Abstract Objectives This case report describes the implementation and outcomes of robotic process automation (RPA) interventions used as an electronic health record (EHR)-based reliability layer for two inpatient quality initiatives: First Contact Provider (FCP) assignment and Admission Medication Reconciliation (AMR) within 24 hours. Methods RPA bots were created to support two inpatient quality initiatives by monitoring EHR patient lists for predefined care gaps: missing FCP assignment and incomplete AMR. When a gap was identified, the bot opened the patient chart, initiated a secure-message conversation with frontline clinicians and quality improvement (QI) champions, and generated operational reports to support follow-up. Chi-square tests compared FCP before (September 1, 2022–February 28, 2023) versus after implementation (March 1–August 31, 2023), and AMR before (December 1, 2022–May 31, 2023) versus after implementation (June 1–November 30, 2023). Results For FCP, the bot identified 9361 encounters without an assignment across 118 hospital units and generated 14,984 secure-message conversations linking 17 QI champions with 2,336 providers. FCP assignment improved from 81.2% (5,096,966/6,269,754 patient-hours) to 88.5% (5,858,442/6,619,047; p < 0.001). For AMR, the bot identified 10,680 incomplete reconciliations across 131 hospital units and generated 21,502 secure-message conversations linking 208 QI champions with 3,234 unique providers. Timely completion improved from 86% (33,886/39,489 encounters) to 96% (39,790/41,624; p < 0.001). Conclusion Implementation of RPA was associated with substantial improvement in FCP assignment and timely AMR completion. Initiating a bot-enabled secure-chat “virtual safety huddle” may provide a practical EHR reliability layer for rule-based inpatient care gaps.
Background Large language models can return citations whose PubMed Identifier (PMID), digital object identifier (DOI), title, and journal appear plausible but identify different papers. Checking one valid identifier may therefore fail to detect an incorrect citation bundle. Objective This study aimed to measure citation metadata incoherence in responses from five large language model snapshots to an internal medicine prompt benchmark. Methods Five models answered 30 synthetic prompts each, producing 150 model-by-prompt responses. Twenty-six prompts per model requested citations, and four were designed to elicit abstention. A corrected parser extracted 300 citation units. We checked reported PMIDs against PubMed metadata, queried reported DOIs separately, and rechecked DOI strings not found in PubMed through Crossref. Response-level outcomes were primary because citation units were nested within responses. The author reviewed a deterministic stratified sample of 50 units for parser capture and citation identity. Results Among 130 citation-expected responses, 69 contained a DOI that resolved to a different PMID than the PMID supplied by the model, 100 contained at least one automated metadata mismatch, and 28 contained no citation. Those 100 were 69 DOI-PMID, 29 other, and 2 unresolvable. All 20 abstention trap responses abstained. Among 116 units whose reported DOI resolved in PubMed, 99 resolved to a different PMID and 17 to the reported PMID. Human review found correct parser capture in 48 of 50 sampled units. Two section boundary errors were corrected before the final rerun; the primary counts did not change. Human review classified 32 sampled units as incoherent, 7 as coherent, 6 as having an unresolvable PMID, and 5 as containing no citation. Clinical claim support was not assessed. Conclusion Citation identity failures occurred in all five tested model snapshots. Evaluation and clinical evidence workflows should confirm that the PMID, DOI, title, and journal identify the same source before treating a model-supplied citation as evidence.
Background The rapid expansion of digital health care technologies has substantially transformed nursing practice and has been accompanied by growing concerns regarding technology-related stress and burnout among nurses. Despite the growing body of literature, the intellectual structure and thematic evolution of this field remain insufficiently explored from a bibliometric perspective. Objectives This study aimed to conduct a comprehensive bibliometric and knowledge mapping analysis of the literature on technology-related stress and burnout in nursing practice. Methods Bibliographic records were retrieved from the Web of Science Core Collection database. A total of 345 publications published between 2000 and 2026 were analyzed using the Bibliometrix package and Biblioshiny interface in R software. Bibliometric indicators, keyword co-occurrence analysis, thematic mapping, trend topic analysis, and thematic evolution analyses were performed to evaluate the conceptual structure and development in the field. Results Scientific production increased markedly after 2020, with an annual growth rate of 15.77%. "Electronic health records," "digital health," "nursing," "nursing informatics," and "burnout" emerged as the dominant research themes. The United States was the leading contributor in publication output, whereas CIN: Computers, Informatics, Nursing was identified as the most productive journal. Keyword co-occurrence analyses revealed strong relationships among workload, stress, usability, and documentation burden. Furthermore, thematic analyses demonstrated increasing attention toward artificial intelligence, ChatGPT, and generative AI within nursing informatics and digital health care research. Conclusion The literature has evolved from traditional documentation and workload concerns toward broader digital health and artificial intelligence-related themes. As intelligent health care technologies continue to expand, future research should investigate their relationships with nurses' cognitive workload, perceived usability, and occupational well-being.
Abstract Background Falls are a leading cause of morbidity, functional decline, and premature care entry among older adults. Assistive living technologies (ALTs) are increasingly used for fall prevention, detection, and risk monitoring, but their value depends on how older adults, caregivers, and care staff experience them in practice. Objectives The objective of this study is to synthesize user-reported experiences of ALTs used for fall prevention, detection, or monitoring across private homes, retirement villages (RVs), and residential aged care homes (RACHs), examining perceived value, implementation friction, adaptation over time, and contextual differences. Methods A thematic synthesis was conducted within a scoping review. Searches of Embase, MEDLINE, CINAHL, and Scopus covered 2010 to June 2025. Studies reporting user experience data on real-world ALT deployment in nonhospital settings were included. Line-by-line codes were developed into descriptive subthemes and three analytical themes: negotiated value, implementation friction, and adaptation as relational work. Findings were compared by setting, technology, and evidence source. Results Twenty studies published between 2012 and 2024 were included. Eighteen reported at least one implementation challenge; the remaining two did not provide evidence challenges were absent. Perceived value was negotiated by setting: autonomy and self-management dominated in private homes, whereas RACH evidence was staff or family mediated and tied to situational awareness and workflow. Implementation friction stemmed from unreliable alerts, device burden, privacy concerns, infrastructure, and organizational integration. Twelve studies described some changes during deployment, but reporting was heterogeneous and did not support a universal familiarization timeframe. The single RV study offered no user experience evidence. Conclusion ALT value is produced through alignment among technology, user roles, care context, and available resources. Sustained use requires user-centered design, workflow integration, infrastructure and financial planning, and support tailored rather than assumed. Future research should prioritize RVs, direct resident perspectives in RACHs, cost and socioeconomic reporting, and prospective cross-setting comparisons.
