Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records
dc.contributor.author | Patel, Jay | |
dc.contributor.author | Mowery, Danielle | |
dc.contributor.author | Krishnan, Anand | |
dc.contributor.author | Thyvalikakath, Thankam | |
dc.date.accessioned | 2018-11-26T15:06:54Z | |
dc.date.available | 2018-11-26T15:06:54Z | |
dc.date.issued | 2018 | |
dc.description.abstract | Dentists are more often treating patients with Cardiovascular Diseases (CVD) in their clinics; therefore, dentists may need to alter treatment plans in the presence of CVD. However, it’s unclear to what extent patient-reported CVD information is accurately captured in Electronic Dental Records (EDRs). In this pilot study, we aimed to measure the reliability of patient-reported CVD conditions in EDRs. We assessed information congruence by comparing patients’ self-reported dental histories to their original diagnosis assigned by their medical providers in the Electronic Medical Record (EMR). To enable this comparison, we encoded patients CVD information from the free-text data of EDRs into a structured format using natural language processing (NLP). Overall, our NLP approach achieved promising performance extracting patients’ CVD-related information. We observed disagreement between self-reported EDR data and physician-diagnosed EMR data. | en_US |
dc.identifier.uri | https://hdl.handle.net/1805/17825 | |
dc.language.iso | en_US | en_US |
dc.subject | Cardiovascular Disease | en_US |
dc.subject | Electronic Medical Records | en_US |
dc.subject | Electronic Dental Records | en_US |
dc.subject | Natural Language Processing | en_US |
dc.subject.mesh | Cardiovascular Diseases | |
dc.subject.mesh | Electronic Health Records | |
dc.subject.mesh | Natural Language Processing | |
dc.title | Assessing Information Congruence of Documented Cardiovascular Disease between Electronic Dental and Medical Records | en_US |
dc.type | Article | en_US |
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