OBJECTIVE: To identify demographic, social, and clinical factors associated with HIV self-management and evaluate whether the CHAMPS intervention is associated with changes in an individual's HIV self-management. METHOD: This study was a secondary data analysis from a randomized controlled trial evaluating the effects of the CHAMPS, a mHealth intervention with community health worker sessions, on HIV self-management in New York City (NYC) and Birmingham. Group comparisons and linear regression analyses identified demographic, social, and clinical factors associated with HIV self-management. We calculated interactions between groups (CHAMPS intervention and standard of care) over time (6 and 12 months) following the baseline observation, indicating a difference in the outcome scores from baseline to each time across groups. RESULTS: Our findings indicate that missing medical appointments, uncertainty about accessing care, and lack of adherence to antiretroviral therapy are associated with lower HIV self-management. For the NYC site, the CHAMPS showed a statistically significant positive effect on daily HIV self-management (estimate = 0.149, SE = 0.069, 95% CI [0.018 to 0.289]). However, no significant effects were observed for social support or the chronic nature of HIV self-management. At the Birmingham site, the CHAMPS did not yield statistically significant effects on HIV self-management outcomes. DISCUSSION: Our study suggests that CHAMPS intervention enhances daily self-management activities for people with HIV at the NYC site, indicating a promising improvement in routine HIV care. CONCLUSION: Further research is necessary to explore how various factors influence HIV self-management over time across different regions.

OBJECTIVE: Our article demonstrates the effectiveness of using a validated framework to create a ChatGPT prompt that generates valid nursing care plan suggestions for one hypothetical older patient with lung cancer. METHOD: This study describes the methodology for creating ChatGPT prompts that generate consistent care plan suggestions and its application for a lung cancer case scenario. After entering a nursing assessment of the patient's condition into ChatGPT, we asked it to generate care plan suggestions. Subsequently, we assessed the quality of the care plans produced by ChatGPT. RESULTS: While not all the suggested care plan terms (11 out of 16) utilized standardized nursing terminology, the ChatGPT-generated care plan closely matched the gold standard in scope and nature, correctly prioritizing oxygenation and ventilation needs. CONCLUSION: Using a validated framework prompt to generate nursing care plan suggestions with ChatGPT demonstrates its potential value as a decision support tool for optimizing cancer care documentation.

OBJECTIVES: Electronic health records (EHRs) user interfaces (UI) designed for data entry can potentially impact the quality of patient information captured in the EHRs. This review identified and synthesized the literature evidence about the relationship of UI features in EHRs on data quality (DQ). MATERIALS AND METHODS: We performed an integrative review of research studies by conducting a structured search in 5 databases completed on October 10, 2022. We applied Whittemore & Knafl's methodology to identify literature, extract, and synthesize information, iteratively. We adapted Kmet et al appraisal tool for the quality assessment of the evidence. The research protocol was registered with PROSPERO (CRD42020203998). RESULTS: Eleven studies met the inclusion criteria. The relationship between 1 or more UI features and 1 or more DQ indicators was examined. UI features were classified into 4 categories: 3 types of data capture aids, and other methods of DQ assessment at the UI. The Weiskopf et al measures were used to assess DQ: completeness (n = 10), correctness (n = 10), and currency (n = 3). UI features such as mandatory fields, templates, and contextual autocomplete improved completeness or correctness or both. Measures of currency were scarce. DISCUSSION: The paucity of studies on UI features and DQ underscored the limited knowledge in this important area. The UI features examined had both positive and negative effects on DQ. Standardization of data entry and further development of automated algorithmic aids, including adaptive UIs, have great promise for improving DQ. Further research is essential to ensure data captured in our electronic systems are high quality and valid for use in clinical decision-making and other secondary analyses.

Current electronic health records (EHRs) are often ineffective in identifying patient priorities and care needs requiring nurses to search a large volume of text to find clinically meaningful information. Our study, part of a larger randomized controlled trial testing nursing care planning clinical decision support coded in standardized nursing languages, focuses on identifying format preferences after random assignment and interaction to 1 of 3 formats (text only, text+table, text+graph). Being assigned to the text+graph significantly increased the preference for graph (P = .02) relative to other groups. Being assigned to the text only (P = .06) and text+table (P = .35) was not significantly associated with preference for their assigned formats. Additionally, the preference for graphs was not significantly associated with understanding graph content (P = .19). Further studies are needed to enhance our understanding of how format preferences influence the use and processing of displayed information.