Investigation on the preferences for data quality assessment indicators of electronic health records: user-oriented perspective
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View abstract on PubMed
Summary
This summary is machine-generated.Different electronic health record (EHR) users prioritize data quality indicators differently. Understanding these preferences for data quality assessment indicators (DQAI) can improve EHR systems and policies.
Area Of Science
- Health Informatics
- Data Quality Management
- Electronic Health Records
Background
- Electronic Health Records (EHRs) are crucial for modern healthcare delivery.
- Ensuring the quality of data within EHR systems is paramount for patient safety and effective clinical decision-making.
- Varied user roles within healthcare may have differing perspectives on what constitutes high-quality EHR data.
Purpose Of The Study
- To investigate distinct preferences for data quality assessment indicators (DQAI) among different types of EHR users.
- To explore how these user preferences can inform the enhancement of EHR systems.
- To optimize policies related to EHR data quality.
Main Methods
- A systematic literature review identified high-frequency indicators for a DQAI system.
- A user-oriented investigation involved doctors, nurses, hospital supervisors, and clinical researchers.
- The entropy weight method, fuzzy comprehensive evaluation, exploratory factor analysis, and visualization analysis were employed for evaluation and preference exploration.
Main Results
- Sixteen DQAI indicators were identified and grouped into structural and relational dimensions.
- The DQAI system achieved a high comprehensive evaluation score (90.445), indicating "very important" status.
- User preferences varied: doctors and nurses valued all indicators, researchers emphasized credibility, and supervisors focused on accuracy. Relational indicators were generally preferred over structural ones.
Conclusions
- Different EHR user groups exhibit distinct preferences for DQAI at both indicator and dimension levels.
- Tailored approaches to EHR system development and policy-making are necessary to enhance data quality.
- User-centered strategies are essential for optimizing EHR systems and improving data quality outcomes.
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