Natural language processing in the electronic medical record: Assessing clinician adherence to tobacco treatment guidelines
Natural language processing in the electronic medical record: Assessing clinician adherence to tobacco treatment guidelines
复制标题
DOI：
10.1016/j.amepre.2005.08.007
复制
发表时间：
2005-12-01
影响因子：
5.5
通讯作者：
Rigotti, NA
中科院分区：
文献类型：
--
作者：
Hazlehurst, B;Sittig, DF;Rigotti, NA
背景资料：全面评估护理质量与电子病历（EMR）目前是不可能的，因为许多数据驻留在临床医生的自由文本notes.Methods：我们评估的准确性MediClass，一个自动化的，基于规则的分类器的电子病历，结合自然语言处理，在评估是否临床医生：（1）询问病人吸烟;（2）建议他们停止;（3）评估他们戒烟的准备程度;（4）通过提供信息或药物帮助他们戒烟;以及（5）安排适当的随访护理（即，设计：我们分析了2003年和2004年四家卫生组织的125份已知吸烟者的医疗记录。每个HMO的一名受过训练的抽象者根据是否在常规门诊访视期间解决了戒烟护理的5A中的每一个来手动编码所有500个记录。测量：对于每个患者的记录，我们比较了由每个人类编码器和MediClass评估的5A中的每一个的存在或不存在。我们使用kappa统计量测量了人类评分员和MediClass之间的机会校正一致性。结果：对于“询问”和“协助”，人类编码员之间的一致性与人类和MediClass之间的一致性没有区别（p > 0.05）。对于“评估”和“建议”，人类编码员彼此之间的一致性比他们与MediClass的一致性更高（p > 0.01）;然而，MediClass的表现足以评估这些领域的质量。的频率“安排”是太低，以analysed.Conclusions：MediClass性能似乎足以取代人类编码器的5A的戒烟护理，允许自动评估临床医生遵守一个最重要的，以证据为基础的准则，在预防性保健。
Background: Comprehensively assessing care quality with electronic medical records (EMRs) is not currently possible because much data reside in clinicians' free-text notes.Methods: We evaluated the accuracy of MediClass, an automated, rule-based classifier of the EMR that incorporates natural language processing, in assessing whether clinicians: (1) asked if the patient smoked; (2) advised them to stop; (3) assessed their readiness to quit; (4) assisted them in quitting by providing information or medications; and (5) arranged for appropriate follow-up care (i.e., the 5A's of smoking-cessation care).Design: We analyzed 125 medical records of known smokers at each of four HMOs in 2003 and 2004. One trained abstractor at each HMO manually coded all 500 records according to whether or not each of the 5A's of smoking cessation care was addressed during routine outpatient visits.Measurements: For each patient's record, we compared the presence or absence of each of the 5A's as assessed by each human coder and by MediClass. We measured the chance-corrected agreement between the human raters and MediClass using the kappa statistic.Results: For "ask" and "assist," agreement among human coders was indistinguishable from agreement between humans and MediClass (p > 0.05). For "assess" and "advise," the human coders agreed more with each other than they did with MediClass (p > 0.01); however, MediClass performance was sufficient to assess quality in these areas. The frequency of "arrange" was too low to be analyzed.Conclusions: MediClass performance appears adequate to replace human coders of the 5A's of smoking-cessation care, allowing for automated assessment of clinician adherence to one of the most important, evidence-based guidelines in preventive health care.