CIC2022:利用二维条形码和条形码扫描实践提高免疫医疗数据质量的信息学驱动战略。

IF 2.1 2区 医学 Q4 MEDICAL INFORMATICS
Applied Clinical Informatics Pub Date : 2024-03-01 Epub Date: 2024-01-29 DOI:10.1055/a-2255-9749
Faisal Reza, Caroline Jones, Jenica H Reed
{"title":"CIC2022:利用二维条形码和条形码扫描实践提高免疫医疗数据质量的信息学驱动战略。","authors":"Faisal Reza, Caroline Jones, Jenica H Reed","doi":"10.1055/a-2255-9749","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong> Manual data entry is time-consuming, inefficient, and error prone. In contrast, leveraging two-dimensional (2D) barcodes and barcode scanning tools is a rapid and effective practice for automatically entering vaccine data accurately and completely. CDC pilots documented clinical and public health impacts of 2D barcode scanning practices on data quality and completeness, time savings, workflow efficiencies, and staff experience.</p><p><strong>Objectives: </strong> Data entry practices and entered records from routine and mass vaccination settings were analyzed. Data quality improvement opportunities were identified.</p><p><strong>Methods: </strong> A sample of 50 million emergency use authorization (EUA) coronavirus disease 2019 (COVID-19) vaccine records were analyzed for accuracy and completeness across three data fields: lot number, expiration date, and National Drug Code (NDC). The EUA COVID-19 vaccines lacked a 2D barcode containing these data fields, which necessitated manual data entry at administration. A CDC pilot at clinic compared scanned and manually entered data for routine vaccines across these same data fields.</p><p><strong>Results: </strong> Analysis of 50 million manually entered EUA COVID-19 vaccine administration records indicated significant gaps in data accuracy and completeness across three data fields. Over half of the analyzed EUA vaccine NDCs (53%) and one-third of the expiration dates (35%) had missing or inaccurate data recorded. Pilot data also showed many errors when manually entered. However, when the pilot's routine vaccines were scanned (out of 71,969 records), nearly all entries were complete and accurate across all three data fields (ranging from 99.7% to 99.999% accurate).</p><p><strong>Conclusion: </strong> Vaccine 2D barcode scanning practices increased data accuracy and completeness (up to 99.999% accurate) across data fields assessed. When used consistently, vaccine 2D barcode scanning can resolve issues demonstrated in manually entered data. To realize these benefits, the immunization community should widely use scanning practices. To increase use, CDC developed a Vaccine 2D Barcode National Adoption Strategy and implementation resources.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":null,"pages":null},"PeriodicalIF":2.1000,"publicationDate":"2024-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10990595/pdf/","citationCount":"0","resultStr":"{\"title\":\"Improving Immunization Health Care Data Quality using Two-Dimensional Barcoding and Barcode Scanning Practices.\",\"authors\":\"Faisal Reza, Caroline Jones, Jenica H Reed\",\"doi\":\"10.1055/a-2255-9749\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong> Manual data entry is time-consuming, inefficient, and error prone. In contrast, leveraging two-dimensional (2D) barcodes and barcode scanning tools is a rapid and effective practice for automatically entering vaccine data accurately and completely. CDC pilots documented clinical and public health impacts of 2D barcode scanning practices on data quality and completeness, time savings, workflow efficiencies, and staff experience.</p><p><strong>Objectives: </strong> Data entry practices and entered records from routine and mass vaccination settings were analyzed. Data quality improvement opportunities were identified.</p><p><strong>Methods: </strong> A sample of 50 million emergency use authorization (EUA) coronavirus disease 2019 (COVID-19) vaccine records were analyzed for accuracy and completeness across three data fields: lot number, expiration date, and National Drug Code (NDC). The EUA COVID-19 vaccines lacked a 2D barcode containing these data fields, which necessitated manual data entry at administration. A CDC pilot at clinic compared scanned and manually entered data for routine vaccines across these same data fields.</p><p><strong>Results: </strong> Analysis of 50 million manually entered EUA COVID-19 vaccine administration records indicated significant gaps in data accuracy and completeness across three data fields. Over half of the analyzed EUA vaccine NDCs (53%) and one-third of the expiration dates (35%) had missing or inaccurate data recorded. Pilot data also showed many errors when manually entered. However, when the pilot's routine vaccines were scanned (out of 71,969 records), nearly all entries were complete and accurate across all three data fields (ranging from 99.7% to 99.999% accurate).</p><p><strong>Conclusion: </strong> Vaccine 2D barcode scanning practices increased data accuracy and completeness (up to 99.999% accurate) across data fields assessed. When used consistently, vaccine 2D barcode scanning can resolve issues demonstrated in manually entered data. To realize these benefits, the immunization community should widely use scanning practices. To increase use, CDC developed a Vaccine 2D Barcode National Adoption Strategy and implementation resources.</p>\",\"PeriodicalId\":48956,\"journal\":{\"name\":\"Applied Clinical Informatics\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-03-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10990595/pdf/\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Clinical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1055/a-2255-9749\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"2024/1/29 0:00:00\",\"PubModel\":\"Epub\",\"JCR\":\"Q4\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Clinical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1055/a-2255-9749","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/1/29 0:00:00","PubModel":"Epub","JCR":"Q4","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 0

摘要

手动数据录入耗时、效率低且容易出错。相比之下,利用二维 (2D) 条形码和条形码扫描工具是一种快速、高效和有效的做法,可在疫苗库存和管理过程中自动准确完整地输入数据。疾病预防控制中心在各种医疗机构开展的试点工作记录了二维条形码扫描实践对数据质量和完整性、时间节省、工作流程效率和员工体验的临床和公共卫生影响。对 5000 万份紧急使用授权 (EUA) COVID-19 疫苗记录样本进行了分析,以确定三个数据字段的准确性和完整性:批号、有效期和国家药品代码 (NDC)。这些疫苗没有包含这些数据字段的二维条形码,因此在接种时需要手动输入疫苗数据。在疾控中心与诊所试点期间,对常规疫苗的自动扫描数据和手动输入数据进行了比较,以确定这些数据字段的准确性和完整性。对 5000 万份手动输入的 EUA 疫苗接种记录进行的分析表明,在所观察到的三个数据字段中,数据的准确性和完整性存在很大差距。在分析的 5000 万份 EUA 疫苗 NDC 中,超过一半(53%)和三分之一(35%)的过期日期记录了缺失或不准确的数据。试点数据在手动输入时也显示出许多错误。然而,在试点期间扫描了 67951 支(共 72979 支)疫苗后,几乎所有的输入在所有三个数据字段中都是完整和准确的(准确率从 99.7% 到 99.999% 不等)。疫苗二维条形码扫描实践提高了各评估数据字段的数据准确性和完整性(准确率高达 99.999%)。如果坚持使用,疫苗二维条形码扫描可以解决手工输入数据中出现的问题。为了实现这些优势,免疫界应广泛采用扫描方法。为了提高使用率,疾病预防控制中心制定了疫苗二维条形码国家采用战略和资源,以指导实施工作。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Improving Immunization Health Care Data Quality using Two-Dimensional Barcoding and Barcode Scanning Practices.

Background:  Manual data entry is time-consuming, inefficient, and error prone. In contrast, leveraging two-dimensional (2D) barcodes and barcode scanning tools is a rapid and effective practice for automatically entering vaccine data accurately and completely. CDC pilots documented clinical and public health impacts of 2D barcode scanning practices on data quality and completeness, time savings, workflow efficiencies, and staff experience.

Objectives:  Data entry practices and entered records from routine and mass vaccination settings were analyzed. Data quality improvement opportunities were identified.

Methods:  A sample of 50 million emergency use authorization (EUA) coronavirus disease 2019 (COVID-19) vaccine records were analyzed for accuracy and completeness across three data fields: lot number, expiration date, and National Drug Code (NDC). The EUA COVID-19 vaccines lacked a 2D barcode containing these data fields, which necessitated manual data entry at administration. A CDC pilot at clinic compared scanned and manually entered data for routine vaccines across these same data fields.

Results:  Analysis of 50 million manually entered EUA COVID-19 vaccine administration records indicated significant gaps in data accuracy and completeness across three data fields. Over half of the analyzed EUA vaccine NDCs (53%) and one-third of the expiration dates (35%) had missing or inaccurate data recorded. Pilot data also showed many errors when manually entered. However, when the pilot's routine vaccines were scanned (out of 71,969 records), nearly all entries were complete and accurate across all three data fields (ranging from 99.7% to 99.999% accurate).

Conclusion:  Vaccine 2D barcode scanning practices increased data accuracy and completeness (up to 99.999% accurate) across data fields assessed. When used consistently, vaccine 2D barcode scanning can resolve issues demonstrated in manually entered data. To realize these benefits, the immunization community should widely use scanning practices. To increase use, CDC developed a Vaccine 2D Barcode National Adoption Strategy and implementation resources.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Applied Clinical Informatics
Applied Clinical Informatics MEDICAL INFORMATICS-
CiteScore
4.60
自引率
24.10%
发文量
132
期刊介绍: ACI is the third Schattauer journal dealing with biomedical and health informatics. It perfectly complements our other journals Öffnet internen Link im aktuellen FensterMethods of Information in Medicine and the Öffnet internen Link im aktuellen FensterYearbook of Medical Informatics. The Yearbook of Medical Informatics being the “Milestone” or state-of-the-art journal and Methods of Information in Medicine being the “Science and Research” journal of IMIA, ACI intends to be the “Practical” journal of IMIA.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信