Patel Jay, Mowery Danielle, Krishnan Anand, Thyvalikakath Thankam
Indiana University School of Dentistry, Indianapolis, IN.
Department of Bio-Health Informatics, IUPUI School of Informatics and Computing, Indianapolis, IN.
AMIA Annu Symp Proc. 2018 Dec 5;2018:1442-1450. eCollection 2018.
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.
牙医在其诊所中越来越频繁地治疗患有心血管疾病(CVD)的患者;因此,牙医可能需要在患者患有心血管疾病的情况下调整治疗计划。然而,目前尚不清楚电子牙科记录(EDR)中患者报告的心血管疾病信息被准确记录的程度。在这项试点研究中,我们旨在衡量电子牙科记录中患者报告的心血管疾病状况的可靠性。我们通过将患者自我报告的牙科病史与其在电子病历(EMR)中由医疗服务提供者指定的原始诊断进行比较,来评估信息的一致性。为了进行这种比较,我们使用自然语言处理(NLP)将电子牙科记录的自由文本数据中的患者心血管疾病信息编码为结构化格式。总体而言,我们的自然语言处理方法在提取患者的心血管疾病相关信息方面取得了令人满意的表现。我们观察到电子牙科记录的自我报告数据与医生诊断的电子病历数据之间存在不一致。