CMAJ Open

OBJECTIVES: Population aging is a major contributor to increasing demand for emergency medical services (EMS), yet EMS workforce projections based on population data remain limited. This study projected future EMS incident volume and clinician workforce requirements in Florida from 2026 to 2035 based on historical data on EMS response records to inform workforce planning. METHODS: We conducted a retrospective, population-based secondary analysis and forecasting study using de-identified state-wide emergency EMS response records from Florida's Emergency Medical Services Tracking and Reporting System (EMSTARS) spanning January 1, 2017 through December 31, 2025. Incidents were assigned to seven age cohorts and aggregated into monthly time series. We used Seasonal Autoregressive Integrated Moving Average models with exogenous inputs (SARIMAX) to project age and cohort-specific incident volume for 2026 through 2035. Projected future incident volumes were translated into EMT and paramedic full-time equivalent (FTE) requirements using observed EMSTARS staffing configurations and target operational parameters. RESULTS: Annual EMS incidents increased from 4.10 million in 2017 to 5.22 million in 2025 and are projected to reach 7.76 million by 2035, a 48.8% increase over the 2025 baseline. By 2035, adults aged 60 and older are projected to represent 31.2% of Florida's population while accounting for 61.6% of all EMS incidents. Total estimated EMS workforce requirements are projected to increase from 9,542 FTEs in 2025 to 14,195 FTEs by 2035, requiring approximately 4,654 additional FTEs (a 48.8% increase). CONCLUSIONS: Florida's aging population is projected to drive a nearly 50% increase in EMS incident volume and associated workforce requirements over the next decade, with demand disproportionately concentrated among older adults. With a substantial concentration of adults aged 80 and older and a rapidly expanding oldest-old cohort, Florida is confronting the demographic conditions projected to emerge in other states over the next decade. The findings offer researchers and policymakers a replicable framework and a directly applicable planning reference for jurisdictions across the United States.

Introduction The COVID-19 pandemic profoundly disrupted healthcare delivery worldwide, with cancer care among the most affected services. Prior studies documented delays in referrals, reduced specialist access, and increased provider burden. However, the extent to which these experiences were reflected at the system level remains unclear. Objective To document cancer care experiences and examine whether these experiences were reflected in population-level health system indicators across Ontario, Canada. Methods We used an exploratory sequential mixed-methods design. Qualitative data were collected through focus groups and semi-structured interviews with 32 participants, including patients with cancer (n=8), caregivers (n=5), healthcare providers (n=14), and decision-makers (n=5) across two hospital settings in Ontario, Canada. Emergent themes informed the development of quantitative indicators. We then conducted a retrospective population-based analysis of linked administrative health databases for cancer patients in Ontario (n=87,786) to assess the prevalence of identified themes. Results Four themes emerged: (I) delays in diagnosis and screening; (II) disrupted access to primary care; (III) barriers to specialist and mental health services; and (IV) fragmented care for patients with multimorbidity. Quantitative findings corroborated major themes. Screening rates declined for cervical (64.8% to 57.5%) and breast cancer (64.5% to 57.2%). While in-person primary care shifted almost entirely to virtual modalities (8.5% to 95.4%), overall visit volumes remained stable. Specialist care showed uneven patterns, with increased oncology visits but declines in cardiology and mental health services. Patients with multiple comorbidities experienced the largest reductions in non-oncology specialist care. Conclusion The pandemic disrupted key components of cancer care, particularly screening, access to certain specialist services, and care for patients with complex needs. Integrating qualitative and quantitative evidence highlights areas of system vulnerability and underscores the need for coordinated, resilient cancer care capable of maintaining essential services during future crises.

Objectives People with mental health conditions experience increased rates of diagnostic errors and delays in acute treatment. While causes such as diagnostic overshadowing (misattribution of physical symptoms to mental health conditions) are well documented, less attention has been paid to the organisational and structural conditions that shape diagnostic work. This study examines how physical illness is diagnosed in patients with mental health conditions in emergency departments (EDs), with a focus on the structural conditions that enable or constrain safe diagnostic practice. Method We conducted a multi-site ethnography across three purposively selected EDs in England between April 2023 and April 2024, varying in size, population demographics, and local service configuration. Data were collected through 284 hours of non-participant observation and 20 semi-structured interviews with ED staff. Results Our analysis identified four recurring structural gaps that shaped the conditions under which physical health diagnosis took place for patients with mental health conditions: a design gap, whereby targets and physical layouts constrained diagnostic reasoning; a preparedness gap, reflecting the lack of structural support to allow staff to act on their existing knowledge and skills; a coordination gap, reflecting fragmented ownership and the challenges of joint assessment across mental and physical healthcare teams; and an expectation gap, whereby unmet need elsewhere in the system increased demand for ED services that were beyond its formal scope. These gaps made diagnostic errors and delay more likely for patients with mental health conditions seeking physical healthcare in the ED. Conclusions As new dedicated mental health EDs are introduced in England, there is an opportunity to avoid reproducing these structural gaps in new settings. Our study suggests that improving physical healthcare for patients with mental health conditions requires changes to how EDs are designed, resourced and supported, and how they connect with the wider health and care system. Keywords: mental health, diagnostic inequality, emergency departments

Background: General purpose language models such as ChatGPT are increasingly used by physicians and triage nurses during emergency triage. A recent study reported 51.6% undertriage of emergencies when patients queried ChatGPT directly (Ramaswamy et al., 2026). DR. INFO is an agentic AI based clinical assistant that retrieves over a curated clinical knowledge base, and an MTS specific retrieval configuration is available in which the system also retrieves the Manchester Triage System (MTS) textbook at inference time. The safety of these systems as a triage adjunct against a structured framework has not been characterised. Methods: We adapted the clinical scenarios published by Ramaswamy et al. and mapped them to the Manchester Triage System, yielding 39 emergency cases covering all five MTS priority levels. Each case was evaluated in two variants, one without and one with the objective clinical data block (vital signs, examination findings, and laboratory results), and permuted across two genders, giving 156 prompts per condition. Three systems were tested with and without a misleading GP referral statement prepended as an anchoring statement, giving 312 prompts per system: DR. INFO Baseline, DR. INFO with MTS retrieval, and OpenAI GPT-5.1. The primary outcome was the undertriage rate on the ordered MTS scale, tested with Fisher's exact test. Results: GPT-5.1 undertriaged 44.2% of cases (69/156; 95% CI 36.7 to 52.1), including 75.0% of Red and 73.4% of Orange presentations. Both DR. INFO configurations undertriaged 11.5% of cases (18/156; 95% CI 7.4 to 17.5; Fisher's exact p = 1.0 x 10^-10 versus GPT-5.1). GPT-5.1 produced 6 dangerous misses (3.8%), and both DR. INFO configurations produced none (p = 0.030). When the anchoring statement was prepended, GPT-5.1 undertriaged 8 of 8 Red cases, while both DR. INFO configurations continued to undertriage none. Adding objective clinical data to the input reduced undertriage in DR. INFO with MTS retrieval from 19.2% to 3.8% (p = 0.005). DR. INFO Baseline and GPT-5.1 showed no comparable change. There was no significant effect of gender. Conclusion: On this benchmark, replacing a general purpose language model with an agentic retrieval augmented system over a curated clinical knowledge base substantially reduced the undertriage and dangerous miss rates. Adding retrieval of the Manchester Triage System textbook to the agentic system was further associated with a reduced susceptibility to the anchoring statement and with an appropriate change in the assigned MTS priority when objective clinical data became available. Of the three configurations evaluated here, only DR. INFO with MTS retrieval combined a clinically conservative assignment at first contact with appropriate updating as additional clinical information arrived.

Background: Healthcare has witnessed administrative staffing roles balloon to twice the number of employed clinicians, resulting in $950 billion per year in administrative costs to deliver healthcare. Administrative workflows, like fax routing, are ripe for automation given the high human labor cost necessary to complete these tasks. Facsimile transmissions remain a key mode of communication in modern healthcare, requiring substantial manpower, and incurring significant, though not well-characterized, costs to health systems. Opportunities may exist for agentic artificial intelligence (AI) to automate this administrative task. Methods: This quality improvement study was performed in 2 phases at Duke University's Division of Cardiology, a single tertiary-care cardiac referral center. The first retrospective phase employed an observational time study design surveying manual fax routing processes at 3 representative cardiology clinics from April 1, 2024, to July 12, 2024. The second phase quantified all inbound faxes received through the division's communication hub from July 1, 2025, to December 31, 2025, and applied direct labor costs observed in the time study to quantify the economic burden of manual fax routing across the hub. Results: The observational time study (Phase 1) demonstrated fax routing processing times ranging from 4.4 to 9.4 minutes depending on fax type, with a mean processing time of 6.0 minutes per fax. On average, the ambulatory clinics received 1,694 faxes per month and spent 169.1 person-hours routing faxes. The divisional communication hub (Phase 2) received 24,420 faxes over the study period, averaging 4,070 inbound faxes and 13,341 pages of information per month. Extrapolating direct labor efforts from the time study, 407 person-hours per month were spent processing inbound faxes. For our institution, this translated to $10,663.40 in total monthly costs, roughly 2.5 full-time equivalents. Conclusion: Manual fax routing represents a substantial, measurable, and previously under-characterized operational and administrative burden. Given the significant opportunity to reduce labor time and costs, our study establishes fax routing as a high-value target for automation. Future work is needed to determine the impact of AI-automated fax routing on the time, labor, accuracy, and economics within clinical settings.

Introduction: Primary care clinicians (PCC) commonly make decisions in the context of time delay and uncertainty. Delay discounting (DD) and probability discounting (PD) are cognitive biases related to delay and uncertainty that are minimally explored in PCC. We assessed DD and PD in PCC and evaluated their association with low-value care (LVC) decision-making. Methods: We administered a survey to PCC in a Southeastern U.S health system and within the American Academy of Family Physicians networks. The survey comprised standardized psychometric assessments of DD and PD and four LVC clinical vignettes. Outcomes included DD and PD discounting rates for two monetary rewards ($100 and $10,000) and ratings of LVC likelihood (0-100). We used regression analysis with model selection to evaluate the relationship between variables. Results: 225 PCC (89% physicians, 11% advanced practice providers) participated. Heterogeneity in DD and PD rates was observed. For the $10,000 reward, ln k(DD)= -6.80, IQR:-7.60--6.10) and ln h(PD)= 1.75, IQR:1.75-2.36). The reward amount impacted DD and PD in opposing directions (i.e., lower DD/higher PD rates for $10,000 vs. $100). LVC likelihood was highest for low-value antibiotics and lowest for low-value cervical cancer screening (median 20, IQR:10-40 and 0, IQR:0-10, respectively). Model selection revealed demographic associations with LVC likelihood, but no association with DD or PD. Conclusions: Consistent with effects previously reported in non-clinicians, PCC exhibited a range of DD and PD, which ranged by reward magnitude. Neither DD nor PD predicted vignette-based LVC likelihood. Further research should investigate actual clinical practice patterns and other LVC scenarios.

Background and importance Emergency Department (ED) crowding is an increasing public health concern, with evidence of harms to patients, the public and healthcare systems. When the number of patients requiring emergency care exceeds the capacity of EDs, operational decisions must be made regarding the best place of care for patients. It is not known if there is an association between place of escalation area care (for example 'ramping' in an ambulance, or in an ED corridor) and patient outcomes. Objective(s) This study aimed to assess the association between the relative proportion of all escalation area care that a site provided in an ambulance (the Ambulance:Escalation Index) and all-cause 28-day mortality. Design A secondary analysis of a prospective cohort study. Adult patients of 16 years or older, attending EDs in England, Wales and Northern Ireland in March 2025. Intervention or exposure (if any) The Ambulance:Escalation Index, a site-level indicator of the proportion of all time in escalation area care provided in an ambulance. Outcome measures and analysis The profile of site-level ambulance use was presented descriptively. Multivariable survival analysis was used to assess the primary outcome, all-cause 28-day mortality. Main results Of 131 EDs using escalation area, 82 (62.6%) used ambulances as a place of escalation area care. There was a significant association between a site's increasing use of ambulances as escalation areas and mortality; for each 5% increase in the proportion of a site's total escalation area care delivered in ambulances, there was a 2.1% increase in the hazard of death by 28 days (HR 1.021, 95% CI 1.002-1.041, p=0.032). Conclusion Emergency Department crowding is associated with poor outcomes irrespective of where departments are forced to deliver care; however this study suggests that there is excess mortality where escalation area care is more often delivered in ambulances.

BACKGROUND: Half of positive blood cultures in pediatric emergency departments (PEDs) represent contaminants, driving unnecessary hospitalization, antibiotic exposure, and repeat visits. A clinical decision rule derived at CHU Sainte-Justine showed 99% sensitivity and 60% specificity for distinguishing bacteremia from contaminants but had not been externally validated. We sought to validate this rule in an independent pediatric cohort. METHODS: This retrospective diagnostic study spanned from January 2015 to May 2025 at a tertiary PED in Switzerland, using positive blood cultures from patients younger than 16 years. The four predictors (Gram-negative organisms or Gram-positive cocci in pairs or chains; time to positivity <17 hours; indwelling device; suspected osteoarticular infection) classified each case as low, moderate, or high risk. The primary outcome was bacteremia, adjudicated by two independent reviewers, based on organism identity and infectious disease specialist's assessment. Diagnostic accuracy was assessed with 95% CIs. RESULTS: Of 130 children enrolled (median age 3.8 years [IQR 0.9-9.9]; 61.5% male), 78 (60.0%) had true bacteremia. The rule yielded a sensitivity of 97.4% (95% CI, 91.0-99.7), specificity of 69.2% (95% CI, 54.9-81.3), positive predictive value of 82.6% (95% CI, 73.3-89.7), and negative predictive value of 94.7% (95% CI, 82.3-99.4). Both false-negatives were immunocompetent children with methicillin-susceptible Staphylococcus aureus bacteremia without indwelling devices. Among contaminants, 71% received antibiotics under usual care versus 31% classified as moderate or high risk by the rule. CONCLUSIONS: This first external validation supports the Sainte-Justine rule in a distinct pediatric population, preserving sensitivity with higher specificity. Multicenter validation is warranted before adoption.

Background: Manual review of 30-day hospital readmissions can identify actionable quality and safety problems, but it is labor-intensive. We developed and evaluated an agentic AI workflow for evidence-grounded readmission review. Materials and methods: We studied adult patients with unplanned 30-day readmission after discharge from a medicine hospitalist service at a single academic health system. An AI agent using a large language model queried a database containing notes, encounters, procedures, laboratory results, and other clinical data, and completed the same structured readmission-review rubric used by physicians. In the primary comparative evaluation, 20 randomly selected readmissions from 2025 were each reviewed by two physicians and the AI system. Blinded physician evaluators rated review quality. After rubric refinement, the AI workflow was applied to 100 recent readmissions in an exploratory expanded-cohort analysis of recurring improvement opportunities. Results: In the primary comparative evaluation, the AI classified 9/20 readmissions (45%) as preventable, compared with 19/40 physician reviews (47.5%). Blinded overall quality ratings were similar for AI and physician reviews (4.35 vs. 4.20 on a 1-5 scale; mean difference 0.15, 95% CI -0.20 to 0.48; p=0.49), as were factuality/support and usefulness/actionability ratings. No AI hallucinations were identified during factuality review. Agreement on preventability and primary readmission category was low for both AI-human and human-human comparisons. The AI system cost $0.23 per chart; physician reviewers took a median of 15 minutes, corresponding to an estimated $42.43 per chart. In the exploratory expanded-cohort analysis, AI-assisted review identified recurring vulnerabilities in post-discharge follow-up plans, incomplete inpatient workups, medication-safety transitions, and indwelling-device transitions. Conclusions: Agentic AI produced readmission reviews with similar blinded quality ratings to physician reviews in this small single-center primary comparative evaluation and supported identification of recurring quality-improvement themes in the exploratory expanded-cohort analysis. Preventability judgments remained variable among both AI and physicians, underscoring the need for human oversight and prospective evaluation before operational use.

Community-level estimates of healthcare utilization are essential for identifying inequities, allocating resources, and evaluating place-based interventions. However, in the United States, no single data source adequately captures healthcare utilization within geographically defined populations. Population-based surveys often lack sufficient geographic resolution, insurance claims represent only covered populations, and electronic health records are limited to care delivered within participating health systems. Increasingly, researchers combine these fragmented data sources, yet limited guidance exists for conducting valid population-based descriptive analyses using incomplete and overlapping data. We review the strengths and limitations of major data sources used to characterize community healthcare utilization and propose an approach for conducting population-based descriptive analyses using fragmented data. Rather than focusing on the limitations of individual data sources, our approach begins by explicitly defining the target population and the ideal observational study that would answer the research question. Available data sources are then conceptualized as incomplete or imperfect realizations of that ideal, providing a structured approach to (a) identifying sources of selection bias, missingness, and measurement error, (b) articulating required assumptions, and (c) selecting appropriate analytic strategies. We illustrate our approach using colorectal cancer screening utilization among adults residing in Brooklyn, New York during 2022. By shifting attention from individual data sources to the target community and the assumptions required for valid inference, this approach provides a practical approach for strengthening descriptive analyses of community healthcare utilization and informing place-based public health research, policy, and practice.

Purpose: Lung cancer screening (LCS) with low-dose computed tomography (LDCT) reduces lung cancer mortality, yet screening participation remains low. We evaluated whether a brief informational video nudge delivered immediately before a scheduled clinical encounter increased LCS ordering and baseline LCS completion. Patients and Methods: We conducted a randomized feasibility trial within Kaiser Permanente Colorado from March through October 2025. LCS-eligible patients with an upcoming primary care or pulmonology appointment were assigned to intervention or usual care based on birth month. Intervention patients were split into two group, a group who received the LCS informational video nudge via text message within 24 hours of an eligible appointment; and second group who received the text plus a QR code video link during appointment rooming. Outcomes included LCS orders, baseline LCS-LDCT completion, and video engagement. Multivariable logistic regression was used to evaluate factors associated with LCS ordering. Results: Among 1,093 patients, 549 were assigned to intervention and 544 to usual care. Intervention patients were more likely to receive an LCS order within 1 day of their appointment (22.6% vs 16.4%; p=.010) and any time during follow-up (32.6% vs 24.1%; p=.002). Baseline LCS-LDCT completion was 51% higher in the intervention group, although the difference was not statistically significant (8.6% vs 5.7%; p=.078). Among the intervention group, 93 individuals (17%) viewed the video, generating 114 total views, and viewers watched an average of 79% of the video. Most views (82.5%) occurred through text-message delivery rather than QR codes. Conclusion: A brief, low-burden LCS informational video delivered immediately before a clinical encounter and integrated into existing workflows significantly increased LCS ordering and was associated with higher screening completion. Timely, scalable digital nudges may provide an effective strategy for improving LCS participation. Based on the observed effectiveness, feasibility, and efficiency of the intervention, KPCO incorporated the behavioral nudge into standard clinical care in February 2026.

Objectives Emergency Department (ED) crowding is an international concern, predominantly caused by 'exit block', the lack of availability of inpatient beds for those requiring admission. The implementation of Flow Navigation Centre Plus (FNC+) services in Scotland aimed to reduce self-presentation to EDs and reduce crowding by providing remote clinical assessment for patients contacting urgent care by telephone and professional-to-professional advice on patient pathways, but their effectiveness is unknown. This study aimed to estimate the effect of board-wide implementation of FNC+ on ED attendances and long waits during the first year of FNC+ operation. Methods Controlled interrupted time series using weekly, publicly reported Public Health Scotland data. The intervention was implementation of the FNC+ in NHS Lanarkshire on 1 April 2024. Counts were summed across constituent sites and percentages derived from board totals. Co-primary outcomes were ED attendance volume and the proportions of attendances spending more than 4, 8 and 12 hours in the department. Segmented regression was fitted with contemporaneous control boards, seasonal terms, and accounted for autoregression. Results 118 pre-intervention and 52 post-intervention weeks were analysed across all 3 EDs in the implementing board. Attendances showed no detectable step change (+1.20%; 95%CIs -0.66 to +3.10) relative to the counterfactual. The estimated effect increased across follow-up, however, changing by +3.95% over 52 weeks (95% CI +0.36 to +7.67%). There was no significant step change in the proportion of attendances waiting more than 4 hours following the intervention (+1.74%; 95%CIs -0.71 to 4.20%). Some transition and structural sensitivity analyses demonstrated significant deteriorations in ED performance, and increased attendances, in the year following implementation, and none demonstrated improvements. Conclusions Board-wide implementation of a Flow Navigation Centre Plus was not associated with a step change in ED attendances or in long waits, but there is some evidence that attendances increased and long waits increased in the year following implementation. Their provision of supply-sensitive care is a possible mechanism. Additionally, given their action at the point of input, aiming to divert patients from ED attendance, it is unlikely that such services could relieve a constraint due to exit block, the availability of inpatient care for those requiring admission.

Purpose: Nonresponse to routinely collected patient-reported outcome measures (PROMs) threatens the representativeness of aggregated data. We characterized patient-, provider-, and clinic-level factors associated with PROMIS Global-10 nonresponse in routine radiation oncology care. Methods: In this retrospective cohort study, all adults seen at five Mass General Brigham radiation oncology clinics over one year were included. The primary outcome was patient-level nonresponse, defined as never completing the portal-administered Global-10 versus completing it at least once. Using iterative mixed-effects logistic regression, we modeled patient-, provider-, and clinic-level factors. Results: Among 12,214 patients, 71 providers, and five clinics, patient- and appointment-level response rates were 35.4% and 10.9%, with patient-level response ranging nearly fivefold across clinics (12.8% to 66.2%). In Model 1, male sex, lower education, not working, and recent surgery had higher odds of nonresponse, and longer time since diagnosis lower odds. After provider- and clinic-level factors were added, patient sex, education, and employment became nonsignificant, whereas recent surgery (adjusted odds ratio [aOR] 1.97) and longer time since diagnosis (aOR 0.46 for >12 months) persisted. A provider's historical collection rate was protective but attenuated at the clinic level. There, a later program launch (aOR 0.29) and higher historical collection rate (aOR 0.79) correlated with lower nonresponse, whereas academic versus community setting did not. Conclusions: Nonresponse to routinely collected PROMs is a multilevel phenomenon driven substantially by clinic-level implementation factors, not patient characteristics alone. Because response rate is only a proxy for representativeness, PROMs programs and PRO-based performance measures should prioritize representative collection over volume.

Background: Respiratory syncytial virus (RSV) remains a leading cause of hospitalization in infancy, with severe outcomes influenced by both contact patterns and passive immunity. Non-pharmaceutical interventions (NPIs) during the COVID-19 pandemic suppressed RSV circulation and reduced opportunities for maternal immune boosting, potentially altering protection among newborns. We evaluated whether incorporating time-varying maternal immunity improves the ability of an age-structured transmission model to predict post-pandemic RSV hospitalization patterns in infants. Methods: We analyzed population-based RSV hospitalizations among Ontario (Canada) infants (<1 year) from July 2, 2017 to June 25, 2024, using linked administrative databases. A deterministic compartmental model across seven age classes was calibrated against pre-pandemic data using Latin Hypercube Sampling. We compared a model incorporating time-varying contact rates alone against a specification that additionally included time-varying maternal immunity. Results: Both specifications accurately reproduced pre-pandemic seasonality and macro-level post-pandemic resurgence features. The constant maternal immunity model showed slightly better accuracy in capturing the 2021/22 peak compared to the time-varying maternal immunity specification. However, both qualitatively captured the continued near-absence of RSV and the observed peak was captured within the 95% credible intervals. While both models precisely captured the timing and overwhelming surge of admissions that occurred in 2022/23, they failed to capture the premature peak timing and magnitude in 2023/24. Conclusions: Incorporating time-varying maternal immunity did not improve model accuracy post-pandemic. While maternal protection is essential for evaluating infant immunizations, population-level contact shifts primarily shaped post-pandemic RSV seasonality, indicating that models must account for these mechanisms of RSV transmission dynamics.

Background Pediatric immunizations for Respiratory Syncytial Virus (RSV), including monoclonal antibodies for infants and vaccines for pregnant people, have become broadly available and can prevent severe RSV outcomes in infants. However, quantifying the impact of RSV immunization in prevention of severe pediatric illness at the population-level is limited by lack of RSV case surveillance data. The Massachusetts Department of Public Health (DPH) conducted a modeling analysis using routine public health surveillance data to estimate the state-level impact of new RSV immunization products on Emergency Department (ED) visits and hospitalizations in Massachusetts for highest risk pediatric groups. Methods A scenario projection tool, called R.Scenario.Vax, was utilized to simulate RSV-associated ED hospital encounters by age group in the context of newly available immunizations. ED visit and hospitalization data from the National Syndromic Surveillance Program (NSSP) during the time period 10/08/2017--10/19/2024 were analyzed, scaled to account for changes in RSV testing practices over time and missing encounter volume in historic data, and utilized to inform model fit of a "typical" RSV season. RSV immunization data from the Massachusetts Immunization Information System (MIIS) for the 2023--2024 and 2024--2025 RSV seasons informed high and moderate pediatric RSV immunization coverage scenarios and their impact was compared to a counterfactual reference scenario of no new immunizations. Median projections were quantitatively and qualitatively compared to observed 2024--2025 season data. Percent reduction in hospital encounters and encounters averted per 10,000 population were calculated for each scenario as compared to the reference. Results Projections for the youngest at-risk age groups showed significantly lower RSV-associated ED visits and hospitalizations during the 2024--2025 season for both high and moderate immunization coverage scenarios. Median projections for infants under 6 months old in the highest coverage scenario, wherein nearly all infants were immunized, showed 72.6% lower ED visits and 73.4% lower hospitalizations when compared to the reference scenario, equating to 262 ED visits and 85 hospitalizations averted per 10,000 population. Conclusions Our results support the use of modeling methods for public health insights and suggest that RSV immunizations for infant populations result in significantly lower RSV-related ED encounters in Massachusetts.

Background Decision-making in emergency services involving the allocation of scarce resources is a key challenge for large, complex organisations required to prioritise demand against multiple, often competing criteria. Emergency Medical Dispatch is a case in point, where Enhanced and Critical Care Teams (ECCTs) represent a scarce and lifesaving clinical resource. Despite its operational and system-level significance, the allocation of ECCTs remains under-researched. Methods We conducted a methodological development study using the P.A.T.H.S. framework (Participants, Artefacts, Transition Stages, Historicity, Setting). We designed and piloted this in our previous work under the 999 R.E.S.P.O.N.D. project, which examined the decision-making process for ECCT dispatch. We applied P.A.T.H.S. to 17 dispatch cases (comprising 100 decision-making episodes). We analysed five data sources: recordings of emergency calls and internal dispatch-related interactions, sequence-of-events records, policy documents, and ethnographic observations. A four-phase analysis--indexing & data mapping, transcription & coding, charting, and synthesising & outputs--was undertaken taking Interactional Sociolinguistics as the theoretical approach and methodology. Results P.A.T.H.S. enables the mapping of non-linear, multifactorial textual trajectories across human and non-human actors. The case example presented herein illustrates how information on key risk indicators (e.g. mechanism of injury) were often delayed, fragmented, or lost between the caller, call-handler, and written records. P.A.T.H.S. provides a framework and analytical tool capturing the textual trajectory of information flow, and trace how dispatch decision making unfolds. We subsequently developed a template and codebook for other researchers to further study complex decision making in multi-actoral systems using a textual trajectory approach. Conclusion This methodological development work demonstrates the potential of P.A.T.H.S. to capture and clarify complex decision-making processes. P.A.T.H.S. offers a practical and theoretically grounded framework for future research, training, and policy that addresses risk points in communication between oral and written forms among teams of actors, to support optimal deployment of scarce resources.

Abstract Purpose: Brain metastases (BM) far exceed primary CNS tumours and constitute the majority workload for neuro-oncology care providers. Currently, the cancer registries only capture synchronous BMs, which is only a small proportion of all BMs. We aim to develop and validate a natural language processing (NLP) algorithm that identifies brain metastases in radiology reports, enabling scalable surveillance of asynchronous BMs. Methods: Using population-based cancer registry data in Alberta, Canada, we identified a cancer cohort diagnosed between 2012--2019 with follow-up to 2022. All brain/head radiology reports at and post-cancer diagnosis were identified. Reports were sampled through a multi-phase approach and manually labeled for BM presence. We trained two Bio_ClinicalBERT models on the "Findings" and "Impressions" sections, respectively, and took the maximum predicted probability as the report-level prediction. Internal and external validation used reports from the Canadian provinces of Alberta, Ontario, and British Columbia. Results: The models were trained on 1,879 samples. For internal validation, 1,833 reports from 357 patients were tested. At a probability threshold of 0.4, the model achieved a sensitivity of 0.888 and precision of 0.499. The ensemble substantially outperformed single-section models, which achieved sensitivities of only 67.8% (Findings) and 74.2% (Impressions). On external validation, sensitivity was 0.918 in Ontario and 0.726 in British Columbia, demonstrating robustness across diverse data distributions. Conclusions: An NLP-based pipeline processing both Findings and Impressions sections has been developed and validated in three Canadian provinces. It meets cancer registry operational requirements and to be implemented into the surveillance workflow in Alberta and British Columbia, providing a foundation for population-level BM surveillance.

Background: Post-checkup health guidance in Japan has traditionally relied on paper-based communication and manual administrative processes. These workflows are time-consuming, prone to transcription errors, and can delay timely engagement with health guidance recipients. Objective: To assess whether replacing a paper-based workflow with an integrated digital system using Microsoft Access, robotic process automation (RPA), and web-based responses could improve administrative efficiency, operational reliability, and engagement among health guidance recipients. Methods: This single-site quality improvement initiative redesigned the existing letter-based workflow. Access served as a central interface for managing recipients and generating guidance letters. RPA (EzRobot) automated repetitive clerical and billing-related tasks. A web form accessed via a QR code enabled recipients to respond digitally. Outcomes included manual administrative handling time per case, occurrence of transcription-related errors, health guidance completion rate, and guidance duration distribution. Results: Following implementation, staff active handling time per case decreased from approximately 10 minutes to less than 1 minute (approximately 30 seconds), while automated RPA execution typically required about 4-5 minutes per case without staff input. No transcription-related errors were detected during the post-implementation observation period. Health guidance completion rates improved from 28.3% to 39.2% (chi-square test, P<0.01; R4 (FY2022) n=184, R5 (FY2023) n=536). Guidance duration distributions, calculated using the corrected method, shifted towards shorter durations: cases with >=200 days decreased from 30.5% to 20.9% and cases with >=240 days decreased from 13.6% to 8.9% (R4 n=59, R5 n=158). Conclusion: An integrated Access-RPA-Web workflow was associated with improvements in administrative efficiency and operational reliability in post-checkup health guidance while retaining human verification and exception handling. This pragmatic, non-AI-dependent approach may offer a useful model for process-level improvement in preventive care settings.

Background: Three San Francisco health system emergency departments have developed Geriatric Emergency Department (GED) models of care programs supporting and providing care for emergency department (ED) patients at risk for or living with dementia. Each system recognized: 1) the high proportion of older adult ED patients and those at risk for dementia, 2) the need to identify cognitive impairment in older adult ED patients, 3) the importance of developing approaches to connect older adult ED patients and their care partners with resources and diagnostic specialty services. Methods: We describe how each hospital adopted and implemented pragmatic GED models of care to support and improve care for ED patients at risk or living with dementia. We also report the proportion of ED encounters made by patients with dementia histories and the number of these reached by GED programs. Results: Three San Francisco hospitals (a tertiary care, critical access, and large integrated health system-community ED) independently implemented GED programs to support and enhance emergency care for patients living with dementia. Each uses screening and assessment tools to identify patients at risk for cognitive impairment. Each captures screening and assessment data to facilitate care and resources for post-discharge care, ensuring coordinated transitions and support for older adults. Programs varied by target patient population age and staff and resource allocation to support program goals. Site-specific pathways differed by location, patient populations, and support from geriatrics, emergency medicine, palliative medicine, neurology, psychiatry, pharmacy, referral processes, and/or pastoral care. Conclusions: Developing GED care interventions that facilitate care for patients at risk of or living with dementia is possible and sustainable when the pathway aligns with health system leadership goals through persistent value demonstration, communication, and promotion. Ultimately, developing and disseminating models of GED care is designed to address geriatric syndromes inclusive of dementia care through continuous quality improvement.

Background A small proportion of hospitalized patients generates a disproportionate share of inpatient admissions, bed-day utilization, and associated health expenditure globally. In Saudi Arabia, where Vision 2030 mandates measurable reductions in preventable hospitalizations and hospitals consume approximately 79% of public health expenditure, population-level evidence on inpatient frequent utilization is absent from the published literature. A key methodological limitation of existing studies is reliance on a single threshold that cannot distinguish acute high-frequency episodes from sustained multi-year hospital dependence. Methods A retrospective cross-sectional study analyzed electronic health records from three public hospitals in Jeddah - East Jeddah Hospital (EJH), King Abdul-Aziz Hospital (KAAH), and Thagher Hospital (TH) - for January 2022 to December 2024. Records from two clinical information systems (Oasis at KAAH and TH; Careware at EJH) were harmonized using an eight-stage data quality protocol applied to 258,391 raw encounters, yielding a final cohort of 82,160 unique patients and 100,685 valid inpatient visits. Three complementary definitions were applied: Frequent Utilizer (FU: >=3 admissions within any rolling 365-day window), Persistent Utilizer (PU: >=3 admissions with >=24 months between first and last), and Yearly Utilizer (YU: >=1 admission in each of 2022, 2023, and 2024). Analyses were conducted in JASP 0.95.4. Results FU prevalence was 2.96% (n=2,434), PU 0.60% (n=494), and YU 0.62% (n=507). Overlap analysis identified 177 compound utilizers (0.22%) satisfying all three criteria simultaneously, with a median of 7 admissions and 33.44 bed days - more than thirteen times the standard patient median. Compound utilizers had the youngest median age of any utilizer group (24 years), while Saudi nationality concentration rose progressively from 75.0% in standard patients to 87.6% in compound utilizers, and female predominance was highest in the persistence-defined groups (PU-only 62.9%, YU-only 63.6%). All three ANOVA models confirmed significant utilizer status x hospital interactions (all p<.001). Logistic regression confirmed age, Saudi nationality, and hospital as independent predictors across all definitions. A gender discrepancy - significant for males in FU Model 1 (OR=1.090, p=.039) but not Model 2 (p=.181) - was attributable to age confounding. Conclusions Approximately one in thirty-four inpatients meets the FU criterion in this Jeddah system, with significant between-hospital variation. The three-definition framework reveals clinically distinct utilization phenotypes invisible to any single threshold, including compound utilizers with extraordinary burden and unexpectedly young age, and persistent users entirely missed by annual-window definitions. Saudi nationality is the strongest and most consistent predictor across all definitions. Integrated clinical pathways connecting primary care and community services to hospital care, with shared accountability for quality across levels, are the recommended system response aligned with Vision 2030.