Dasaro Christopher R, Sabra Ahmad, Jeon Yunho, Williams Tankeesha A, Sloan Nancy L, Todd Andrew C, Teitelbaum Susan L
World Trade Center Health Program General Responder Data Center, Department of Environmental Medicine and Climate Science, Icahn School of Medicine at Mount Sinai, 17 East 102 Street, 2 Floor, New York, NY 10029, United States of America.
Prev Med Rep. 2024 May 17;43:102765. doi: 10.1016/j.pmedr.2024.102765. eCollection 2024 Jul.
To identify and support correction of misspelled medication names recorded as free text, we compared the relative effectiveness of two user-friendly methods, used without reliance on clinical knowledge.
Leveraging the SAS® COMPGED function, fuzzy string search programs examined 1.8 million medication records from 183,600 World Trade Center General Responder Cohort monitoring visits conducted in New York and New Jersey between 7/16/2002 and 3/31/2021, producing replicable generalized edit distance scores between the reported and correct spelling. Scores < 120 were selected as optimal and compared to Stedman's 2020 Plus Medical/Pharmaceutical Spell Checker first suggested word, used as the comparative standard because it employs both spelling and phonetic similarities to suggest matching words. We coded each methods' results as identifying or not identifying the medications within each visit.
Most types of medications (94.4 % anxiety, 98.4 % asthma and 94.6 % ulcer/gastroesophageal reflux disease) were correctly spelled. Cross tabulations assessed the agreement (anxiety 99.9 %, asthma 99.6 % and 98.4 % ulcer/ gastroesophageal reflux disease), false positive (respectively 0.02 %, 0.03 % and 2.0 %) and false negative (respectively 1.9 %, 0.5 % and 1.0 %) values. Scores < 120 occasionally correctly identified medications missed by the spell checker. We observed no difference in medication misspellings across socio-economically and culturally diverse patient characteristics.
Both methods efficiently identified most misspelled medications, greatly minimizing the review and rectification needed. The fuzzy method is more universally applicable for condition-specific medications identification, but requires more programming skills. The spell checker is inexpensive, but benefits from modest programming skills and is only available in some languages.
为了识别并支持纠正记录为自由文本的药物名称拼写错误，我们比较了两种无需依赖临床知识即可使用的用户友好方法的相对有效性。
利用SAS® COMPGED函数，模糊字符串搜索程序检查了2002年7月16日至2021年3月31日期间在纽约和新泽西进行的183,600次世界贸易中心一般应答者队列监测访问中的180万条药物记录，得出报告拼写与正确拼写之间可重复的广义编辑距离分数。分数<120被选为最佳分数，并与作为比较标准的《斯泰德曼2020加医学/药学拼写检查器》首次建议的单词进行比较，因为它利用拼写和语音相似性来建议匹配的单词。我们将每种方法的结果编码为在每次访问中是否识别出药物。
大多数类型的药物（焦虑症94.4%、哮喘98.4%、溃疡/胃食管反流病94.6%）拼写正确。交叉表评估了一致性（焦虑症99.9%、哮喘99.6%、溃疡/胃食管反流病98.4%）、假阳性（分别为0.02%、0.03%和2.0%）和假阴性（分别为1.9%、0.5%和1.0%）值。分数<120偶尔能正确识别拼写检查器遗漏的药物。我们观察到，在社会经济和文化背景各异的患者特征中，药物拼写错误没有差异。
两种方法都能有效地识别出大多数拼写错误的药物，极大地减少了所需的审查和纠正工作。模糊方法在特定疾病药物识别方面更具普遍适用性，但需要更多编程技能。拼写检查器成本低廉，但需要一定的编程技能，且仅适用于某些语言。
