of standardizing EHR flowsheet documentation data using information models (IMs) to support exchange, quality improvement, and big data research. As an exemplar, EHR flowsheet metadata (input) from multiple organizations was used to validate a fall prevention IM. Design: A consensus-based, qualitative, descriptive approach was used to identify a minimum set of essential fall prevention data concepts documented by staff nurses in acute care. The goal was to increase generalizable and comparable nurse-sensitive data on the prevention of falls across organizations for big data research. Methods: The research team conducted a retrospective, observational study using an iterative, consensus-based approach to map, analyze, and evaluate nursing flowsheet metadata contributed by eight health systems. The team used FloMap software to aggregate flowsheet data across organizations for mapping and comparison of data to a reference IM. The FloMap analysis was refined with input from staff nurse subject matter experts, review of published evidence, current documentation standards, Magnet Recognition nursing standards, and informal fall prevention nursing use cases. Findings: Flowsheet metadata analyzed from the EHR systems represented 6.6 million patients, 27 million encounters, and 683 million observations. Compared to the original reference IM, five new IM classes were added, concepts were reduced by 14 (from 57 to 43), and 157 value set items were added. The final fall prevention IM incorporated 11 condition or age-specific fall risk screening tools and a fall event details class with 14 concepts. Conclusion: The iterative, consensus-based refinement and validation of the fall prevention IM from actual EHR fall prevention flowsheet documentation contributes to the ability to semantically exchange and compare fall prevention data across multiple health systems and organizations. This method and approach provides a process for standardizing flowsheet data as coded data for information exchange and use in big data research. Clinical Relevance: Opportunities exist to work with EHR vendors and the Office of the National Coordinator for Health Information Technology to implement standardized IMs within EHRs to expand interoperability of nurse-sensitive data.

Original language	English (US)
Pages (from-to)	306-314
Number of pages	9
Journal	Journal of Nursing Scholarship
Volume	53
Issue number	3
DOIs	https://doi.org/10.1111/jnu.12646
State	Published - May 2021

Bibliographical note

Funding Information:
The authors wish to acknowledge the support of the Nursing Knowledge Big Data Science Initiative (http://z.umn.edu/bigdata) and the following Information Modeling Workgroup members for their contributions: Rivka Atadja, RN (Allina Health); Mikyoung Lee, PhD, RN (Texas Woman’s University College of Nursing); Tari Rajchel, DNP, RN (North Memorial Medical Center); Darinda Sutton, MSN, RN-BC, FACHE (Cerner Corporation); and Joe Zillmer (Epic Systems). Clinical Resources Agency for Healthcare Research and Quality. Preventing falls in hospitals. https://www.ahrq.gov/professionals/systems/hospital/fallpxtoolkit/index.html Patient Safety Movement. Actionable Patient Safety Solution (APSS) #14: Falls and fall prevention. https://patientsafetymovement.org/wp-content/uploads/2017/10/APSS-14_-Falls-and-Fall-Prevention-081518-1.pdf Agency for Healthcare Research and Quality. Preventing falls in hospitals. https://www.ahrq.gov/professionals/systems/hospital/fallpxtoolkit/index.html Patient Safety Movement. Actionable Patient Safety Solution (APSS) #14: Falls and fall prevention. https://patientsafetymovement.org/wp-content/uploads/2017/10/APSS-14_-Falls-and-Fall-Prevention-081518-1.pdf

Publisher Copyright:
© 2021 Sigma Theta Tau International

Keywords

Big data
data exchange
electronic health records
information models
secondary use

PubMed: MeSH publication types

Journal Article
Observational Study

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10.1111/jnu.12646

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@article{44acc75adf594f5c9c17c625017eff13,

title = "Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar",

abstract = "Purpose: The rapid implementation of electronic health records (EHRs) resulted in a lack of data standardization and created considerable difficulty for secondary use of EHR documentation data within and between organizations. While EHRs contain documentation data (input), nurses and healthcare organizations rarely have useable documentation data (output). The purpose of this article is to describe a method of standardizing EHR flowsheet documentation data using information models (IMs) to support exchange, quality improvement, and big data research. As an exemplar, EHR flowsheet metadata (input) from multiple organizations was used to validate a fall prevention IM. Design: A consensus-based, qualitative, descriptive approach was used to identify a minimum set of essential fall prevention data concepts documented by staff nurses in acute care. The goal was to increase generalizable and comparable nurse-sensitive data on the prevention of falls across organizations for big data research. Methods: The research team conducted a retrospective, observational study using an iterative, consensus-based approach to map, analyze, and evaluate nursing flowsheet metadata contributed by eight health systems. The team used FloMap software to aggregate flowsheet data across organizations for mapping and comparison of data to a reference IM. The FloMap analysis was refined with input from staff nurse subject matter experts, review of published evidence, current documentation standards, Magnet Recognition nursing standards, and informal fall prevention nursing use cases. Findings: Flowsheet metadata analyzed from the EHR systems represented 6.6 million patients, 27 million encounters, and 683 million observations. Compared to the original reference IM, five new IM classes were added, concepts were reduced by 14 (from 57 to 43), and 157 value set items were added. The final fall prevention IM incorporated 11 condition or age-specific fall risk screening tools and a fall event details class with 14 concepts. Conclusion: The iterative, consensus-based refinement and validation of the fall prevention IM from actual EHR fall prevention flowsheet documentation contributes to the ability to semantically exchange and compare fall prevention data across multiple health systems and organizations. This method and approach provides a process for standardizing flowsheet data as coded data for information exchange and use in big data research. Clinical Relevance: Opportunities exist to work with EHR vendors and the Office of the National Coordinator for Health Information Technology to implement standardized IMs within EHRs to expand interoperability of nurse-sensitive data.",

keywords = "Big data, data exchange, electronic health records, information models, secondary use",

author = "Lytle, {Kay S.} and Westra, {Bonnie L.} and Luann Whittenburg and Mischa Adams and Mari Akre and Samira Ali and Meg Furukawa and Stephanie Hartleben and Mary Hook and Johnson, {Steven G.} and Theresa Settergren and Mariaelena Thibodeaux",

note = "Funding Information: The authors wish to acknowledge the support of the Nursing Knowledge Big Data Science Initiative (http://z.umn.edu/bigdata) and the following Information Modeling Workgroup members for their contributions: Rivka Atadja, RN (Allina Health); Mikyoung Lee, PhD, RN (Texas Woman{\textquoteright}s University College of Nursing); Tari Rajchel, DNP, RN (North Memorial Medical Center); Darinda Sutton, MSN, RN-BC, FACHE (Cerner Corporation); and Joe Zillmer (Epic Systems). Clinical Resources Agency for Healthcare Research and Quality. Preventing falls in hospitals. https://www.ahrq.gov/professionals/systems/hospital/fallpxtoolkit/index.html Patient Safety Movement. Actionable Patient Safety Solution (APSS) #14: Falls and fall prevention. https://patientsafetymovement.org/wp-content/uploads/2017/10/APSS-14_-Falls-and-Fall-Prevention-081518-1.pdf Agency for Healthcare Research and Quality. Preventing falls in hospitals. https://www.ahrq.gov/professionals/systems/hospital/fallpxtoolkit/index.html Patient Safety Movement. Actionable Patient Safety Solution (APSS) #14: Falls and fall prevention. https://patientsafetymovement.org/wp-content/uploads/2017/10/APSS-14_-Falls-and-Fall-Prevention-081518-1.pdf Publisher Copyright: {\textcopyright} 2021 Sigma Theta Tau International",

year = "2021",

month = may,

doi = "10.1111/jnu.12646",

language = "English (US)",

volume = "53",

pages = "306--314",

journal = "Journal of Nursing Scholarship",

issn = "1527-6546",

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TY - JOUR

T1 - Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data

T2 - A Fall Prevention Exemplar

AU - Lytle, Kay S.

AU - Westra, Bonnie L.

AU - Whittenburg, Luann

AU - Adams, Mischa

AU - Akre, Mari

AU - Ali, Samira

AU - Furukawa, Meg

AU - Hartleben, Stephanie

AU - Hook, Mary

AU - Johnson, Steven G.

AU - Settergren, Theresa

AU - Thibodeaux, Mariaelena

N1 - Funding Information: The authors wish to acknowledge the support of the Nursing Knowledge Big Data Science Initiative (http://z.umn.edu/bigdata) and the following Information Modeling Workgroup members for their contributions: Rivka Atadja, RN (Allina Health); Mikyoung Lee, PhD, RN (Texas Woman’s University College of Nursing); Tari Rajchel, DNP, RN (North Memorial Medical Center); Darinda Sutton, MSN, RN-BC, FACHE (Cerner Corporation); and Joe Zillmer (Epic Systems). Clinical Resources Agency for Healthcare Research and Quality. Preventing falls in hospitals. https://www.ahrq.gov/professionals/systems/hospital/fallpxtoolkit/index.html Patient Safety Movement. Actionable Patient Safety Solution (APSS) #14: Falls and fall prevention. https://patientsafetymovement.org/wp-content/uploads/2017/10/APSS-14_-Falls-and-Fall-Prevention-081518-1.pdf Agency for Healthcare Research and Quality. Preventing falls in hospitals. https://www.ahrq.gov/professionals/systems/hospital/fallpxtoolkit/index.html Patient Safety Movement. Actionable Patient Safety Solution (APSS) #14: Falls and fall prevention. https://patientsafetymovement.org/wp-content/uploads/2017/10/APSS-14_-Falls-and-Fall-Prevention-081518-1.pdf Publisher Copyright: © 2021 Sigma Theta Tau International

PY - 2021/5

Y1 - 2021/5

N2 - Purpose: The rapid implementation of electronic health records (EHRs) resulted in a lack of data standardization and created considerable difficulty for secondary use of EHR documentation data within and between organizations. While EHRs contain documentation data (input), nurses and healthcare organizations rarely have useable documentation data (output). The purpose of this article is to describe a method of standardizing EHR flowsheet documentation data using information models (IMs) to support exchange, quality improvement, and big data research. As an exemplar, EHR flowsheet metadata (input) from multiple organizations was used to validate a fall prevention IM. Design: A consensus-based, qualitative, descriptive approach was used to identify a minimum set of essential fall prevention data concepts documented by staff nurses in acute care. The goal was to increase generalizable and comparable nurse-sensitive data on the prevention of falls across organizations for big data research. Methods: The research team conducted a retrospective, observational study using an iterative, consensus-based approach to map, analyze, and evaluate nursing flowsheet metadata contributed by eight health systems. The team used FloMap software to aggregate flowsheet data across organizations for mapping and comparison of data to a reference IM. The FloMap analysis was refined with input from staff nurse subject matter experts, review of published evidence, current documentation standards, Magnet Recognition nursing standards, and informal fall prevention nursing use cases. Findings: Flowsheet metadata analyzed from the EHR systems represented 6.6 million patients, 27 million encounters, and 683 million observations. Compared to the original reference IM, five new IM classes were added, concepts were reduced by 14 (from 57 to 43), and 157 value set items were added. The final fall prevention IM incorporated 11 condition or age-specific fall risk screening tools and a fall event details class with 14 concepts. Conclusion: The iterative, consensus-based refinement and validation of the fall prevention IM from actual EHR fall prevention flowsheet documentation contributes to the ability to semantically exchange and compare fall prevention data across multiple health systems and organizations. This method and approach provides a process for standardizing flowsheet data as coded data for information exchange and use in big data research. Clinical Relevance: Opportunities exist to work with EHR vendors and the Office of the National Coordinator for Health Information Technology to implement standardized IMs within EHRs to expand interoperability of nurse-sensitive data.

AB - Purpose: The rapid implementation of electronic health records (EHRs) resulted in a lack of data standardization and created considerable difficulty for secondary use of EHR documentation data within and between organizations. While EHRs contain documentation data (input), nurses and healthcare organizations rarely have useable documentation data (output). The purpose of this article is to describe a method of standardizing EHR flowsheet documentation data using information models (IMs) to support exchange, quality improvement, and big data research. As an exemplar, EHR flowsheet metadata (input) from multiple organizations was used to validate a fall prevention IM. Design: A consensus-based, qualitative, descriptive approach was used to identify a minimum set of essential fall prevention data concepts documented by staff nurses in acute care. The goal was to increase generalizable and comparable nurse-sensitive data on the prevention of falls across organizations for big data research. Methods: The research team conducted a retrospective, observational study using an iterative, consensus-based approach to map, analyze, and evaluate nursing flowsheet metadata contributed by eight health systems. The team used FloMap software to aggregate flowsheet data across organizations for mapping and comparison of data to a reference IM. The FloMap analysis was refined with input from staff nurse subject matter experts, review of published evidence, current documentation standards, Magnet Recognition nursing standards, and informal fall prevention nursing use cases. Findings: Flowsheet metadata analyzed from the EHR systems represented 6.6 million patients, 27 million encounters, and 683 million observations. Compared to the original reference IM, five new IM classes were added, concepts were reduced by 14 (from 57 to 43), and 157 value set items were added. The final fall prevention IM incorporated 11 condition or age-specific fall risk screening tools and a fall event details class with 14 concepts. Conclusion: The iterative, consensus-based refinement and validation of the fall prevention IM from actual EHR fall prevention flowsheet documentation contributes to the ability to semantically exchange and compare fall prevention data across multiple health systems and organizations. This method and approach provides a process for standardizing flowsheet data as coded data for information exchange and use in big data research. Clinical Relevance: Opportunities exist to work with EHR vendors and the Office of the National Coordinator for Health Information Technology to implement standardized IMs within EHRs to expand interoperability of nurse-sensitive data.

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KW - data exchange

KW - electronic health records

KW - information models

KW - secondary use

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Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar

Abstract

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