دورية أكاديمية
Assessment of Data Quality Variability across Two EHR Systems through a Case Study of Post-Surgical Complications.
| العنوان: | Assessment of Data Quality Variability across Two EHR Systems through a Case Study of Post-Surgical Complications. |
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| المؤلفون: | Fu S; Department of AI and Informatics Research, Mayo Clinic, Rochester, MN, USA., Wen A; Department of AI and Informatics Research, Mayo Clinic, Rochester, MN, USA., Schaeferle GM; Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA., Wilson PM; Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA., Demuth G; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA., Ruan X; Department of AI and Informatics Research, Mayo Clinic, Rochester, MN, USA., Liu S; Department of AI and Informatics Research, Mayo Clinic, Rochester, MN, USA., Storlie C; Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA.; Kern Center for the Science of Healthcare Delivery, Mayo Clinic, Rochester, MN, USA., Liu H; Department of AI and Informatics Research, Mayo Clinic, Rochester, MN, USA. |
| المصدر: | AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science [AMIA Jt Summits Transl Sci Proc] 2022 May 23; Vol. 2022, pp. 196-205. Date of Electronic Publication: 2022 May 23 (Print Publication: 2022). |
| نوع المنشور: | Journal Article |
| اللغة: | English |
| بيانات الدورية: | Publisher: AMIA Country of Publication: United States NLM ID: 101539486 Publication Model: eCollection Cited Medium: Internet ISSN: 2153-4063 (Electronic) NLM ISO Abbreviation: AMIA Jt Summits Transl Sci Proc Subsets: PubMed not MEDLINE |
| أسماء مطبوعة: | Original Publication: Bethesda, MD : AMIA, [2011]- |
| مستخلص: | Translation of predictive modeling algorithms into routine clinical care workflows faces challenges in the form of varying data quality-related issues caused by the heterogeneity of electronic health record (EHR) systems. To better understand these issues, we retrospectively assessed and compared the variability of data produced from two different EHR systems. We considered three dimensions of data quality in the context of EHR-based predictive modeling for three distinct translational stages: model development (data completeness), model deployment (data variability), and model implementation (data timeliness). The case study was conducted based on predicting post-surgical complications using both structured and unstructured data. Our study discovered a consistent level of data completeness, a high syntactic, and moderate-high semantic variability across two EHR systems, for which the quality of data is context-specific and closely related to the documentation workflow and the functionality of individual EHR systems. (©2022 AMIA - All rights reserved.) |
| معلومات مُعتمدة: | R01 EB019403 United States EB NIBIB NIH HHS; U01 TR002062 United States TR NCATS NIH HHS |
| تواريخ الأحداث: | Date Created: 20220720 Date Completed: 20230601 Latest Revision: 20230601 |
| رمز التحديث: | 20231215 |
| مُعرف محوري في PubMed: | PMC9285181 |
| PMID: | 35854735 |
| قاعدة البيانات: | MEDLINE |
| تدمد: | 2153-4063 |
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