{"title":"Investigation on the preferences for data quality assessment indicators of electronic health records: user-oriented perspective.","authors":"Liu Yang, Mudan Ren, Shuifa Sun, Ji Lu, Yirong Wu","doi":"10.1093/jamiaopen/ooae142","DOIUrl":null,"url":null,"abstract":"<p><strong>Objectives: </strong>This study aims to investigate whether different types of electronic health record (EHR) users have distinct preferences for data quality assessment indicators (DQAI) and explore how these preferences can guide the enhancement of EHR systems and the optimization of related policies.</p><p><strong>Materials and methods: </strong>High-frequency indicators were identified by a systematic literature review to construct a DQAI system, which was assessed by a user-oriented investigation involving doctors, nurses, hospital supervisors, and clinical researchers. The entropy weight method and fuzzy comprehensive evaluation model were employed for the system comprehensive evaluation. Exploratory factor analysis was used to construct dimensions, and visualization analysis was utilized to explore preferences at both the indicator and dimension levels.</p><p><strong>Results: </strong>Sixteen indicators were identified to construct the DQAI system and grouped into 2 dimensions: structural and relational. The DQAI system achieved a comprehensive evaluation score of 90.445, corresponding to a \"very important\" membership level (62.5%). Doctors and nurses exhibited a higher score mean (4.43-4.66 out of 5) than supervisors (3.73-4.55 out of 5). Researchers emphasized credibility, with a score mean of 4.79 out of 5.</p><p><strong>Discussion: </strong>The findings reveal that different types of EHR users exhibit distinct preferences for the DQAI at both indicator and dimension levels. Doctors and nurses thought that all indicators were important, clinical researchers emphasized credibility, and supervisors focused mainly on accuracy. Indicators in the relational dimension were generally more valued than structural ones. Doctors and nurses prioritized indicators of relational dimension, while researchers and supervisors leaned towards indicators of structural dimension. These insights suggest that tailored approaches in EHR system development and policy-making could enhance EHR data quality.</p><p><strong>Conclusion: </strong>This study underscores the importance of user-centered approaches in optimizing EHR systems, highlighting diverse user preferences at both indicator and dimension levels.</p>","PeriodicalId":36278,"journal":{"name":"JAMIA Open","volume":"7 4","pages":"ooae142"},"PeriodicalIF":2.5000,"publicationDate":"2024-12-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11633948/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JAMIA Open","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1093/jamiaopen/ooae142","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/12/1 0:00:00","PubModel":"eCollection","JCR":"Q2","JCRName":"HEALTH CARE SCIENCES & SERVICES","Score":null,"Total":0}
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Abstract
Objectives: This study aims to investigate whether different types of electronic health record (EHR) users have distinct preferences for data quality assessment indicators (DQAI) and explore how these preferences can guide the enhancement of EHR systems and the optimization of related policies.
Materials and methods: High-frequency indicators were identified by a systematic literature review to construct a DQAI system, which was assessed by a user-oriented investigation involving doctors, nurses, hospital supervisors, and clinical researchers. The entropy weight method and fuzzy comprehensive evaluation model were employed for the system comprehensive evaluation. Exploratory factor analysis was used to construct dimensions, and visualization analysis was utilized to explore preferences at both the indicator and dimension levels.
Results: Sixteen indicators were identified to construct the DQAI system and grouped into 2 dimensions: structural and relational. The DQAI system achieved a comprehensive evaluation score of 90.445, corresponding to a "very important" membership level (62.5%). Doctors and nurses exhibited a higher score mean (4.43-4.66 out of 5) than supervisors (3.73-4.55 out of 5). Researchers emphasized credibility, with a score mean of 4.79 out of 5.
Discussion: The findings reveal that different types of EHR users exhibit distinct preferences for the DQAI at both indicator and dimension levels. Doctors and nurses thought that all indicators were important, clinical researchers emphasized credibility, and supervisors focused mainly on accuracy. Indicators in the relational dimension were generally more valued than structural ones. Doctors and nurses prioritized indicators of relational dimension, while researchers and supervisors leaned towards indicators of structural dimension. These insights suggest that tailored approaches in EHR system development and policy-making could enhance EHR data quality.
Conclusion: This study underscores the importance of user-centered approaches in optimizing EHR systems, highlighting diverse user preferences at both indicator and dimension levels.
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