Manifestations and Potential Consequences of Information Distortion in Electronic Nursing Records: Qualitative Study

Abstract Background Nursing records are a critical reflection of nursing practices and play a key role in ensuring the quality of patient care. However, these records sometimes fail to accurately represent the real-world health truth, resulting in information distortion. The manifestations and potential consequences of such distortion remain understudied, which may hinder efforts to improve nursing practices. Objective This study aims to explore the primary manifestations and potential consequences of information distortion in electronic nursing records (ENRs). Methods We conducted semistructured interviews at a Class-A tertiary hospital in China. A total of 12 nurses from various care units were recruited through purposive and snowball sampling. Inductive content analysis was used to analyze the qualitative data. Results Qualitative analysis revealed that manifestations of information distortion in ENRs fall into 4 main categories: untimeliness, inaccuracy, incompleteness, and inconsistency. These categories are further subdivided into 7 subcategories: premature recording, delayed recording, incorrect data entry, fabrication, account misuse, missing data, and inconsistent data entry. The potential consequences of information distortion encompass 3 main categories: patient-related, nurse-related, and data-related. These categories are further subdivided into 5 subcategories: threats to patient safety, increases in negative emotions, damage to professional image, erosion of nursing professionalism, and impairment of secondary data use. Conclusions The identified manifestations encompass a wide range of nursing activities, with potential consequences affecting patients, research, and caregivers. Strengthening ethics education, implementing monitoring mechanisms, establishing standardized guidelines, and leveraging technological solutions are recommended to reduce information distortion in ENRs. Further research on contributing factors and effective countermeasures is needed to mitigate information distortion in ENRs.