Validation and Refinement of a Pain Information Model from EHR Flowsheet Data

Bonnie L. Westra, Steve Johnson, Samira Ali, Karen M. Bavuso, Christopher A. Cruz, Sarah Collins, Meg Furukawa, Mary L. Hook, Anne LaFlamme, Kay Lytle, Lisiane Pruinelli, Tari Rajchel, Theresa Tess Settergren, Kathryn F. Westman, Luann Whittenburg

Research output: Contribution to journalArticle

3 Citations (Scopus)

Abstract

Background Secondary use of electronic health record (EHR) data can reduce costs of research and quality reporting. However, EHR data must be consistent within and across organizations. Flowsheet data provide a rich source of interprofessional data and represents a high volume of documentation; however, content is not standardized. Health care organizations design and implement customized content for different care areas creating duplicative data that is noncomparable. In a prior study, 10 information models (IMs) were derived from an EHR that included 2.4 million patients. There was a need to evaluate the generalizability of the models across organizations. The pain IM was selected for evaluation and refinement because pain is a commonly occurring problem associated with high costs for pain management. Objective The purpose of our study was to validate and further refine a pain IM from EHR flowsheet data that standardizes pain concepts, definitions, and associated value sets for assessments, goals, interventions, and outcomes. Methods A retrospective observational study was conducted using an iterative consensus-based approach to map, analyze, and evaluate data from 10 organizations. Results The aggregated metadata from the EHRs of 8 large health care organizations and the design build in 2 additional organizations represented flowsheet data from 6.6 million patients, 27 million encounters, and 683 million observations. The final pain IM has 30 concepts, 4 panels (classes), and 396 value set items. Results are built on Logical Observation Identifiers Names and Codes (LOINC) pain assessment terms and extend the need for additional terms to support interoperability. Conclusion The resulting pain IM is a consensus model based on actual EHR documentation in the participating health systems. The IM captures the most important concepts related to pain.

Original languageEnglish (US)
Pages (from-to)185-198
Number of pages14
JournalApplied clinical informatics
Volume9
Issue number1
DOIs
StatePublished - Jan 1 2018

Fingerprint

Flowcharting
Electronic Health Records
Health
Pain
Organizations
Documentation
Logical Observation Identifiers Names and Codes
Health care
Consensus
Delivery of Health Care
Costs and Cost Analysis
Information Storage and Retrieval
Pain Measurement
Pain Management
Observational Studies
Metadata
Interoperability
Retrospective Studies
Costs
Research

Keywords

  • efficiency improvement
  • electronic health records and systems
  • knowledge modeling and representation
  • nursing informatics
  • pain management
  • secondary use

PubMed: MeSH publication types

  • Journal Article
  • Observational Study
  • Validation Studies

Cite this

Westra, B. L., Johnson, S., Ali, S., Bavuso, K. M., Cruz, C. A., Collins, S., ... Whittenburg, L. (2018). Validation and Refinement of a Pain Information Model from EHR Flowsheet Data. Applied clinical informatics, 9(1), 185-198. https://doi.org/10.1055/s-0038-1636508

Validation and Refinement of a Pain Information Model from EHR Flowsheet Data. / Westra, Bonnie L.; Johnson, Steve; Ali, Samira; Bavuso, Karen M.; Cruz, Christopher A.; Collins, Sarah; Furukawa, Meg; Hook, Mary L.; LaFlamme, Anne; Lytle, Kay; Pruinelli, Lisiane; Rajchel, Tari; Settergren, Theresa Tess; Westman, Kathryn F.; Whittenburg, Luann.

In: Applied clinical informatics, Vol. 9, No. 1, 01.01.2018, p. 185-198.

Research output: Contribution to journalArticle

Westra, BL, Johnson, S, Ali, S, Bavuso, KM, Cruz, CA, Collins, S, Furukawa, M, Hook, ML, LaFlamme, A, Lytle, K, Pruinelli, L, Rajchel, T, Settergren, TT, Westman, KF & Whittenburg, L 2018, 'Validation and Refinement of a Pain Information Model from EHR Flowsheet Data', Applied clinical informatics, vol. 9, no. 1, pp. 185-198. https://doi.org/10.1055/s-0038-1636508
Westra, Bonnie L. ; Johnson, Steve ; Ali, Samira ; Bavuso, Karen M. ; Cruz, Christopher A. ; Collins, Sarah ; Furukawa, Meg ; Hook, Mary L. ; LaFlamme, Anne ; Lytle, Kay ; Pruinelli, Lisiane ; Rajchel, Tari ; Settergren, Theresa Tess ; Westman, Kathryn F. ; Whittenburg, Luann. / Validation and Refinement of a Pain Information Model from EHR Flowsheet Data. In: Applied clinical informatics. 2018 ; Vol. 9, No. 1. pp. 185-198.
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abstract = "Background Secondary use of electronic health record (EHR) data can reduce costs of research and quality reporting. However, EHR data must be consistent within and across organizations. Flowsheet data provide a rich source of interprofessional data and represents a high volume of documentation; however, content is not standardized. Health care organizations design and implement customized content for different care areas creating duplicative data that is noncomparable. In a prior study, 10 information models (IMs) were derived from an EHR that included 2.4 million patients. There was a need to evaluate the generalizability of the models across organizations. The pain IM was selected for evaluation and refinement because pain is a commonly occurring problem associated with high costs for pain management. Objective The purpose of our study was to validate and further refine a pain IM from EHR flowsheet data that standardizes pain concepts, definitions, and associated value sets for assessments, goals, interventions, and outcomes. Methods A retrospective observational study was conducted using an iterative consensus-based approach to map, analyze, and evaluate data from 10 organizations. Results The aggregated metadata from the EHRs of 8 large health care organizations and the design build in 2 additional organizations represented flowsheet data from 6.6 million patients, 27 million encounters, and 683 million observations. The final pain IM has 30 concepts, 4 panels (classes), and 396 value set items. Results are built on Logical Observation Identifiers Names and Codes (LOINC) pain assessment terms and extend the need for additional terms to support interoperability. Conclusion The resulting pain IM is a consensus model based on actual EHR documentation in the participating health systems. The IM captures the most important concepts related to pain.",
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AU - Ali, Samira

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AU - Collins, Sarah

AU - Furukawa, Meg

AU - Hook, Mary L.

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AU - Rajchel, Tari

AU - Settergren, Theresa Tess

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N2 - Background Secondary use of electronic health record (EHR) data can reduce costs of research and quality reporting. However, EHR data must be consistent within and across organizations. Flowsheet data provide a rich source of interprofessional data and represents a high volume of documentation; however, content is not standardized. Health care organizations design and implement customized content for different care areas creating duplicative data that is noncomparable. In a prior study, 10 information models (IMs) were derived from an EHR that included 2.4 million patients. There was a need to evaluate the generalizability of the models across organizations. The pain IM was selected for evaluation and refinement because pain is a commonly occurring problem associated with high costs for pain management. Objective The purpose of our study was to validate and further refine a pain IM from EHR flowsheet data that standardizes pain concepts, definitions, and associated value sets for assessments, goals, interventions, and outcomes. Methods A retrospective observational study was conducted using an iterative consensus-based approach to map, analyze, and evaluate data from 10 organizations. Results The aggregated metadata from the EHRs of 8 large health care organizations and the design build in 2 additional organizations represented flowsheet data from 6.6 million patients, 27 million encounters, and 683 million observations. The final pain IM has 30 concepts, 4 panels (classes), and 396 value set items. Results are built on Logical Observation Identifiers Names and Codes (LOINC) pain assessment terms and extend the need for additional terms to support interoperability. Conclusion The resulting pain IM is a consensus model based on actual EHR documentation in the participating health systems. The IM captures the most important concepts related to pain.

AB - Background Secondary use of electronic health record (EHR) data can reduce costs of research and quality reporting. However, EHR data must be consistent within and across organizations. Flowsheet data provide a rich source of interprofessional data and represents a high volume of documentation; however, content is not standardized. Health care organizations design and implement customized content for different care areas creating duplicative data that is noncomparable. In a prior study, 10 information models (IMs) were derived from an EHR that included 2.4 million patients. There was a need to evaluate the generalizability of the models across organizations. The pain IM was selected for evaluation and refinement because pain is a commonly occurring problem associated with high costs for pain management. Objective The purpose of our study was to validate and further refine a pain IM from EHR flowsheet data that standardizes pain concepts, definitions, and associated value sets for assessments, goals, interventions, and outcomes. Methods A retrospective observational study was conducted using an iterative consensus-based approach to map, analyze, and evaluate data from 10 organizations. Results The aggregated metadata from the EHRs of 8 large health care organizations and the design build in 2 additional organizations represented flowsheet data from 6.6 million patients, 27 million encounters, and 683 million observations. The final pain IM has 30 concepts, 4 panels (classes), and 396 value set items. Results are built on Logical Observation Identifiers Names and Codes (LOINC) pain assessment terms and extend the need for additional terms to support interoperability. Conclusion The resulting pain IM is a consensus model based on actual EHR documentation in the participating health systems. The IM captures the most important concepts related to pain.

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