OBJECTIVE:Although early diagnosis improves long-term outcomes, patients with juvenile idiopathic arthritis (JIA) often experience prolonged, circuitous paths to diagnosis. To inform diagnostic improvement, we sought to characterize health care utilization in the year preceding diagnosis. METHODS:We identified 10,021 patients with an incident diagnosis of JIA and 20,042 age- and sex-matched healthy controls in the Merative MarketScan administrative datasets (2014-2022). Using negative binomial or hurdle models, we calculated incidence rate ratios (IRRs) comparing outpatient, inpatient, and emergency department or urgent care (ED/UC) utilization between patients with JIA and controls. RESULTS:In the year before diagnosis, patients with JIA had significantly increased health care utilization compared to controls (IRR 2.55 [95% confidence interval (CI) 2.49-2.62], P < 0.001). Accounting for sex, age, and insurance, utilization was increased across care settings: outpatient (IRR 2.56 [95% CI 2.49-2.62], P < 0.001), inpatient (IRR 2.00 [95% CI 1.48-2.71], P < 0.001), and ED/UC encounters (IRR 1.76 [95% CI 1.67-1.86], P < 0.001). The most common visits by patients with JIA preceding diagnosis were to a general practitioner (91.8%), ED/UC (47.0%), and orthopedist (22.9%). Health care utilization increased as the index date approached. In patients insured by Medicaid, ED/UC care was more frequent (odds ratio 2.05 [95% CI 1.89-2.23]) and orthopedic care less frequent (odds ratio 0.27 [95% CI 0.24-0.34]) than in patients with commercial insurance. CONCLUSION:In the year before diagnosis, children with JIA have significantly higher health care utilization compared to healthy peers. There are differences in the patterns of utilization in patients with Medicaid versus commercial insurance. There may be opportunities for earlier identification of JIA in primary care, orthopedics, and ED/UC.

Introduction: Semantic search, which retrieves documents based on conceptual similarity rather than keyword matching, offers substantial advantages for retrieval of clinical information. However, deploying semantic search across entire health systems, comprising hundreds of millions of clinical notes, presents formidable engineering, cost, and governance challenges that have prevented adoption. Methods: We deployed a semantic search system at a large children's hospital indexing 166 million clinical notes (484 million vectors) from 1.68 million patients. The system uses instruction-tuned qwen3-embedding-0.6B embeddings, stores vectors in a managed database with storage-optimized indexing, maintains full-text metadata in a low-latency key-value store, and operates within a HIPAA-compliant governance framework. We evaluated the system through three experiments: optimization of embedding model and chunking strategy using a physician-authored benchmark dataset, characterization of full-scale performance (cost, latency, retrieval quality), and clinical utility assessment via comparison of chart abstraction efficiency across three tasks. Results: The system delivers sub-second query latency (median 237 ms single-user, 451 ms 20-user concurrency) with monthly costs of approximately USD 4,000. Qwen3 embeddings with 300-token chunk size achieved 94.6

OBJECTIVES:Early diagnosis of juvenile idiopathic arthritis (JIA) improves long-term outcomes. The study aims to assess patient reported time to diagnosis with JIA and signs of disease-related damage at the time of diagnosis. METHODS:Retrospective cohort study of patients with an incident JIA diagnosis at an academic center over a 2-year period. Patient reported time to diagnosis and signs of disease-related damage were extracted from the electronic medical record. Factors associated with time to diagnosis were evaluated with regression modeling. RESULTS:Of the 237 children diagnosed during the study period, the median patient reported time to diagnosis was 19 weeks (IQR: 8-40, range: 1-311). Time to diagnosis was >1 year in 23.5 % of patients, and >2 years in 11.7 %. In the linear regression model, older age was associated with longer time to diagnosis. Many patients (40.9 %) had at least one sign of damage. Damage was most common in younger children and children with oligoarticular disease. CONCLUSIONS:It is common for patients with JIA to have a prolonged time to diagnosis, and many have damage at diagnosis. A regression model fails to explain most of the variance in the time to diagnosis, suggesting there is much to learn about the drivers of diagnostic delay in JIA.

BACKGROUND:Improving paediatric diagnosis by addressing errors (Safety-I) is insufficient for diagnostic excellence. Resilience engineering's approach to understanding adaptations to achieve success in everyday work may elucidate how individuals and systems manage diagnostic complexity to achieve diagnostic success (Safety-II). Our objective was to characterise resilience-promoting behaviours of patients/families and clinicians and resilient organisational properties in outpatient diagnosis of children with medical complexity. METHODS:This ethnographic study included observations of outpatient visits and semistructured interviews of parents/guardians and providers. Resilience engineering frameworks informed data collection and analysis. We prospectively sampled patients <18 years old with medical complexity presenting with acute problems in clinics at three academic health systems from 25 January 2024 to 26 February 2025. We qualitatively coded field notes and interview transcripts, focusing on individual resilient behaviours and instances of organisational resilience. We identified resilience themes using directed content analysis and described the frequency and types of resilience concepts. RESULTS:We observed 258 unique resilience-promoting behaviours and organisational properties in 31 of 33 (94%) patient visits, with an average of 7.8 resilient behaviours/properties per visit. Of these, 178 (69%) reflected patient/family and clinician activities to promote diagnostic resilience and 80 (31%) reflected organisational resilience relevant to diagnosis. The most frequently identified resilience behaviours/properties were anticipation (prediction of future events and contingency planning), monitoring (vigilant observation for clinical evolution), robustness (comprehensiveness and redundancy in the diagnostic process) and graceful extensibility (stretching resources to meet diagnostic needs). CONCLUSIONS:Resilient behaviours by patients/families and clinicians and resilient organisational properties supporting diagnostic excellence can be prevalent in outpatient paediatrics for children with medical complexity. Future research should characterise diagnostic resilience's impact on diagnostic safety and patient/family outcomes.

Background Efforts to improve diagnosis should reflect the unique needs of children; however, there is no consensus on paediatric-specific priority areas. Methods An expert panel of 25 United States (US) paediatric diagnostic excellence researchers, patient safety leaders and family partners from 15 institutions participated in a modified Delphi panel. Panellists participated in generating a literature-derived list of topics relevant to improving diagnosis. Panellists then rated agreement on each topic as a research or operational improvement priority. Topics that achieved consensus (≥75% of panellists rating them as high priority) were subsequently rated based on feasibility for implementation at the respondent’s institution. Results Literature review identified 24 topics relevant to paediatric diagnostic safety and quality, which were expanded to 62 topics during the initial panel discussion and grouped into 25 survey topics. Consensus was reached on 6 topics as research priorities and 11 as operational improvement priorities. Of these, three were deemed highly feasible for research and three for operational improvement. Research priorities included: identifying paediatric conditions at high risk for diagnostic error, developing communication methods to enhance the diagnostic process and establishing diagnostic process feedback mechanisms. Operational improvement priorities included: identifying care delivery or health services scenarios at high risk for diagnostic error, establishing interdisciplinary, structured review of cases of diagnostic error and encouraging reporting of missed opportunities for improving diagnostic safety. Conclusion Experts successfully prioritised important and feasible topics in research and practice for improving paediatric diagnosis in US academic medical centres.

BACKGROUND:Clinical deterioration with late escalation, termed emergency transfer (ET), is a key safety metric. However, national benchmarking is limited by variable definitions. We sought to estimate pooled ET rates and assess variation across pediatric hospitals. METHODS:We performed a cross-sectional analysis of ET events (2020-2022) at 7 pediatric tertiary care hospitals. ET was defined as unplanned transfer to the pediatric intensive care unit with intubation, vasopressors, and/or at least 60 mL/kg fluid resuscitation within 1 hour before or after transfer. We calculated ET rates per 10 000 patient-days and as a percentage of total unplanned transfers. RESULTS:Across 1 582 794 patient-days and 11 100 unplanned transfers, we identified 448 ET events. The most common qualifying intervention categories were endotracheal intubation in 236 ETs (53%) and vasopressor initiation in 204 ETs (46%). The pooled ET rate was 2.83 per 10 000 patient-days (95% CI, 2.57-3.11), with significant variation across sites (range, 1.2-5.1; P < .01). The pooled proportion of unplanned transfers meeting ET criteria was 4.04% (95% CI, 3.68%-4.42%), which also varied significantly (P < .01). Variation persisted when restricted to sites already institutionally tracking ETs. Metric definitions varied most regarding fluid thresholds and which medications were included in vasoactive infusion definitions. CONCLUSION:ET rates vary significantly across pediatric hospitals regardless of the denominator used. Variation may be related to the observed differences in ET metric definitions (eg, fluid thresholds) and hospital characteristics (eg, intensive care unit criteria). These pooled estimates provide initial benchmarks, and standardization of the ET metric definition is required to enable reliable interinstitutional comparison.

BACKGROUND:Skeletal surveys (SS), a critical component of non-accidental trauma (NAT) evaluations, may have indeterminate findings concerning for a possible fracture. The frequency and clinical impact of these indeterminate fractures is unknown. OBJECTIVES:In children with SS obtained as part of NAT evaluations, 1) determine the prevalence and clinical characteristics of indeterminate fractures, 2) describe frequency and type of follow-up imaging, and 3) ascertain the potential impact of indeterminate fractures. PARTICIPANTS AND SETTING:A single-center, retrospective chart review of children <2 years who underwent an initial SS to evaluate for NAT, between January 2020 and March 2024. METHODS:Descriptive statistics were used to calculate the frequency of indeterminate fractures, clinical characteristics, follow up imaging, and impact. RESULTS:Of 1198 initial SSs, 153 (13%) had at least 1 indeterminate fracture. These 153 SSs identified a total of 254 indeterminate fractures (median = 1, IQR 1-2), frequently classic metaphyseal lesions (25%, 64/254) or rib fractures (25%, 64/254). Most patients (93%, 143/153) underwent at least one clarifying imaging study; more than half (80, 52%) required >24 h for uncertainty resolution. A minority of indeterminate fractures were ultimately confirmed as true fractures (24%, 60/254). Seventy percent (107/153) of patients had concurrent, definitive injury and 55% (84/153) were referred to child protective services. CONCLUSIONS:One in eight SSs identified at least one indeterminate fracture, often a fracture type highly specific for NAT. However, most were not true fractures, suggesting opportunities for improvement in SS workflow given the diagnostic implications.

The field of diagnostic excellence has advanced considerably in the past decade, reframing diagnosis as a patient safety priority and highlighting the prevalence and harms of diagnostic error. Foundational evidence now supports the development of Diagnostic Excellence Programs; organizational initiatives designed to reduce diagnostic errors and improve system-level and individual performance. While early studies established the epidemiology of diagnostic error across inpatient, emergency, and ambulatory care, newer approaches emphasize continuous, systematic surveillance to inform targeted improvements. Emerging frameworks, such as the DEER Taxonomy and root cause or success cause analyses, help classify drivers of both failures and successes in diagnostic processes. Effective programs must address system factors, including electronic health record design, workload, team structures, and communication, while also enhancing individual clinician performance through feedback, diagnostic reflection, cross-checks, and coaching. Patient engagement represents a critical but underdeveloped dimension; strategies such as structured communication frameworks, patient-family advisory councils, and electronic tools co-designed with patients aim to foster shared diagnostic decision-making and improve transparency. Artificial intelligence (AI) holds promise to accelerate measurement, streamline clinical workflows, reduce cognitive load, and support communication, though careful implementation and oversight are required to ensure safety. Ultimately, Diagnostic Excellence Programs will succeed by embedding diagnostic safety into institutional standards of care, providing clinicians with ongoing, psychologically safe opportunities for recalibration, and leveraging AI to scale surveillance and improvement activities.

Objectives During the SARS-CoV-2 pandemic, new patient evaluations in pediatric rheumatology were performed using telehealth. Given the pediatric rheumatology workforce shortage, telehealth may be a way to efficiently triage referrals. The objective was to assess the utility of telehealth visits as a diagnostic tool to accurately assess the need for in-person evaluation. Methods This was a retrospective cohort study of patients evaluated by telehealth for a new patient visit from March 1 to June 30, 2020 at a tertiary center. Electronic health record documentation from subsequent rheumatology, specialty, and primary care encounters over the subsequent 4 years were reviewed. The primary outcome was diagnostic concordance, defined as consistency in the documented diagnostic reasoning, between the initial telehealth video visit and in-person follow-up visits. Results During the study period, there were 111 telehealth visits, 80 (72 %) of which had follow-up data. 55/80 had in-person rheumatology evaluations. Only 9 % patients had discordant diagnoses, all of whom had initial concern for inflammatory arthritis during the telehealth visit but a diagnosis of a non-inflammatory condition after in-person evaluation. Nine patients with a significant rheumatic disease were identified via telehealth. There were no unplanned ED visits or hospital admissions following telehealth visits. 33 % of patients were found to not warrant rheumatologic follow-up after the telehealth visit. Conclusions For pediatric rheumatology new patient evaluations, diagnostic accuracy via telehealth evaluation was high. Providers triaged patients with chronic rheumatologic conditions for in-person evaluations and were able to accurately identify benign conditions that did not require in-person follow-up.

Background: Continuous physiologic monitoring commonly is used in pediatric medical-surgical (med-surg) units and is associated with high alarm burden for clinicians. Characteristics of pediatric patients generating high rates of alarms on med-surg units are not known. Objective: To describe the demographic and clinical characteristics of pediatric med-surg patients associated with high rates of clinical alarms. Methods: We conducted a cross-sectional, single-site, retrospective study using existing clinical and alarm data from a children's hospital. Continuously monitored patients from med-surg units who had available alarm data were included. Negative binomial regression models were used to test the association between patient characteristics and the rate of clinical alarms per continuously monitored hour. Results: Our final sample consisted of 1,569 patients with a total of 38,501 continuously monitored hours generating 265,432 clinical alarms. Peripheral oxygen saturation (SpO2) low alarms accounted for 57.5% of alarms. Patients with medical complexity averaged 11% fewer alarms per hour than those without medical complexity (P < 0.01). Patients older than 5 years had up to 30% fewer alarms per hour than those who were younger than 5 years (P < 0.01). Patients using supplemental oxygen averaged 39% more alarms per hour compared with patients who had no supplemental oxygen use (P < 0.01). Patients at high risk for deterioration averaged 19% more alarms per hour than patients who were not high risk (P = 0.01). Conclusion: SpO2 alarms were the most common type of alarm in this study. The results highlight patient populations in pediatric medical-surgical units that may be high yield for interventions to reduce alarms. Most physiologic monitor alarms in pediatric medical-surgical (med-surg) units are not informative and likely could be safely eliminated to reduce noise and alarm fatigue.1-3 However, identifying and sustaining successful alarm-reduction strategies is a challenge. Research shows that 25% of patients in pediatric med-surg units produce almost three-quarters of all alarms.4 These patients are a potential high-yield target for alarm-reduction strategies; however, we are not aware of studies describing characteristics of pediatric patients generating high rates of alarms. The patient populations seen on pediatric med-surg units are diverse. Children of all ages are cared for on these units, with diagnoses ranging from acute respiratory infections, to management of chronic conditions, and to psychiatric conditions. Not all patients on pediatric med-surg units have physiologic parameters continuously monitored,4 but among those who do, understanding patient characteristics associated with high rates of alarms may help clinicians, healthcare technology management (HTM) professionals, and others working on alarm management strategies to develop targeted interventions. We conducted an exploratory retrospective study to describe patient characteristics associated with high rates of alarms in pediatric med-surg units.

Communication underlies every stage of the diagnostic process. The Dialog Study aims to characterize the pediatric diagnostic journey, focusing on communication as a source of resilience, in order to ultimately develop and test the efficacy of a structured patient-centered communication intervention in improving outpatient diagnostic safety. In this manuscript, we will describe protocols, data collection instruments, methods, analytic approaches, and theoretical frameworks to be used in to characterize the patient journey in the Dialog Study. Our approach to characterization of the patient journey will attend to patient and structural factors, like race and racism, and language and language access, before developing interventions. Our mixed-methods approach is informed by the Systems Engineering Initiative for Patient Safety (SEIPS) 3.0 framework (which describes the sociotechnical system underpinning diagnoses within the broader context of multiple interactions with different care settings over time) and the Safety II framework (which seeks to understand successful and unsuccessful adaptations to ongoing changes in demand and capacity within the healthcare system). We will assess the validity of different methods to detect diagnostic errors along the diagnostic journey. In doing so, we will emphasize the importance of viewing the diagnostic process as the product of communications situated in systems-of-work that are constantly adapting to everyday challenges.

Background: Diagnostic excellence is central to healthcare quality and safety. Prior literature identified a lack of psychological safety and time as barriers to diagnostic reasoning education. We performed a needs assessment to inform the development of diagnostic safety education. Methods: To evaluate existing educational programming and identify opportunities for content delivery, surveys were emailed to 155 interprofessional educational leaders and 627 clinicians at our hospital. Educational leaders and learners were invited to participate in focus groups to further explore beliefs, perceptions, and recommendations about diagnostic reasoning. The study team analyzed data using directed content analysis to identify themes. Results: Of the 57 education leaders who responded to our survey, only 2 (5%) reported having formal training on diagnostic reasoning in their respective departments. The learner survey had a response rate of 47% (293/627). Learners expressed discomfort discussing diagnostic uncertainty and preferred case-based discussions and bedside learning as avenues for learning about the topic. Focus groups, including 7 educators and 16 learners, identified the following as necessary precursors to effective teaching about diagnostic safety: (1) faculty development, (2) institutional culture change, and (3) improved reporting of missed diagnoses. Participants preferred mandatory sessions integrated into existing educational programs. Conclusions: Our needs assessment identified a broad interest in education regarding medical diagnosis and potential barriers to implementation. Respondents highlighted the need to develop communication skills regarding diagnostic errors and uncertainty across professions and care areas. Study findings informed a pilot diagnostic reasoning curriculum for faculty and trainees.

BACKGROUND:Scientific writing is a core component of academic hospital medicine, and yet finding time to engage in deeply focused writing is difficult in part due to the highly clinical, 24/7 nature of the specialty that can limit opportunities for writing-focused collaboration and mentorship. OBJECTIVE:Our objective was to develop and evaluate an academic writing retreat program. METHODS:We drafted a set of key retreat features to guide implementation of a 3-day, 2-night retreat program held within a 2 h radius of our hospital. Agendas included writing blocks ranging from 45 to 90 min interspersed with breaks and opportunities for feedback, exercise, and preparing meals together. After each retreat, we distributed an evaluation with multiple choice and free text response options to characterize retreat helpfulness and later gathered data on the status of each paper and grant worked on. RESULTS:We held 4 retreats between September 2022 and October 2023, engaging 18 faculty and fellows at a cost of $296 per attendee per retreat. In evaluations, nearly 80% reported that the retreat was extremely helpful, and comments praised the highly mentored environment, enriching community of colleagues, and release from commitments that get in the way of writing. Of the 24 papers attendees worked on, 12 have been accepted and 6 are under review. Of the 4 grant proposals, 2 are under review. CONCLUSIONS:We implemented a low-cost, productive writing retreat program that attendees reported was helpful in supporting deep work and represented a meaningful step toward building a community centered around academic writing.

Juvenile idiopathic arthritis (JIA) is the most common rheumatic disease of childhood and a disease for which we have safe and effective therapies. Early diagnosis of JIA enables timely initiation of therapy and improves long-term disease outcomes. However, many patients with JIA experience prolonged diagnostic delays and have a turbulent course to diagnosis. In this narrative review, we explore the importance of early diagnosis in JIA, what is known about time to diagnosis and diagnostic trajectory, and factors that contribute to delayed diagnosis. We also discuss next steps to improve time to diagnosis for these vulnerable patients.

Importance:Subspecialty consultation is a frequent, consequential practice in the pediatric inpatient setting. Little is known about factors affecting consultation practices. Objectives:To identify patient, physician, admission, and systems characteristics that are independently associated with subspecialty consultation among pediatric hospitalists at the patient-day level and to describe variation in consultation utilization among pediatric hospitalist physicians. Design, Setting, and Participants:This retrospective cohort study of hospitalized children used electronic health record data from October 1, 2015, through December 31, 2020, combined with a cross-sectional physician survey completed between March 3 and April 11, 2021. The study was conducted at a freestanding quaternary children's hospital. Physician survey participants were active pediatric hospitalists. The patient cohort included children hospitalized with 1 of 15 common conditions, excluding patients with complex chronic conditions, intensive care unit stay, or 30-day readmission for the same condition. Data were analyzed from June 2021 to January 2023. Exposures:Patient (sex, age, race and ethnicity), admission (condition, insurance, year), physician (experience, anxiety due to uncertainty, gender), and systems (hospitalization day, day of week, inpatient team, and prior consultation) characteristics. Main Outcomes and Measures:The primary outcome was receipt of inpatient consultation on each patient-day. Risk-adjusted consultation rates, expressed as number of patient-days consulting per 100, were compared between physicians. Results:We evaluated 15 922 patient-days attributed to 92 surveyed physicians (68 [74%] women; 74 [80%] with ≥3 years' attending experience) caring for 7283 unique patients (3955 [54%] male patients; 3450 [47%] non-Hispanic Black and 2174 [30%] non-Hispanic White patients; median [IQR] age, 2.5 ([0.9-6.5] years). Odds of consultation were higher among patients with private insurance compared with those with Medicaid (adjusted odds ratio [aOR], 1.19 [95% CI, 1.01-1.42]; P = .04) and physicians with 0 to 2 years of experience vs those with 3 to 10 years of experience (aOR, 1.42 [95% CI, 1.08-1.88]; P = .01). Hospitalist anxiety due to uncertainty was not associated with consultation. Among patient-days with at least 1 consultation, non-Hispanic White race and ethnicity was associated with higher odds of multiple consultations vs non-Hispanic Black race and ethnicity (aOR, 2.23 [95% CI, 1.20-4.13]; P = .01). Risk-adjusted physician consultation rates were 2.1 times higher in the top quartile of consultation use (mean [SD], 9.8 [2.0] patient-days consulting per 100) compared with the bottom quartile (mean [SD], 4.7 [0.8] patient-days consulting per 100; P < .001). Conclusions and Relevance:In this cohort study, consultation use varied widely and was associated with patient, physician, and systems factors. These findings offer specific targets for improving value and equity in pediatric inpatient consultation.

OBJECTIVE:Evaluate the positive predictive value of International Classification of Disease, 10th Revision, Clinical Modification (ICD-10-CM) codes in identifying young children diagnosed with physical abuse.METHODS:We extracted 230 charts of children <24 months of age who had any emergency department, inpatient, or ambulatory care encounters between Oct 1, 2015 and Sept 30, 2020 coded using ICD-10-CM codes suggestive of physical abuse. Electronic health records were reviewed to determine if physical abuse was considered during the medical encounter and assess the level of diagnostic certainty for physical abuse. Positive predictive value of each ICD-10-CM code was assessed.RESULTS:Of 230 charts with ICD-10 codes concerning for physical abuse, 209 (91%) had documentation that a diagnosis of physical abuse was considered during an encounter. The majority of cases, 138 (60%), were rated as definitely or likely abuse, 36 cases (16%) were indeterminate, and 35 (15%) were likely or definitely accidental injury. Other forms of suspected maltreatment were discussed in 16 (7%) charts and 5 (2%) had no documented concerns for child maltreatment. The positive predictive values of the specific ICD-10 codes for encounters rated as definitely or likely abuse varied considerably, ranging from 0.89 (0.80-0.99) for T74.12 "Adult and child abuse, neglect, and other maltreatment, confirmed" to 0.24 (95% CI: 0.06-0.42) for Z04.72 "Encounter for examination and observation following alleged child physical abuse."CONCLUSIONS:ICD-10-CM codes identify young children who experience physical abuse, but certain codes have a higher positive predictive value than others.

Alarm fatigue (and resultant alarm nonresponse) threatens the safety of hospitalized patients. Historically threats to patient safety, including alarm fatigue, have been evaluated using a Safety I perspective analyzing rare events such as failure to respond to patients' critical alarms. Safety II approaches call for learning from the everyday adaptations clinicians make to keep patients safe. To identify such sources of resilience in alarm systems, we conducted 59 in situ simulations of a critical hypoxemic-event alarm in medical/surgical and intensive care units at a tertiary care pediatric hospital between December 2019 and May 2022. Response timing, observations of the environment, and postsimulation debrief interviews were captured. Four primary means of successful alarm responses were mapped to domains of Systems Engineering Initiative for Patient Safety framework to inform alarm system design and improvement.