Observational studyApplied clinical informatics2018
Validation and Refinement of a Pain Information Model from EHR Flowsheet Data.
Observational study in Applied clinical informatics, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 2 of them syntheses that pooled it.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Psychometric Properties of Pain Scales in Inpatient Settings: An Umbrella Review.Journal of clinical nursing · 2026Pooled it
- The Impact of Patient Interactive Systems on the Management of Pain in an Inpatient Hospital Setting: A Systematic Review.Applied clinical informatics · 2019Pooled it
- Semi-automated pipeline to accelerate multi-site flowsheet alignment and concept mapping in electronic health records.Journal of the American Medical Informatics Association : JAMIA · 2025Article
- Information Model on pain management for elder adults aged 75 years or older.Revista latino-americana de enfermagem · 2024Observational
- EHR Documentation Frequency Changes Across the COVID-19 Pandemic.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2024Article
- Clinician Acceptance of Order Sets for Pain Management: A Survey in Two Urban Hospitals.Applied clinical informatics · 2022Article
- Learning health systems, embedded research, and data standards-recommendations for healthcare system leaders.JAMIA open · 2020Review
- A refined methodology for validation of information models derived from flowsheet data and applied to a genitourinary case.Journal of the American Medical Informatics Association : JAMIA · 2020Article
- Machine Learned Mapping of Local EHR Flowsheet Data to Standard Information Models using Topic Model Filtering.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2019Article
- Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
15 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundSecondary 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.
objectiveThe 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.
methodsA retrospective observational study was conducted using an iterative consensus-based approach to map, analyze, and evaluate data from 10 organizations.
resultsThe 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.
conclusionThe 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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Registered trials
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