Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar
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 organ...
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| Veröffentlicht in: | Journal of nursing scholarship 2021-05, Vol.53 (3), p.306-314 |
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| creator | Lytle, Kay S. Westra, Bonnie L. Whittenburg, Luann Adams, Mischa Akre, Mari Ali, Samira Furukawa, Meg Hartleben, Stephanie Hook, Mary Johnson, Steven G. Settergren, Theresa (Tess) Thibodeaux, Mariaelena |
| description | 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 w |
| doi_str_mv | 10.1111/jnu.12646 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2501483719</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2531372963</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3536-596318f3d47aceb69aed223914d37ffb4231c27c2095a99392ec5e26ae693c23</originalsourceid><addsrcrecordid>eNp1kclOwzAQhi0EYj_wAsgSFzgEvMQO5laVloLYxHaNXGciUjlxaydsT4-hhQMSc5k5fPr0a36Edig5pHGOJk13SJlM5RJapyLNEkGkWv66WZZIkco1tBHChBAiacZX0RrnGSOCputodt6Uzte6rVyDr1wBNuCbsgSPn7TtALcO37e6KbQvqg_AAwum9a6pDB6Btu0zvgPjfIGH1r2GZ4AWn-pWn-AeHmpr8a2HF2i-5YM3qKdW-y20UmobYHuxN9HDcPDQHyWXN2fn_d5lYrjgMhFKcnpc8iLNtIGxVBoKxriiacGzshynjFPDMsOIEloprhgYAUxqkIobxjfR_lw79W7WQWjzugoGrNUNuC7kTBCaHvOMqoju_UEnrvNNDBcpTuOvYpZIHcwp410IHsp86qta-_eckvyrhjzWkH_XENndhbEb11D8kj9_j8DRHHitLLz_b8ovrh_nyk_hdZCZ</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2531372963</pqid></control><display><type>article</type><title>Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar</title><source>Applied Social Sciences Index & Abstracts (ASSIA)</source><source>Wiley Online Library All Journals</source><creator>Lytle, Kay S. ; Westra, Bonnie L. ; Whittenburg, Luann ; Adams, Mischa ; Akre, Mari ; Ali, Samira ; Furukawa, Meg ; Hartleben, Stephanie ; Hook, Mary ; Johnson, Steven G. ; Settergren, Theresa (Tess) ; Thibodeaux, Mariaelena</creator><creatorcontrib>Lytle, Kay S. ; Westra, Bonnie L. ; Whittenburg, Luann ; Adams, Mischa ; Akre, Mari ; Ali, Samira ; Furukawa, Meg ; Hartleben, Stephanie ; Hook, Mary ; Johnson, Steven G. ; Settergren, Theresa (Tess) ; Thibodeaux, Mariaelena</creatorcontrib><description>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.</description><identifier>ISSN: 1527-6546</identifier><identifier>EISSN: 1547-5069</identifier><identifier>DOI: 10.1111/jnu.12646</identifier><identifier>PMID: 33720514</identifier><language>eng</language><publisher>United States: Blackwell Publishing Ltd</publisher><subject>Acute services ; Age differences ; Big Data ; Clinical decision making ; Clinical research ; Clinical standards ; Computerized medical records ; data exchange ; Decision making ; Electronic health records ; Falls ; Health care industry ; Health information ; Health records ; information models ; Information sharing ; Information technology ; Injury prevention ; Mapping ; Medical screening ; Medical technology ; Nurses ; Nursing ; Nursing care ; Prevention ; Quality management ; secondary use ; Staff nurses ; Standardization ; Teams ; Validity ; Vendors</subject><ispartof>Journal of nursing scholarship, 2021-05, Vol.53 (3), p.306-314</ispartof><rights>2021 Sigma Theta Tau International</rights><rights>2021 Sigma Theta Tau International.</rights><rights>Copyright Blackwell Publishing Ltd. May 2021</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3536-596318f3d47aceb69aed223914d37ffb4231c27c2095a99392ec5e26ae693c23</citedby><cites>FETCH-LOGICAL-c3536-596318f3d47aceb69aed223914d37ffb4231c27c2095a99392ec5e26ae693c23</cites><orcidid>0000-0001-9845-1501</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fjnu.12646$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fjnu.12646$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>314,780,784,1416,12845,27923,27924,30998,45573,45574</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/33720514$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Lytle, Kay S.</creatorcontrib><creatorcontrib>Westra, Bonnie L.</creatorcontrib><creatorcontrib>Whittenburg, Luann</creatorcontrib><creatorcontrib>Adams, Mischa</creatorcontrib><creatorcontrib>Akre, Mari</creatorcontrib><creatorcontrib>Ali, Samira</creatorcontrib><creatorcontrib>Furukawa, Meg</creatorcontrib><creatorcontrib>Hartleben, Stephanie</creatorcontrib><creatorcontrib>Hook, Mary</creatorcontrib><creatorcontrib>Johnson, Steven G.</creatorcontrib><creatorcontrib>Settergren, Theresa (Tess)</creatorcontrib><creatorcontrib>Thibodeaux, Mariaelena</creatorcontrib><title>Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar</title><title>Journal of nursing scholarship</title><addtitle>J Nurs Scholarsh</addtitle><description>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.</description><subject>Acute services</subject><subject>Age differences</subject><subject>Big Data</subject><subject>Clinical decision making</subject><subject>Clinical research</subject><subject>Clinical standards</subject><subject>Computerized medical records</subject><subject>data exchange</subject><subject>Decision making</subject><subject>Electronic health records</subject><subject>Falls</subject><subject>Health care industry</subject><subject>Health information</subject><subject>Health records</subject><subject>information models</subject><subject>Information sharing</subject><subject>Information technology</subject><subject>Injury prevention</subject><subject>Mapping</subject><subject>Medical screening</subject><subject>Medical technology</subject><subject>Nurses</subject><subject>Nursing</subject><subject>Nursing care</subject><subject>Prevention</subject><subject>Quality management</subject><subject>secondary use</subject><subject>Staff nurses</subject><subject>Standardization</subject><subject>Teams</subject><subject>Validity</subject><subject>Vendors</subject><issn>1527-6546</issn><issn>1547-5069</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2021</creationdate><recordtype>article</recordtype><sourceid>7QJ</sourceid><sourceid>8G5</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><sourceid>GUQSH</sourceid><sourceid>M2O</sourceid><recordid>eNp1kclOwzAQhi0EYj_wAsgSFzgEvMQO5laVloLYxHaNXGciUjlxaydsT4-hhQMSc5k5fPr0a36Edig5pHGOJk13SJlM5RJapyLNEkGkWv66WZZIkco1tBHChBAiacZX0RrnGSOCputodt6Uzte6rVyDr1wBNuCbsgSPn7TtALcO37e6KbQvqg_AAwum9a6pDB6Btu0zvgPjfIGH1r2GZ4AWn-pWn-AeHmpr8a2HF2i-5YM3qKdW-y20UmobYHuxN9HDcPDQHyWXN2fn_d5lYrjgMhFKcnpc8iLNtIGxVBoKxriiacGzshynjFPDMsOIEloprhgYAUxqkIobxjfR_lw79W7WQWjzugoGrNUNuC7kTBCaHvOMqoju_UEnrvNNDBcpTuOvYpZIHcwp410IHsp86qta-_eckvyrhjzWkH_XENndhbEb11D8kj9_j8DRHHitLLz_b8ovrh_nyk_hdZCZ</recordid><startdate>202105</startdate><enddate>202105</enddate><creator>Lytle, Kay S.</creator><creator>Westra, Bonnie L.</creator><creator>Whittenburg, Luann</creator><creator>Adams, Mischa</creator><creator>Akre, Mari</creator><creator>Ali, Samira</creator><creator>Furukawa, Meg</creator><creator>Hartleben, Stephanie</creator><creator>Hook, Mary</creator><creator>Johnson, Steven G.</creator><creator>Settergren, Theresa (Tess)</creator><creator>Thibodeaux, Mariaelena</creator><general>Blackwell Publishing 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Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar</title><author>Lytle, Kay S. ; Westra, Bonnie L. ; Whittenburg, Luann ; Adams, Mischa ; Akre, Mari ; Ali, Samira ; Furukawa, Meg ; Hartleben, Stephanie ; Hook, Mary ; Johnson, Steven G. ; Settergren, Theresa (Tess) ; Thibodeaux, Mariaelena</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3536-596318f3d47aceb69aed223914d37ffb4231c27c2095a99392ec5e26ae693c23</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2021</creationdate><topic>Acute services</topic><topic>Age differences</topic><topic>Big Data</topic><topic>Clinical decision making</topic><topic>Clinical research</topic><topic>Clinical standards</topic><topic>Computerized medical records</topic><topic>data exchange</topic><topic>Decision making</topic><topic>Electronic health records</topic><topic>Falls</topic><topic>Health care industry</topic><topic>Health information</topic><topic>Health records</topic><topic>information models</topic><topic>Information sharing</topic><topic>Information technology</topic><topic>Injury prevention</topic><topic>Mapping</topic><topic>Medical screening</topic><topic>Medical technology</topic><topic>Nurses</topic><topic>Nursing</topic><topic>Nursing care</topic><topic>Prevention</topic><topic>Quality management</topic><topic>secondary use</topic><topic>Staff nurses</topic><topic>Standardization</topic><topic>Teams</topic><topic>Validity</topic><topic>Vendors</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lytle, Kay S.</creatorcontrib><creatorcontrib>Westra, Bonnie L.</creatorcontrib><creatorcontrib>Whittenburg, Luann</creatorcontrib><creatorcontrib>Adams, Mischa</creatorcontrib><creatorcontrib>Akre, Mari</creatorcontrib><creatorcontrib>Ali, Samira</creatorcontrib><creatorcontrib>Furukawa, Meg</creatorcontrib><creatorcontrib>Hartleben, Stephanie</creatorcontrib><creatorcontrib>Hook, Mary</creatorcontrib><creatorcontrib>Johnson, Steven G.</creatorcontrib><creatorcontrib>Settergren, Theresa (Tess)</creatorcontrib><creatorcontrib>Thibodeaux, Mariaelena</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Social Sciences Premium Collection</collection><collection>ProQuest Central (Corporate)</collection><collection>University Readers</collection><collection>Applied Social Sciences Index & Abstracts (ASSIA)</collection><collection>Nursing & Allied Health Database</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Medical Database (Alumni Edition)</collection><collection>Psychology Database (Alumni)</collection><collection>ProQuest Pharma Collection</collection><collection>Public Health Database</collection><collection>Hospital Premium 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Academic</collection><jtitle>Journal of nursing scholarship</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lytle, Kay S.</au><au>Westra, Bonnie L.</au><au>Whittenburg, Luann</au><au>Adams, Mischa</au><au>Akre, Mari</au><au>Ali, Samira</au><au>Furukawa, Meg</au><au>Hartleben, Stephanie</au><au>Hook, Mary</au><au>Johnson, Steven G.</au><au>Settergren, Theresa (Tess)</au><au>Thibodeaux, Mariaelena</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar</atitle><jtitle>Journal of nursing scholarship</jtitle><addtitle>J Nurs Scholarsh</addtitle><date>2021-05</date><risdate>2021</risdate><volume>53</volume><issue>3</issue><spage>306</spage><epage>314</epage><pages>306-314</pages><issn>1527-6546</issn><eissn>1547-5069</eissn><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.</abstract><cop>United States</cop><pub>Blackwell Publishing Ltd</pub><pmid>33720514</pmid><doi>10.1111/jnu.12646</doi><tpages>9</tpages><orcidid>https://orcid.org/0000-0001-9845-1501</orcidid></addata></record> |
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| identifier | ISSN: 1527-6546 |
| ispartof | Journal of nursing scholarship, 2021-05, Vol.53 (3), p.306-314 |
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| source | Applied Social Sciences Index & Abstracts (ASSIA); Wiley Online Library All Journals |
| subjects | Acute services Age differences Big Data Clinical decision making Clinical research Clinical standards Computerized medical records data exchange Decision making Electronic health records Falls Health care industry Health information Health records information models Information sharing Information technology Injury prevention Mapping Medical screening Medical technology Nurses Nursing Nursing care Prevention Quality management secondary use Staff nurses Standardization Teams Validity Vendors |
| title | Information Models Offer Value to Standardize Electronic Health Record Flowsheet Data: A Fall Prevention Exemplar |
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