Ambient artificial intelligence scribes are increasingly integrated into electronic health records to reduce documentation burden, but limited data describe their performance in high-complexity pediatric subspecialties. To describe early implementation of Dragon Ambient Experience (DAX) Copilot in a pediatric hematology-oncology division and evaluate patterns of use, documentation time, and workflow effect among providers who adopted the tool. We reviewed outpatient encounters from January to July 2025 and identified those using DAX. Analyses focused on providers who used DAX more than twice, given low overall adoption. Documentation time (minutes spent in active editing sessions) and note-closure timeliness were compared between DAX and non-DAX encounters. Implementation processes, barriers, and accuracy concerns were qualitatively summarized. Of 11,544 outpatient encounters, 427 (3.7%) involved DAX; 10 of 29 providers used DAX at least once, while 6 (20.7%) used it more than twice. Among repeat users, for shorter encounters (≤40 minutes), median documentation time was lower for DAX vs. non-DAX (11 [IQR 13] vs 24 [IQR 30] minutes), corresponding to an approximately 30% lower documentation time (95% CI, 19.9%-38.4%; p < 0.001). For longer encounters (>40 minutes), documentation time did not differ significantly. Although unadjusted analyses showed lower rates of same-day and 7-day note closure with DAX, these differences were not significant after adjustment in mixed-effects logistic regression models. Providers reported improved engagement with families and narrative drafting; limitations were related to workflow alignment rather than transcription accuracy. For repeat users, DAX reduced active documentation time in shorter visits but showed limited benefit for longer encounters and did not improve timeliness of note closure. Adoption remained modest, underscoring the importance of workflow fit and usability considerations in AI-scribe deployment.

Federal healthcare legislation has played a central role in shaping the digital transformation of U.S. healthcare. Key legislative milestones-including the Health Security Act of 1993, the Health Insurance Portability and Accountability Act (HIPAA), the Health Information Technology for Economic and Clinical Health (HITECH) Act, the Patient Protection and Affordable Care Act (ACA), and the 21st Century Cures Act-collectively accelerated the adoption of electronic health records (EHRs), promoted interoperability, and expanded access to digital health tools. This study aimed to examine how federal legislation across recent administrations has shaped the digitalization of healthcare delivery, and to evaluate the challenges and consequences associated with these policies. A targeted narrative review was conducted using primary legislative texts and federal policy documents from 1993 to 2016. Findings were organized chronologically and thematically to assess the intended objectives, observed impacts, and ongoing implementation challenges associated with major healthcare legislation. Federal legislation significantly accelerated the adoption of EHR systems, expanded health information exchange, and enabled advances in patient safety, clinical quality, and population health analytics. Implementation has also introduced challenges, including high system costs, workflow disruption, documentation burden, and disparities in adoption amongst smaller practices. Emerging technologies, such as artificial intelligence, are further reshaping digital healthcare while raising new concerns regarding regulation, safety, and data governance. Federal policies have fundamentally transformed healthcare delivery by establishing the infrastructure for a digitally connected healthcare system. Despite measurable improvements in interoperability and data accessibility, persistent challenges remain. Future reforms should prioritize improved EHR design, enhanced clinician training frameworks, and policies that address adoption barriers to ensure that digital health innovations translate into safer, more effective, and equitable patient care.

Identifying patient portals (PP) activation disparities, especially in electronic health record (EHR) activation workflows, can help facilitate equitable health care access.Our study aimed to assess whether the parent/guardian's preferred language was associated with being offered, activating, and using the PP and the methods used to offer activation codes.This retrospective cohort study examined PP offer, activation, and usage rates at a large freestanding children's hospital. Patients <12 years old with ambulatory visits from July 1, 2022, to June 30, 2023, without prior active proxy PP accounts were included. The primary independent variable was the self-reported parent/guardian preferred language (English/Spanish). Outcomes included the probability of being offered, overall and by specific offer methods, activation, and usage. Zou's modified multivariate Poisson regression models examined the association between preferred language and offer/activate/use status.Among 39,578 patients, 85.1% were patients with English as preferred language (PEPL) and 14.9% had Spanish as preferred language (PSPL). PSPL had a lower probability of being offered (adjusted relative risk ratio [aRR]: 0.65, 95% confidence interval [CI]: 0.63-0.67), activated (aRR: 0.72, 95% CI: 0.70-0.75), and used (aRR: 0.68, 95% CI: 0.65-0.72) a PP compared to PEPL. Specifically, PSPL had a lower probability of activating if ever offered via instant activation (aRR: 0.72, 95% CI: 0.69-0.75), parent/guardian with existing account (aRR: 0.73, 95% CI: 0.69-0.76), and had equal probability of activating if ever offered via letter (aRR: 0.42, 95% CI: 0.19-0.94) and clinician-assisted method (aRR: 0.99, 95% CI: 0.86-1.16), compared to PEPL.PSPL at a large, free-standing pediatric health system had a lower probability of PP offer, activation, and usage than PEPL. Activation methods were not universally effective across language groups, emphasizing the need for equitable workflow optimization. This study highlights an approach to analyzing health disparities in activation workflows to inform targeted interventions to improve equitable PP access.

Ambient listening tools utilize generative artificial intelligence (AI) to create clinical notes from real-time conversations between clinicians and patients during an encounter. One of the potential benefits of ambient listening tools is an improvement in reported patient experience.This study aimed to compare the patient experience of an outpatient visit during which an ambient listening tool is used with a standard visit and to quantify any perceived improvements in care.Patients completed a targeted survey following outpatient clinic visits across all departments at a large academic institution. We conducted ordered logistic regression analyses to examine the association between ambient scribe use and patient satisfaction across six survey domains: provider communication, provider attention, perceived time spent with the provider, overall interaction with the provider, understanding of health information, and quality of the after-visit summary.Our analysis included 8,120 patients who submitted a survey following their outpatient visit in February to April 2025. Patients whose provider used an ambient scribe had higher odds of reporting satisfaction with the perceived duration of time spent with the provider (OR = 1.13, 95% CI: 1.01-1.26, p = 0.033).In this observational study, use of an ambient AI scribe was associated with a small improvement in one patient-reported satisfaction domain of perceived attention from the provider and no detectable differences across other domains assessing patient experience. These findings suggest that, in early real-world implementation, ambient AI documentation tools may be acceptable to patients and do not appear to adversely affect perceived visit quality.

BackgroundFederated network studies allow data to remain locally while the research is conducted through the sharing of analytical code and aggregated results across different health care settings and countries. A large number of databases have been mapped to the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM), boosting the use of analytical pipelines for standardized observational research within this open science framework. Transparency, reproducibility, and robustness of results have positioned federated analyses using the OMOP CDM within the European Health Data and Evidence Network (EHDEN) as an essential tool for generating large-scale evidence.ObjectivesWe conducted large-scale federated analyses involving 52 databases from 19 countries using the OMOP CDM. In thisState-of-the-Art/Best Practicearticle, we aimed to share key lessons and strategies for conducting such complex, large multidatabase analyses.ResultsMeticulous planning, establishing a strong community of collaborators, efficient communication channels, standardized analytics, and strategic division of responsibilities are essential. We highlight the benefits of network engagement, cross-fertilization of ideas, and shared learning. Further key elements contributing to the study's success included an inclusive, incremental implementation of the analytical code, timely engagement of data partners, and community webinars to discuss and interpret study findings.ConclusionWe received predominantly positive feedback from data custodians about their participation, and included input for further improvements for future large-scale federated network studies from this shared learning experience.

BackgroundMalnutrition remains a critical global health challenge among hospitalized patients, necessitating effective nutrition support systems.ObjectivesThis study aims to construct and evaluate a cross-professional, cross-sectoral nutrition health care information system (CPCS-NHIS) to optimize clinical nutrition management.MethodsThe system integrates modified medical systems to unify information flows, knowledge, and clinical roles. In addition, a 20-item across six dimensions electronic questionnaire based on the revised DeLone and McLean IS Success model was developed to evaluate the success of the CSCP-NHIS. Besides, user satisfaction was assessed as a key dimension using a 5-point Likert's scale (1 = strongly disagree to 5 = strongly agree). Finally, the results of the questionnaire were further validated by reliability and validity analysis.ResultsThe CPCS-NHIS features comprehensive functions including bedside nutritional screening, assessment, intervention, diagnosis, monitoring, enteral nutrition prescription, and QR-code autopayment. Over 80% of users expressed willingness to continue using and recommending the system. The questionnaire results demonstrated strong psychometric properties: all Cronbach's α coefficients >0.80, confirmed reliability; confirmatory factor analysis showed convergent validity (the average variance extracted >0.50, construct reliability >0.80); and model fit indices were excellent, with a chi-square value of 1.86, a root mean squared error of approximation of 0.09, a root mean square residual of 0.02, and a comparative fit index of 0.9.ConclusionThe CPCS-NHIS provides a practical framework for existing nutritional health care information systems, based on the nutrition care process and model, with robust psychometric evidence and high user acceptance.

End-of-life care (EoLC), such as advance care planning, advance directives, hospice, and palliative care consults, can improve patient quality of life and reduce costs, yet such interventions are underused. Machine learning-based prediction models show promise in identifying patients who may be candidates for EoLC based on increased risk of short-term (less than 1 year) mortality. Clinical decision support systems using these models can identify candidate patients at a time during their care when care teams can increase the provision of EoLC.Evaluate changes in the provision of EoLC with implementation of a machine learning-based mortality prediction model in an academic health center.A clinical decision support system based on a random forest machine learning mortality prediction model is described. The system was implemented in an academic health system, first in the medical intensive care unit, then house-wide. An interrupted time series analysis was performed over the 16 weeks prior to and 43 weeks after the implementations. Primary outcomes were the rates of documentation of advance directives, palliative care consultations, and do not attempt resuscitation (DNAR) orders among encounters with an alert for PRISM score over 50% (PRISM positive) compared with those without an alert (PRISM negative).Following a steep preintervention decline, the rate of advance directive documentation improved immediately after implementation. However, the implementations were not associated with improvements in any of the other primary outcomes. The model discrimination was substantially worse than that observed in model development, and after 16 months, it was withdrawn from production.A clinical decision support system based on a machine learning mortality prediction model failed to provide clinically meaningful improvements in EoLC measures. Possible causes for the failure include system-level factors, clinical decision support system design, and poor model performance.

Patient portals are increasingly used to support digital health engagement, but little is known about how caregivers used patient portals before, during, and after the coronavirus disease 2019 (COVID-19) pandemic.This study aimed to examine longitudinal changes in caregiver engagement with pediatric patient portals, focusing on logins, session duration, messaging behaviors, and provider response times across prepandemic, pandemic, and postpandemic periods.We conducted a retrospective cohort study using deidentified MyChart data from caregivers of children aged 0 through 11 who received care at four pediatric primary care clinics in the Southeastern United States between March 2018 and March 2023. Generalized linear models were used to compare portal engagement across prepandemic, pandemic, and postpandemic periods. Outcomes included login frequency, session duration, message volume, message types and recipients, and provider response times, all normalized per user per year.Among 478 caregivers, portal logins and session duration increased significantly during and postpandemic, with 16-fold increases postpandemic compared with prepandemic (p < 0.001). Message volume declined substantially during the pandemic (p < 0.001) but returned to baseline levels. Provider response times shortened during the pandemic and remained lower than prepandemic levels (p = 0.032). Messaging to primary care declined and did not recover fully, while specialty care messaging increased across all periods. Appointment and medical advice messages declined during the pandemic, with only the latter rebounding. Customer service inquiries rose significantly and remained elevated, and medication renewal messages increased markedly postpandemic.The COVID-19 pandemic initiated lasting changes in caregivers' engagement with pediatric patient portals, including deeper engagement, quicker provider responses, and shifts in messaging patterns. Findings can be used to guide and optimize caregiver-centered digital health strategies in pediatrics. Future work should explore potential provider burnout from increased portal workload, incorporate multicenter studies, and link portal use to clinical characteristics to better inform digital health interventions.

Falls among adults over 60 are a global health concern, including Australia.This study aimed to investigate temporospatial fall alert patterns-across time and location-detected by ambient fall detection sensors in three Australian aged care settings, to inform fall prevention strategies.A mixed-methods approach was used to analyze fall alert patterns and fall risks. Ambient fall detection sensors collected data from three care settings (residential aged care facilities [RACFs], retirement villages [RVs], and home dwelling communities [HDCs]; n = 31 households). Quantitative analysis involved fall alerts, temporospatial analysis by time of day and location. Qualitative insights were obtained through semistructured interviews with 14 older adults and 9 caregivers to understand fall risks.Distinct fall alert patterns emerged. In RACFs, alerts were most frequently recorded in bedrooms at night, linked to physical limitations and cognitive decline. RVs showed a more even distribution of alerts throughout the day, influenced by mobility issues, social activities, and pets affecting sensor accuracy. HDCs had the lowest fall alert rates, with nighttime alerts mainly in bedrooms, reflecting residents' physical status and strong family support. Qualitative data underscored the effect of cognitive and physical impairments in RACFs, mobility challenges, social activities, and pet influences in RVs, and shared living arrangements in HDCs.Fall alert patterns varied across RACFs, RVs, and HDCs, requiring tailored strategies. In RACFs, prevention should focus on nighttime safety with improved monitoring and bed alarms. Medication reviews are important, as many residents take medications affecting balance and cognition, increasing nighttime fall risks. In RVs, mobility programs and sensor accuracy improvements are needed to reduce false alerts from pets or daily activities. In HDCs, where alerts were fewer, more adaptable fall detection technology is needed to address the effect of shared bedrooms at night.

Appropriate Use Criteria Clinical Decision Support (AUC CDS) was legislatively mandated in the United States in 2014, and multiple CDS vendors were designated as qualified Clinical Decision Support Mechanisms by the Centers for Medicare and Medicaid Services. Little is known about the costs and benefits of these systems in real-world settings.We evaluated the effectiveness of an AUC CDS system and the time costs it imposes on clinicians at an academic medical center.Our U.S. academic medical center's enterprise data warehouse was queried for AUC CDS alert events and timestamps occurring between July 1, 2021, and June 30, 2022. We calculated the percentage of altered orders and alert-related timespans, and used these to calculate CDS positive predictive value (PPV), time costs, and the cost-benefit ratio of minutes of provider time per altered order. Based on the medical literature and expert opinion on well-performing CDS, we hypothesized a CDS PPV of 8%.Overall PPV was 1%, leading us to reject our hypothesis that our CDS was well-performing (p < 0.001). Median time costs per alert were high (12 seconds load time, 2 seconds dwell time), yielding a CDS cost-benefit ratio of 38 provider minutes per altered order.Despite using one of three market-leading AUC CDS tools, our CDS demonstrated long load times, short dwell times, and low PPV. Provider attention is not free-policymakers should consider both CDS effectiveness and costs (including time costs) when designing AUC policy.