Med Decis Making - Natural language processing improves identification of colorectal cancer testing in the electronic medical record.

Tópicos

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Resumo

CKGROUND: Difficulty identifying patients in need of colorectal cancer (CRC) screening contributes to low screening rates.OBJECTIVE: To use Electronic Health Record (EHR) data to identify patients with prior CRC testing.DESIGN: A clinical natural language processing (NLP) system was modified to identify 4 CRC tests (colonoscopy, flexible sigmoidoscopy, fecal occult blood testing, and double contrast barium enema) within electronic clinical documentation. Text phrases in clinical notes referencing CRC tests were interpreted by the system to determine whether testing was planned or completed and to estimate the date of completed tests.SETTING: Large academic medical center.PATIENTS: 200 patients = 50 years old who had completed = 2 non-acute primary care visits within a 1-year period.MEASURES: Recall and precision of the NLP system, billing records, and human chart review were compared to a reference standard of human review of all available information sources.RESULTS: For identification of all CRC tests, recall and precision were as follows: NLP system (recall 93%, precision 94%), chart review (74%, 98%), and billing records review (44%, 83%). Recall and precision for identification of patients in need of screening were: NLP system (recall 95%, precision 88%), chart review (99%, 82%), and billing records (99%, 67%).LIMITATIONS: Small sample size and requirement for a robust EHR.CONCLUSIONS: Applying NLP to EHR records detected more CRC tests than either manual chart review or billing records review alone. NLP had better precision but marginally lower recall to identify patients who were due for CRC screening than billing record review.

Resumo Limpo

ckground difficulti identifi patient need colorect cancer crc screen contribut low screen ratesobject use electron health record ehr data identifi patient prior crc testingdesign clinic natur languag process nlp system modifi identifi crc test colonoscopi flexibl sigmoidoscopi fecal occult blood test doubl contrast barium enema within electron clinic document text phrase clinic note referenc crc test interpret system determin whether test plan complet estim date complet testsset larg academ medic centerpati patient year old complet nonacut primari care visit within year periodmeasur recal precis nlp system bill record human chart review compar refer standard human review avail inform sourcesresult identif crc test recal precis follow nlp system recal precis chart review bill record review recal precis identif patient need screen nlp system recal precis chart review bill record limit small sampl size requir robust ehrconclus appli nlp ehr record detect crc test either manual chart review bill record review alon nlp better precis margin lower recal identifi patient due crc screen bill record review

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