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- Goossen, William T. F., et al.
(författare)
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Development of a provisional domain model for the nursing process for use within the Health Level 7 reference information model
- 2004
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - Philadelphia, USA : Hanley & Belfus Inc.. - 1067-5027 .- 1527-974X. ; 11:3, s. 186-94
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Tidskriftsartikel (refereegranskat)abstract
- Objective: Since 1999, the Nursing Terminology Summits have promoted the development, evaluation, and use of reference terminology for nursing and its integration into comprehensive health care data standards. The use of such standards to represent nursing knowledge, terminology, processes, and information in electronic health records will enhance continuity of care, decision support, and the exchange of comparable patient information. As part of this activity, working groups at the 2001, 2002, and 2003 Summit Conferences examined how to represent nursing information in the Health Level 7 (HL7) Reference Information Model (RIM).Design: The working groups represented the nursing process as a dynamic sequence of phases, each containing information specific to the activities of the phase. They used Universal Modeling Language (UML) to represent this domain knowledge in models. An Activity Diagram was used to create a dynamic model of the nursing process. After creating a structural model of the information used at each stage of the nursing process, the working groups mapped that information to the HL7 RIM. They used a hierarchical structure for the organization of nursing knowledge as the basis for a hierarchical model for "Findings about the patient." The modeling and mapping reported here were exploratory and preliminary, not exhaustive or definitive. The intent was to evaluate the feasibility of representing some types of nursing information consistently with HL7 standards.Measurements: The working groups conducted a small-scale validation by testing examples of nursing terminology against the HL7 RIM class "Observation."Results: It was feasible to map patient information from the proposed models to the RIM class "Observation." Examples illustrate the models and the mapping of nursing terminology to the HL7 RIM.Conclusion: It is possible to model and map nursing information into the comprehensive health care information model, the HL7 RIM. These models must evolve and undergo further validation by clinicians. The integration of nursing information, terminology, and processes in information models is a first step toward rendering nursing information machine-readable in electronic patient records and messages. An eventual practical result, after much more development, would be to create computable, structured information for nursing documentation.
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| 2. |
- Ludvigsson, Jonas F., et al.
(författare)
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Use of computerized algorithm to identify individuals in need of testing for celiac disease
- 2013
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Ingår i: JAMIA Journal of the American Medical Informatics Association. - : Oxford University Press (OUP). - 1067-5027 .- 1527-974X. ; 20:E2, s. E306-E310
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Tidskriftsartikel (refereegranskat)abstract
- Background and aim Celiac disease (CD) is a lifelong immune-mediated disease with excess mortality. Early diagnosis is important to minimize disease symptoms, complications, and consumption of healthcare resources. Most patients remain undiagnosed. We developed two electronic medical record (EMR)-based algorithms to identify patients at high risk of CD and in need of CD screening. Methods (I) Using natural language processing (NLP), we searched EMRs for 16 free text (and related) terms in 216 CD patients and 280 controls. (II) EMRs were also searched for ICD9 (International Classification of Disease) codes suggesting an increased risk of CD in 202 patients with CD and 524 controls. For each approach, we determined the optimal number of hits to be assigned as CD cases. To assess performance of these algorithms, sensitivity and specificity were calculated. Results Using two hits as the cut-off, the NLP algorithm identified 72.9% of all celiac patients (sensitivity), and ruled out CD in 89.9% of the controls (specificity). In a representative US population of individuals without a prior celiac diagnosis (assuming that 0.6% had undiagnosed CD), this NLP algorithm could identify a group of individuals where 4.2% would have CD (positive predictive value). ICD9 code search using three hits as the cut-off had a sensitivity of 17.1% and a specificity of 88.5% (positive predictive value was 0.9%). Discussion and conclusions This study shows that computerized EMR-based algorithms can help identify patients at high risk of CD. NLP-based techniques demonstrate higher sensitivity and positive predictive values than algorithms based on ICD9 code searches.
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