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| − | Care delivery organizations aspire to increase the content of coded data in their [[EMR|Clinical Information Systems]] to enhance processes such as: coding for billing purposes; abstracting clinical data for Performance Improvement Efforts; and use in [[CDS|Clinical Decision Support]]. Unfortunately, care providers find coded data entry cumbersome because it interferes with individualized patient care and workflow. In addition, when compared to traditional natural language narrative, coded entry captures only a fraction of the information produced in the clinical encounter and hence there is a trade-off between sensitivity and specificity in physician coding.
| + | #REDIRECT [[Structured data entry]] |
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| − | == Introduction ==
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| − | There now exist systems which are capable of coding clinical notes. For example, MediClass (a "medical classifier") is a knowledge-based system that automatically classifies the content of clinical notes within an EHR. MediClass is able to code notes by applying a set of application-specific logical rules to the medical concepts that are automatically identified in both the free-text notes and precoded data elements such as medication orders. This system can process data from any EMR system as long as data can be expressed in the [[Clinical Document Architecture (CDA)]] data standard that is maintained by Health Level Seven.
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| − | The MediClass system was built from open source components and utilizes 3 technologies:
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| − | #[[HL7|Hl7's]] CDA for representing the clinical encounter including both structured and unstructured data elements. | + | |
| − | #[[Natural Language Processing (NLP)|Natural language processing]] techniques for parsing and assigning structured semantic representations to text segments within the CDA.
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| − | # Knowledge-based systems for processing semantic representations addressing specific subdomains of medicine and clinical care and for defining logical classifications over the semantic contents of a clinical note.
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| − | Studies have shown the application to have be similar in accuracy to trained human medical record abstractors; using the trained abstractors as gold standard, the system performed with an average sensitivity of 82% and an average specificity of 93%.
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| − | ==References==
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| − | # Dolin RH, Aschuler L, Beebe C, Biron PV, Boyer SL, Essin D, et al. The HL7 Clinical Document Architecture. J Am Med Inform Assoc. 2001;8:552–69
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| − | # Gilbert J. Physician data entry: providing options is essential. Health Data. 1998;6:84–92.
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| − | # Hazlehurst B, Frost HR, Sittig DF, Stevens VJ. MediClass: A system for detecting and classifying encounter-based clinical events in any electronic medical record. J Am Med Inform Assoc. 2005 Sep-Oct;12(5):517-29.
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| − | # Kaplan B. Reducing barriers to physician data entry for computer-based patient records. Top Heal Inf Manag. 1994;15:24–34.
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| − | # McDonald C. The barriers to electronic medical record systems and how to overcome them. J Am Med Inform Assoc. 1997;4:213–21.
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| − | # Schneider EC, Riehl V, Courte-Wienecke S, Eddy DM, Sennett C. Enhancing performance measurement: NCQA's road map for a health information framework. JAMA. 1999;282:1184–90.
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| − | [[Category:OHSU-F-06]]
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