Factors Influencing Information Distortion in Electronic Nursing Records: Qualitative Study

影响电子护理记录信息失真的因素:一项定性研究

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Abstract

BACKGROUND: Information distortion in nursing records poses significant risks to patient safety and impedes the enhancement of care quality. The introduction of information technologies, such as decision support systems and predictive models, expands the possibilities for using health data but also complicates the landscape of information distortion. Only by identifying influencing factors about information distortion can care quality and patient safety be ensured. OBJECTIVE: This study aims to explore the factors influencing information distortion in electronic nursing records (ENRs) within the context of China's health care system and provide appropriate recommendations to address these distortions. METHODS: This qualitative study used semistructured interviews conducted with 14 nurses from a Class-A tertiary hospital. Participants were primarily asked about their experiences with and observations of information distortion in clinical practice, as well as potential influencing factors and corresponding countermeasures. Data were analyzed using inductive content analysis, which involved initial preparation, line-by-line coding, the creation of categories, and abstraction. RESULTS: The analysis identified 4 categories and 10 subcategories: (1) nurse-related factors-skills, awareness, and work habits; (2) patient-related factors-willingness and ability; (3) operational factors-work characteristics and system deficiencies; and (4) organizational factors-management system, organizational climate, and team collaboration. CONCLUSIONS: Although some factors influencing information distortion in ENRs are similar to those observed in paper-based records, others are unique to the digital age. As health care continues to embrace digitalization, it is crucial to develop and implement strategies to mitigate information distortion. Regular training and education programs, robust systems and mechanisms, and optimized human resources and organizational practices are strongly recommended.

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