ArticleJournal of the American Medical Informatics Association : JAMIA2016
Clinical element models in the SHARPn consortium.
Article in Journal of the American Medical Informatics Association : JAMIA, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Biomedical Big Data Technologies, Applications, and Challenges for Precision Medicine: A Review.Global challenges (Hoboken, NJ) · 2024Review
- Deep Phenotyping of Chinese Electronic Health Records by Recognizing Linguistic Patterns of Phenotypic Narratives With a Sequence Motif Discovery Tool: Algorithm Development and Validation.Journal of medical Internet research · 2022Article
- Constructing High-Fidelity Phenotype Knowledge Graphs for Infectious Diseases With a Fine-Grained Semantic Information Model: Development and Usability Study.Journal of medical Internet research · 2021Article
- Implementing Structured Clinical Templates at a Single Tertiary Hospital: Survey Study.JMIR medical informatics · 2020Article
- Use of Computerized Provider Order Entry Events for Postoperative Complication Surveillance.JAMA surgery · 2019Article
- Lessons Learned in Creating Interoperable Fast Healthcare Interoperability Resources Profiles for Large-Scale Public Health Programs.Applied clinical informatics · 2019Article
- iT2DMS: a Standard-Based Diabetic Disease Data Repository and its Pilot Experiment on Diabetic Retinopathy Phenotyping and Examination Results Integration.Journal of medical systems · 2018Article
- An Efficient, Clinically-Natural Electronic Medical Record System that Produces Computable Data.EGEMS (Washington, DC) · 2017Article
- An ontology-aware integration of clinical models, terminologies and guidelines: an exploratory study of the Scale for the Assessment and Rating of Ataxia (SARA).BMC medical informatics and decision making · 2017Article
- The Role of Free/Libre and Open Source Software in Learning Health Systems.Yearbook of medical informatics · 2017Article
- Evolution of an Implementation-Ready Interprofessional Pain Assessment Reference Model.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2017Article
- Prioritization and Refinement of Clinical Data Elements within EHR Systems.AMIA ... Annual Symposium proceedings. AMIA Symposium · 2016Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
objectiveThe objective of the Strategic Health IT Advanced Research Project area four (SHARPn) was to develop open-source tools that could be used for the normalization of electronic health record (EHR) data for secondary use--specifically, for high throughput phenotyping. We describe the role of Intermountain Healthcare's Clinical Element Models ([CEMs] Intermountain Healthcare Health Services, Inc, Salt Lake City, Utah) as normalization "targets" within the project. MATERIALS AND
methodsIntermountain's CEMs were either repurposed or created for the SHARPn project. A CEM describes "valid" structure and semantics for a particular kind of clinical data. CEMs are expressed in a computable syntax that can be compiled into implementation artifacts. The modeling team and SHARPn colleagues agilely gathered requirements and developed and refined models.
resultsTwenty-eight "statement" models (analogous to "classes") and numerous "component" CEMs and their associated terminology were repurposed or developed to satisfy SHARPn high throughput phenotyping requirements. Model (structural) mappings and terminology (semantic) mappings were also created. Source data instances were normalized to CEM-conformant data and stored in CEM instance databases. A model browser and request site were built to facilitate the development. DISCUSSION: The modeling efforts demonstrated the need to address context differences and granularity choices and highlighted the inevitability of iso-semantic models. The need for content expertise and "intelligent" content tooling was also underscored. We discuss scalability and sustainability expectations for a CEM-based approach and describe the place of CEMs relative to other current efforts.
conclusionsThe SHARPn effort demonstrated the normalization and secondary use of EHR data. CEMs proved capable of capturing data originating from a variety of sources within the normalization pipeline and serving as suitable normalization targets.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.