Rashaud Senior, Timothy Tsai, William Ratliff, Lisa Nadler, Suresh Balu, Elizabeth Malcolm, Eugenia McPeek Hinz
{"title":"评估 SNOMED CT 分类器在按临床系统整理电子健康记录问题列表时的准确性和覆盖范围:观察研究","authors":"Rashaud Senior, Timothy Tsai, William Ratliff, Lisa Nadler, Suresh Balu, Elizabeth Malcolm, Eugenia McPeek Hinz","doi":"10.2196/51274","DOIUrl":null,"url":null,"abstract":"Background: The Problem List (PL) is often poorly organized which makes the use for clinical care more challenging over time. Objective: To measure the accuracy of diagnoses sorting for PL system/conditions groupers based on SNOMED-CT concepts mapped to ICD-10 codes. Methods: We developed 21 system/condition-based groupers using SNOMED-CT hierarchal concepts refined with Boolean logic to re-organize the ICD-10-based PL in our electronic health record (EHR). We extracted the PL from a convenience sample of 50 patients divided across age and sex in a deidentified format for evaluation. Two clinicians independently determined whether a PL diagnosis was correctly attributed to a system/condition grouper. Discrepancies were discussed and, if no consensus was reached, were adjudicated by a third clinician. Descriptive statistics and Cohen’s kappa statistic for interrater reliability were calculated. Results: Our 50-patient sample had a total of 869 diagnoses (range 4–59; median 12, IQR 9-23.75). The reviewers initially agreed on 821 placements. Of the remaining 48 items, 16 required adjudication, leading to a final count of 787 True Positives and 37 True Negatives. We determined PL diagnoses were grouped with Sensitivity 97.6%, Specificity 58.7%, Positive Predictive Value 96.8%, and F1 Score 0.972. After discussion, the calculated kappa statistic was 0.9, confirming “near perfect” agreement. Conclusions: We successfully developed a structured methodology to organize diagnoses on the problem list that supports clinical review.","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":null,"pages":null},"PeriodicalIF":3.1000,"publicationDate":"2024-05-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Evaluation of SNOMED CT Grouper Accuracy and Coverage in Organizing the Electronic Health Record Problem List by Clinical System: Observational Study\",\"authors\":\"Rashaud Senior, Timothy Tsai, William Ratliff, Lisa Nadler, Suresh Balu, Elizabeth Malcolm, Eugenia McPeek Hinz\",\"doi\":\"10.2196/51274\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Background: The Problem List (PL) is often poorly organized which makes the use for clinical care more challenging over time. Objective: To measure the accuracy of diagnoses sorting for PL system/conditions groupers based on SNOMED-CT concepts mapped to ICD-10 codes. Methods: We developed 21 system/condition-based groupers using SNOMED-CT hierarchal concepts refined with Boolean logic to re-organize the ICD-10-based PL in our electronic health record (EHR). We extracted the PL from a convenience sample of 50 patients divided across age and sex in a deidentified format for evaluation. Two clinicians independently determined whether a PL diagnosis was correctly attributed to a system/condition grouper. Discrepancies were discussed and, if no consensus was reached, were adjudicated by a third clinician. Descriptive statistics and Cohen’s kappa statistic for interrater reliability were calculated. Results: Our 50-patient sample had a total of 869 diagnoses (range 4–59; median 12, IQR 9-23.75). The reviewers initially agreed on 821 placements. Of the remaining 48 items, 16 required adjudication, leading to a final count of 787 True Positives and 37 True Negatives. We determined PL diagnoses were grouped with Sensitivity 97.6%, Specificity 58.7%, Positive Predictive Value 96.8%, and F1 Score 0.972. After discussion, the calculated kappa statistic was 0.9, confirming “near perfect” agreement. Conclusions: We successfully developed a structured methodology to organize diagnoses on the problem list that supports clinical review.\",\"PeriodicalId\":56334,\"journal\":{\"name\":\"JMIR Medical Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":3.1000,\"publicationDate\":\"2024-05-09\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"JMIR Medical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.2196/51274\",\"RegionNum\":3,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/51274","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
Evaluation of SNOMED CT Grouper Accuracy and Coverage in Organizing the Electronic Health Record Problem List by Clinical System: Observational Study
Background: The Problem List (PL) is often poorly organized which makes the use for clinical care more challenging over time. Objective: To measure the accuracy of diagnoses sorting for PL system/conditions groupers based on SNOMED-CT concepts mapped to ICD-10 codes. Methods: We developed 21 system/condition-based groupers using SNOMED-CT hierarchal concepts refined with Boolean logic to re-organize the ICD-10-based PL in our electronic health record (EHR). We extracted the PL from a convenience sample of 50 patients divided across age and sex in a deidentified format for evaluation. Two clinicians independently determined whether a PL diagnosis was correctly attributed to a system/condition grouper. Discrepancies were discussed and, if no consensus was reached, were adjudicated by a third clinician. Descriptive statistics and Cohen’s kappa statistic for interrater reliability were calculated. Results: Our 50-patient sample had a total of 869 diagnoses (range 4–59; median 12, IQR 9-23.75). The reviewers initially agreed on 821 placements. Of the remaining 48 items, 16 required adjudication, leading to a final count of 787 True Positives and 37 True Negatives. We determined PL diagnoses were grouped with Sensitivity 97.6%, Specificity 58.7%, Positive Predictive Value 96.8%, and F1 Score 0.972. After discussion, the calculated kappa statistic was 0.9, confirming “near perfect” agreement. Conclusions: We successfully developed a structured methodology to organize diagnoses on the problem list that supports clinical review.
期刊介绍:
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.