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Clinical care globally faces increasing strain due to escalating documentation demands. Simultaneously, technological solutions for clinical workflows, particularly inpatient handovers, are being developed to alleviate workforce stress. However, the maturity, adoption scale, and impact of these technologies on clinical practice remain unclear. To address this gap, we conducted a scoping review to summarize current advancements in technological solutions for inpatient handovers. This study was prospectively registered on Open Science Framework. Publications from January 1, 2010, to January 1, 2024, were retrieved from MEDLINE, Embase, Cochrane Library, and Scopus. To be included in this review, studies were required to focus on (1) the implementation, assessment, or enhancement of health care provider handover workflows; (2) inpatient setting; and (3) the proposal or implementation of one or more technological solutions. Abstract and full-text screenings were conducted independently by 2 reviewers, with conflicts resolved by a third reviewer. Data extraction and synthesis were performed by multiple authors and cross-reviewed for accuracy. The search identified 779 publications, of which 53 met the inclusion criteria. Analysis revealed a predominance of low-complexity technologies, such as electronic checklists, with limited exploration of advanced solutions like natural language processing. Most studies were in the pilot stage (33/53, 62%), while some described documented implementations (11/53, 21%). Reported outcomes included improvements in the completeness, accuracy, and consistency of critical information during patient transfers (20/53, 38%). Challenges included scalability, inconsistent adoption, and difficulties integrating advanced technologies into existing workflows. Low-complexity technological solutions show potential for enhancing inpatient handovers but face barriers to scalability and sustained adoption. While artificial intelligence (AI) has the potential to bring transformative benefits, a limitation of this review is that none of the included studies reported successful clinical implementations of AI solutions aimed at improving handover processes.

Clinical communication is central to the delivery of effective, timely, and safe patient care. The use of text-based tools for clinician-to-clinician communication-commonly referred to as secure messaging-has increased exponentially over the past decade. The use of secure messaging has a potential impact on clinician work behaviors, workload, and cognitive burden. The objective of this study is to investigate the relationship between conversational multitasking-engaging in multiple concurrent, text-based secure messaging conversations-and clinician workload and cognitive burden for inpatient care. This observational cohort study included attending physicians, trainee physicians, and advanced practice providers who worked in an inpatient setting at 14 academic and community hospitals affiliated with a large academic medical center in the United States between February and April 2023. The primary exposure was the maximum number of concurrent secure messaging conversations a clinician engaged in during a workday. The co-primary outcomes were total time spent on the electronic health record (EHR; EHR time) and number of switches between patient charts (patient switching) on that workday. Linear mixed-effect models were used to measure the association between the maximum number of concurrent secure messaging conversations, EHR time, and patient switching on the clinician-day level, after adjusting for covariates (age, gender, total secure messaging volume, patient load, and clinical service assignments). In total, 50,027 clinician-days involving 3232 clinicians (1798 females, 56%; median age 37, IQR 32-46 y) and 3,556,562 secure messages were included. Median EHR time per day was 307 (IQR 204-413) minutes, and the median number of patient switches per day was 107 (IQR 60-176). Compared to clinician-days with no concurrent secure messaging conversations, engaging in a maximum of 2, 3, and 4 or more concurrent secure messaging conversations was associated with an increase in EHR time of 20.3 (95% CI 18.2-22.4), 38.0 (95% CI 34.9-41.1), and 54.8 (95% CI 50.6-58.9) minutes, respectively. Similarly, compared to clinician-days with no concurrent secure messaging conversations, engaging in a maximum of 2, 3, and 4 or more concurrent secure messaging conversations was associated with 14.5 (95% CI 11.3-17.7), 26.7 (95% CI 21.9-31.5), and 41.6 (95% CI 35.2-48.1) additional patient switches, respectively. Stratified analyses showed that trainees experienced the largest increases in EHR time (up to 82.3 min, 95% CI 73.2-91.4) and patient switches (up to 61.8, 95% CI 54.3-69.3). Higher levels of conversational multitasking were associated with increased EHR time and more patient switches in a dose-dependent manner. These results suggest that conversational multitasking may be linked with increased clinician workload and cognitive burden, emphasizing the need for guidelines and interventions to streamline secure messaging use in clinical practice.

Determining effective nurse staffing levels is crucial for ensuring quality patient care and operational efficiency within hospitals. Traditional workload prediction methods often rely on professional judgment or simple volume-based approaches, which can be inaccurate. Machine learning offers a promising avenue for more data-driven and precise predictions, by using historical nursing workload data to forecast future patient care requirements, which could help with staff planning while also improving patient outcomes and nurse well-being. This methodological study aimed to use nursing activity data, specifically LEP (Leistungserfassung in der Pflege; \"documentation of nursing activities\"), to predict the future workload requirements using machine learning techniques. We conducted a retrospective observational study at the University Hospital of Zürich, using nursing workload data for inpatients across eight wards, collected between 2017 and 2021. Data were transformed to represent nursing workload per ward and shift, with 3 shifts per day. Variables used in modeling included historical workload trends, patient characteristics, and upcoming operations. Machine learning models, including linear regression variants and tree-based methods (Random Forest and XGBoost), were trained and tested on this dataset to predict workload 72 hours in advance, on a shift-by-shift basis. Model performance was assessed using mean absolute error and mean absolute percentage error, and results were compared against a baseline of assuming no change in workload from the time of prediction. Prediction accuracy was further evaluated by categorizing future workload changes into decreased, similar, or increased workload relative to current shift levels. Our findings demonstrate that machine learning models consistently outperform the baseline across all wards. The best-performing model was the lasso regression model, which achieved an average improvement in accuracy of 25.0% compared to the baseline. When used to predict upcoming changes in workload levels, the model achieved strong classification performance, giving an average area under the receiver operating characteristic curve of 0.79 and precision values between 66.2% and 75.3%. Crucially, the model severely misclassified-predicting an upcoming increase as a decrease, and vice versa-in just 0.17% of cases, highlighting potential reliability for using the model in practice. Key variables identified as important for predictions include historical shift workload averages and overall ward workload trends. This study suggests the potential of machine learning to enhance nurse workload prediction, while highlighting the need for refinement. Limitations due to potential discrepancies between recorded nursing activities and the actual workload highlight the need for further investigation into data quality. To maximize impact, future research should focus on: (1) using more diverse data, (2) more advanced machine learning architecture that performs time-series modeling, (3) addressing data quality concerns, and (4) conducting controlled trials for real-world evaluation.

Electronic health record (EHR) data are widely used in public health research, including in HIV-related studies, but are limited by potential bias due to incomplete and inaccurate information, lack of generalizability, and lack of representativeness. This study explores how workflow processes among HIV health care providers (HCPs), data scientists, and state health department professionals may potentially introduce or minimize bias within EHR data. One focus group with 3 health department professionals working in HIV surveillance and 16 in-depth interviews (ie, 5 people with HIV, 5 HCPs, 5 data scientists, and 1 health department professional providing retention-in-care services) were conducted with participants purposively sampled in South Carolina from August 2023 to April 2024. All interviews were transcribed verbatim and analyzed using a constructivist grounded theory approach, where transcripts were first coded and then focused, axial, and theoretically coded. The EHR data lifecycle originates with people with HIV and HCPs in the clinical setting. Data scientists then curate EHR data and health department professionals manage and use the data for surveillance and policy decision-making. Throughout this lifecycle, the three primary stakeholders (ie, HCPs, data scientists, and health department professionals) identified challenges with EHR processes and provided their recommendations and accommodations in addressing the related challenges. HCPs reported the influence of socio-structural biases on their inquiry, interpretation, and documentation of social determinants of health (SDOH) information of people living with HIV, the influence of which is proposed to be mitigated through people living with HIV access to their EHRs. Data scientists identified limited data availability and representativeness as biasing the data they manage. Health department professionals face challenges with delayed and incomplete data, which may be addressed statistically but require consideration of the data's limitations. Overall, bias within the EHR data lifecycle persists because workflows are not intentionally structured to minimize bias and there is a diffusion of responsibility for data quality between the various stakeholders. From the perspective of various stakeholders, this study describes the EHR data lifecycle and its associated challenges as well as stakeholders' accommodations and recommendations for mitigating and eliminating bias in EHR data. Based upon these findings, studies reliant on EHR data should adequately consider its challenges and limitations. Throughout the EHR data lifecycle, bias could be reduced through an inclusive, supportive health care environment, people living with HIV verification of SDOH information, the customization of data collection systems, and EHR data inspection for completeness, accuracy, and timeliness. Future research is needed to further identify instances where bias is introduced and how it can best be mitigated and eliminated across the EHR data lifecycle. Systematic changes are necessary to reduce instances of bias between data workflows and stakeholders.

Recently, surgical data recorders that are comparable to flight data recorders, also known as black boxes in the aviation industry, have been developed to improve patient safety and performance in surgery. These devices allow for unique insights in the operating room by providing new data capture capabilities. No systematic review has been carried out to evaluate the areas of application of surgical data recorders to date. This systematic review and single-arm meta-analysis aims to assess the aspects of the operating theater environment for which surgical data recorders are used and to make a preliminary assessment of the quantifiable data that can be collected, compared to traditional collection methods. This systematic review followed the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Medline, Embase, and Web of Science databases were lastly systematically searched for papers that focused on a clinical use case for surgical data recorders on February 10, 2025. In particular, not relevant papers focusing on implementation of surgical data recorders were excluded. Title, abstract, and full-text screening were completed to identify relevant articles. The included studies were analyzed descriptively using data extraction forms. Where possible, quantifiable data was also analyzed. Risk of bias was assessed using the Risk Of Bias In Non-Randomized Studies of Exposure (ROBINS-E) tool. In total, 70 studies were screened, and a total of 17 studies were included. A total of 10 of the 17 studies had a low overall risk of bias; however, confounding, selection bias, small sample sizes, short study periods, and potential Hawthorne effects were the notable limitations. Only 2 studies were assessed to have publication bias. Use cases could be grouped into 4 categories: economic, safety, behavior in the operating room, and technical skill assessment. A single-arm meta-analysis focusing on adverse events and distractions in the operating theater could be conducted, demonstrating accurate reporting of distractions in line with the existing literature. Surgical data recorders provide an unobstructed view of various aspects of the operating theatre. Most published papers present preliminary studies on surgical data recorders, indicating the potential for further, larger-scale studies with enhanced methodological quality.

Emergency departments are often chaotic environments where delays can significantly impact patient care. Key items are stored in supply carts in or near patient rooms to promote efficiency and enable nurses to spend more time assisting patients. However, disorganization, lack of standardization, and lack of stocking can cause significant delays and negatively impact the quality of care. This study utilized human-centered and participatory design to improve the workflow for supply acquisition in an emergency department. Using a mixed methods, participatory design approach following the double diamond framework, the team worked with nursing staff and physicians in an urban emergency department to understand the root causes of frustrations with the current supply carts. Qualitative findings about bedside nursing workflows were integrated with quantitative observations of inventory and supply usage to drive a rapid-cycle prototyping process to optimize supply management in the bedside cart. A lack of clinical staffing exacerbates preexisting challenges with restocking the medical supplies in the bedside carts. This problem is compounded by the misallocation of supplies, with high-frequency items underrepresented and low-frequency items overrepresented in the bedside carts. This leads to wastage of the seldom-used supplies and a lack of access to the most used supplies. The reorganization of the cart through co-design with nursing staff sped up supply acquisition by approximately 20% overall, tripled the availability of the most important supplies, and reduced the need for restocking from once per shift to once per 3 shifts, thus producing tangible improvements even within institutional limitations. A participatory design process, using human factors principles in tandem with extensive input from end users, enables improvements to stocking. Implications for practice include (1) lack of easy access to appropriate supplies negatively impacts patient care and contributes to nurse burnout and frustration, (2) human factors engineering can improve access to patient care supplies through redesigning the layout of hospital supply carts to better align with workflows, and (3) co-design with frequent collaboration from stakeholders and end users ensures that solutions address the issues that matter most in a sustainable way.

Clinical coding is critical for hospital reimbursement, quality assessment, and health care planning. In Scandinavia, however, coding is often done by junior doctors or medical secretaries, leading to high rates of coding errors. Artificial intelligence (AI) tools, particularly semiautomatic computer-assisted coding tools, have the potential to reduce the excessive burden of administrative and clinical documentation. To date, much of what we know regarding these tools comes from lab-based evaluations, which often fail to account for real-world complexity and variability in clinical text. This study aims to investigate whether an AI tool developed by by Norwegian Centre for E-health Research at the University Hospital of North Norway, Easy-ICD (International Classification of Diseases), can enhance clinical coding practices by reducing coding time and improving data quality in a realistic setting. We specifically examined whether improvements differ between long and short clinical notes, defined by word count. An AI tool, Easy-ICD, was developed to assist clinical coders and was tested for improving both accuracy and time in a 1:1 crossover randomized controlled trial conducted in Sweden and Norway. Participants were randomly assigned to 2 groups (Sequence AB or BA), and crossed over between coding longer texts (Period 1; mean 307, SD 90; words) versus shorter texts (Period 2; mean 166, SD 55; words), while using our tool versus not using our tool. This was a purely web-based trial, where participants were recruited through email. Coding time and accuracy were logged and analyzed using Mann-Whitney U tests for each of the 2 periods independently, due to differing text lengths in each period. The trial had 17 participants enrolled, but only data from 15 participants (300 coded notes) were analyzed, excluding 2 incomplete records. Based on the Mann-Whitney U test, the median coding time difference for longer clinical text sequences was 123 seconds (P<.001, 95% CI 81-164), representing a 46% reduction in median coding time when our tool was used. For shorter clinical notes, the median time difference of 11 seconds was not significant (P=.25, 95% CI -34 to 8). Coding accuracy improved with Easy-ICD for both longer (62% vs 67%) and shorter clinical notes (60% vs 70%), but these differences were not statistically significant (P=.50and P=.17, respectively). User satisfaction ratings (submitted for 37% of cases) showed slightly higher approval for the tool's suggestions on longer clinical notes. This study demonstrates the potential of AI to transform common tasks in clinical workflows, with ostensible positive impacts on work efficiencies for clinical coding tasks with more demanding longer text sequences. Further studies within hospital workflows are required before these presumed impacts can be more clearly understood.

Each year, nursing informatics researchers contribute to nursing and health informatics knowledge. The year 2024 emerged as yet another year of significant advances. In this editorial, I describe and highlight some of the key trends in nursing informatics research as published in JMIR Nursing in 2024. Artificial intelligence (AI), data science, mobile health (mHealth), and the integration of technology into nursing education and practice remain key research themes in the literature. Nursing informatics publications continue to grow in number. A greater number of AI and data science articles are being published, while at the same time, mHealth and technology research continues to be conducted in nursing education and practice contexts.

Chronic obstructive pulmonary disease (COPD) affects more than 16 million US adults, many of whom experience high rates of acute care revisits (emergency department and hospital) after initial hospitalization. These frequent exacerbations, often due to failures in transitions of care (TOC), lead to lung function decline and premature mortality. While effective interventions exist to reduce readmissions, wide-scale implementation of COPD TOC programs remains limited. The National Institutes of Health-funded Reducing Respiratory Emergency Visits Using Implementation Science Interventions Tailored to Settings (REVISITS) study was designed to address this implementation gap by developing and implementing bundled COPD TOC programs across diverse US hospitals. This study aimed to conduct pre-implementation contextual assessments at US hospitals to guide the development of site-specific, evidence-based COPD TOC programs. We conducted pre-implementation contextual assessments using a novel semi-structured interview format that integrated the Consolidated Framework for Implementation Research (CFIR) with human-centered design approaches (ethnographic interviewing) to capture real-world experiences of COPD care across inpatient, outpatient, and home settings. We used a sequential explanatory mixed methods design in which pre-interview survey data completed by site leads informed and shaped the subsequent semi-structured interviews. Site leads, clinicians, organizational leaders, patients, and caregivers were interviewed. Interviews explored baseline COPD TOC practices, local resources, opportunities for improvement, as well as participant priorities from a menu of 12 evidence-based interventions (eg, pulmonary rehabilitation, patient navigation, and inhaler teaching). Rapid analysis methods identified intervention priorities across participant groups, along with perceived barriers and facilitators to implementation. Findings were shared with site leads to help guide their development of tailored COPD TOC programs. Among 194 participants from 21 sites (42 site leads, 29 organizational leaders, 105 clinicians, and 18 patients or caregivers), the highest priority interventions identified during interviews were post-emergency department follow-up visits, education (inhaler technique, disease management, and action plan), and pulmonary rehabilitation. Reported barriers included clinician-level challenges (limited training, staffing, and time), patient-level challenges (social needs and physical burden of COPD), and system-level challenges (lack of standardization, limited resources, and cost). Key facilitators included the presence of dedicated staff and the availability of pre-existing programs or infrastructure. The 3 most commonly chosen interventions for implementation were patient education (eg, inhaler education and COPD action plans), medication reconciliation, and post-discharge care (eg, post-discharge visits and pulmonary rehabilitation). This study demonstrates how the integration of implementation science and human-centered design approaches can yield valuable insights, beyond what either field could obtain separately, during the pre-implementation phase of COPD TOC program implementation development. Contextual assessments that capture diverse views are instrumental in designing feasible and relevant interventions. Future work will explore how pre-implementation insights relate to post-implementation outcomes across participating sites.