Background: Delays in the diagnostic process, particularly for chronic conditions such as Type 2 Diabetes (T2D), can lead to suboptimal treatment and poor health outcomes. Considering diagnosis as a process comprised of sequential tasks, clinical observations, and decisions, visualizing the sequence of events and decisions within the diagnostic process can help identify barriers to timely diagnosis and develop solutions to prevent diagnostic delay. Objective: In this study, our objective was to visualize and analyze sequence of events and decisions made by providers and non-provider clinical staff when establishing a new T2D diagnosis in primary care. Methods: Semi-structured interviews were conducted with primary care providers and non-provider clinical staff to explore their experiences in diagnosing, managing, and communicating with T2D patients. Interview transcripts were used to generate individual process maps, which were synthesized by integrating common elements across participants. A high-level map of the entire diagnostic process was developed by combining synthesized maps from provider and non-provider groups and identifying major subprocesses. Results: A total of 20 participants, including 10 healthcare providers and 10 non-provider clinical staff, were interviewed to develop individual and synthesized process maps of the T2D diagnostic process. The final process map illustrates a complex workflow comprising multiple interdependent subprocesses and decision points influencing clinicians’ workflow when gathering, integrating, and interpreting information, as well as forming a working diagnosis and communicating it to patients. Key bottlenecks included incomplete laboratory testing, patients’ lack of awareness or engagement with the diagnosis, and missed follow-up appointments. Conclusions: This study provides a detailed visualization of the T2D diagnostic process from multiple provider and clinical staff perspectives. Future work will involve eliciting quantities related to diagnostic delay from domain experts to refine and validate the process map findings and develop targeted clinician-facing decision-support solutions.

Delayed diagnosis of type 2 diabetes (T2D) increases the risk of diabetes-related health complications. Although signs of T2D are commonly identified in primary care, delays in diagnosis remain a significant challenge. The relationship between patient-level factors (e.g., demographics and healthcare utilization patterns), clinic-specific factors (e.g., primary care location), and the time to T2D diagnosis represents a critical, yet understudied, research area. We conducted a retrospective observational cohort study of 594 adults who received care from two primary care clinics within an integrated healthcare system in the mid-Atlantic region of the United States (2017–2023). Kaplan–Meier survival analysis and time-varying Cox proportional hazards models quantified the time from the first diabetes-range hemoglobin A1c (HgA1c ≥ 6.5

Introduction: Disparities in diabetes technology use and glycemic outcomes among US youth with type 1 diabetes (T1D) are strongly associated with race/ethnicity and insurance. The Social Deprivation Index (SDI) offers a multidimensional measure of area-level socioeconomic disadvantage linked to poorer health outcomes in pediatric populations. Methods: We conducted a retrospective cohort study of 1,541 youth aged <19 years who were newly diagnosed with T1D between 2018 and 2022 at a single tertiary care center. SDI was calculated from address and categorized into quintiles (Q1 least deprived). Primary outcomes included time to continuous glucose monitor (CGM) and insulin pump initiation and hemoglobin A1c (A1c) over 12 months. Differences by SDI quintile were assessed using interval-censored Cox proportional hazards and linear mixed-effects models. Results: Within 1 year, 84% initiated CGM and 50% initiated pump therapy. Time to CGM initiation increased across SDI quintiles; patients in Q3-Q5 were significantly less likely to initiate CGM than Q1 (Q3: HR, 95% CI: 0.81, 0.66-0.99, p = 0.036; Q4: 0.81, 0.67-0.98, p = 0.027; Q5: 0.69, 0.56-0.86; p = 0.001). Hispanic, non-Hispanic black, and Medicaid-insured patients had lower CGM uptake. Pump initiation was significantly lower only in Q5. Among CGM users, Q5 had higher A1c than Q1 (difference 0.80%, p = 0.001). Among non-pump users, Q4 and Q5 had higher A1c than Q1. AID users had lower A1c than pump-only users (7.0% vs. 7.2%, p = 0.003). Conclusion: A1c is lower across all SDI levels with CGM use, but disparities persist. Addressing structural barriers is essential to achieving equity.

Chronic disease management relies on regular patient-provider interactions to follow-up on disease progression and control. For Type 2 Diabetes (T2D), current guidelines prescribe fixed time intervals between subsequent primary care visits for all patients, overlooking heterogeneity in clinical trajectories and patient characteristics. This study introduces a Contextual Markov Decision Process (CMDP) model to optimize subpopulation-specific follow-up interval decisions using Electronic Health Record (EHR) data from 22,154 T2D patients across 10 primary care clinics. Contexts are identified by: i) dimensionality reduction of variables representing the individual health trajectories utilizing Principal Component Analysis, and ii) assigning patients to contexts via principal components and additional patient-level features using clustering. Two distinct contexts emerged, representing a lower- and a higher-risk subpopulation. CMDP-derived policies recommend: (i) follow-up within 1 month if lab value at current visit is unmeasured; (ii) up to 3 months for elevated lab values or recent hospitalizations; and (iii) 6 to 12 months for sustained glycemic control, with shorter follow-up intervals for patients in high-risk context. The optimal policies achieved lower expected cumulative cost than benchmarks (e.g., in the higher-comorbidity context, the CMDP policy reduced cost by about 34.8

Patients diagnosed with type 2 diabetes (T2D) are at increased risk of developing cardiovascular disease (CVD), the leading cause of morbidity and mortality in this population. Early detection and glycemic control within the first year after diagnosis reduce CVD risk. However, gaps remain in how to operationalize early detection of T2D using Electronic Health Record (EHR) data and quantify its relationship with subsequent CVD risk using longitudinal observations. We developed a probabilistic graph model to analyze the interdependencies between early detection of T2D, post-diagnosis glycemic control, and CVD occurrence. Using a temporally structured Bayesian Network (BN) learned from EHR data of 9,450 primary care patients between 2017 and 2023, we quantified probabilistic dependencies between demographics, diagnostic delay surrogates, glycemic control, and post-diagnosis CVD occurrence. Percentile based thresholds defined risk groups, where individuals with predicted probabilities in the bottom decile (≤ 10th percentile) were classified as low risk, and those in the top decile (≥ 90th percentile) as high risk. Results demonstrated heterogeneity in predicted risks across glycemic and cardiovascular outcomes. Predicted probability of developing CVD within the first year after T2D diagnosis ranged from a mean of 5.2% in the low-risk group to 28.9% in the high-risk group, while predicted probabilities of mean Hemoglobin A1c (HbA1c) ≥ 8% during the first year post-diagnosis ranged from 1.6% in low-risk to 55.1% in high-risk group. Patients with HbA1c at diagnosis ≥ 8% had higher predicted probabilities of first-year post-diagnosis mean HbA1c ≥ 8% (53.3% vs. 1.9%) and high HbA1c coefficient of variation (18.7% vs. 3.1%) compared with those with HbA1c ≤ 6.5%. Incorporating early clinical outcomes refined later risk predictions, with long-term CVD risk reaching 33.5% among high-risk individuals. The proposed model achieved predictive performance comparable to conventional machine learning approaches while providing interpretable relationships for risk stratification in primary care populations. ### Competing Interest Statement Muge Capan, Kristen Miller, William J. Gallagher and Yukti Kathuria report financial support was provided by Agency for Healthcare Research and Quality. ### Funding Statement Yes ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: MedStar IRB ID: MOD0017487 Approved on: April 5, 2024 UMass IRB ID: 5153 Approved on July 25, 2024 I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable. Yes The minimal dataset and accompanying code are publicly available on Synapse (Sage Bionetworks) at https://www.synapse.org/Synapse:syn75373876/wiki/641088.

Precursor B-cell acute lymphoblastic leukemia (B-ALL) is the most common childhood malignancy. Relapsed or refractory (R/R) B-ALL carries a dismal prognosis.1-3 CD19-specific CAR T cell therapy has emerged as a promising treatment, yet relapse rates among responders remain high, with 40-50% relapsing post-therapy (4-12). This underscores the need for improved strategies to predict and prevent relapse following CAR T cell therapy. This study reports the impact of peripheral blasts at the time of apheresis on outcomes in pediatric and young adult patients with R/R B-ALL treated with CD19-specific CAR T cell therapy. A multi-institutional cohort of 162 patients from the Pediatric Real-World CAR Consortium was retrospectively analyzed. Key outcomes such as day 28 response, event-free survival (EFS), and overall survival (OS) were evaluated based on the presence versus absence of peripheral blasts at apheresis. While response at day 28 was comparable among peripheral blast groups in this cohort, the presence of peripheral blasts at apheresis was associated with inferior EFS (27% vs. 55% at 12 months) and OS (55% vs. 78% at 12 months). However, this association was confounded by disease burden at the time of infusion. In multivariable analysis, higher blast percentage at apheresis was not associated with an increased hazard of death (HR = 1.11, 95% CI: 0.93 - 1.33, p = 0.25). These findings suggest that the association between peripheral blasts at apheresis and inferior survival is largely influenced by disease burden at infusion, highlighting the need for further investigation into the biological and clinical significance of peripheral blasts during CAR T cell manufacturing.

(Abstracted from Am J Obstet Gynecol 2024:S0002-9378(24)00693-8) Amniocentesis, a standard method for obtaining fetal samples for genetic testing since the 1970s, is typically performed between 15 and 22 weeks of gestation. While useful for detecting chromosomal or single-gene fetal disorders, it carries low-incidence risks such as pregnancy loss, preterm prelabor rupture of membranes, chorioamnionitis, and fetal needle injury.

OBJECTIVES:To describe frequency of, and risk factors, for change in caregiver employment among critically ill children with acute respiratory failure. DESIGN:Preplanned secondary analysis of prospective cohort dataset, 2018-2021. SETTING:Quaternary Children's Hospital PICU. PATIENTS:Children who required greater than or equal to 3 days of invasive ventilation, survived hospitalization, and completed greater than or equal to 1 post-discharge survey. INTERVENTIONS:None. MEASUREMENTS AND MAIN RESULTS:We measured change in caregiver employment 1 and 12 months post-discharge relative to pre-admission and, when present, change in caregiver identity defined by relationship to the patient. Data were collected by survey. We used logistic regression to identify factors associated with these changes. We evaluated 130 children, median age 6.4 years (interquartile range, 1.10-13.3 yr), 40 (30.8%) with a complex chronic condition (CCC), and 99 (76.2%) with normal pre-illness Functional Status Scale scores. Of 123 with 1-month post-discharge data, 25 of 123 (20.3%) experienced a change in caregiver employment and an additional 14 of 123 (11.4%) had a change in caregiver(s). Of 115 with 12-month post-discharge data, 33 of 115 (28.7%) experienced a change in caregiver employment and an additional 16 of 115 (13.9%) had a change in caregiver(s). After controlling for age, CCC, baseline caregiver employment, new morbidity at discharge, and social and economic index; higher maximum Pediatric Logistic Organ Dysfunction-2 score (odds ratio [OR], 1.19 [95% CI, 1.01-1.41]) and government insurance (OR, 3.85 [95% CI, 1.33-11.11]) were associated with the composite outcome of change in caregiver employment or caregiver(s) at 1-month post-discharge. CONCLUSIONS:At 1 and 12 months post-discharge, more than one-in-five children who survived greater than or equal to 3 days of invasive ventilation had a change in caregiver employment and one-in-ten had a change in caregiver(s). Identification of risk factors, such as illness severity and social determinants of health, associated with a significant family change may improve our support of these families.

IntroductionEmergency department (ED) encounters offer strategic opportunities for sexually transmitted infection (STI) screening, prevention, and treatment for adolescents at risk for STIs who may not otherwise have access to routine screening. This study determined optimal ED implementation of the Teen Health Screen (THS), a validated, tablet-based, patient-reported, sexual risk assessment, and evaluated its implementation feasibility under variable workflows and high-stress tasks.MethodsWorkflow analysis included semi-structured interviews with patients, caregivers, and clinical staff members and clinical observations to understand patient and clinical workflow. The study was conducted in two urban pediatric EDs over six weeks. Participants included patients, parents/caregivers, registration staff, nurses, social workers, child life specialists, providers, and health IT experts.ResultsThe primary study outcome was development of a general model of ED workflow and patient-clinician engagement, focusing on patient flow, clinical tasks, people, and technologies involved. Workflow analyses identified key opportunities for THS deployment during the nurse assessment process, which aligns with other existing screening activities and offers privacy. This approach addresses potential barriers to integration such as privacy concerns, language and literacy barriers, the sensitivity of discussing sexual history, comfort with technology, tablet accessibility and security, and internet availability.DiscussionWorkflow analysis provided valuable insights to the perceptions, thoughts, and practicality of implementing the THS in the ED. Interviews revealed general acceptance of the new process but highlighted logistical challenges, particularly with staffing and patient surge. Implementing the THS in ED settings appears feasible, with important opportunities identified for integration to improve patient safety, including staffing and workflow optimization.

Artificial intelligence (AI) is rapidly transforming health care, offering potential benefits in diagnosis, treatment, and workflow efficiency. However, limited research explores patient perspectives on AI, especially in its role in diagnosis and communication. This study examines patient perceptions of various AI applications, focusing on the diagnostic process and communication. This study aimed to examine patient perspectives on AI use in health care, particularly in diagnostic processes and communication, identifying key concerns, expectations, and opportunities to guide the development and implementation of AI tools. This study used a qualitative focus group methodology with co-design principles to explore patient and family member perspectives on AI in clinical practice. A single 2-hour session was conducted with 17 adult participants. The session included interactive activities and breakout sessions focused on five specific AI scenarios relevant to diagnosis and communication: (1) portal messaging, (2) radiology review, (3) digital scribe, (4) virtual human, and (5) decision support. The session was audio-recorded and transcribed, with facilitator notes and demographic questionnaires collected. Data were analyzed using inductive thematic analysis by 2 independent researchers (GF and JB), with discrepancies resolved via consensus. Participants reported varying comfort levels with AI applications contingent on the level of patient interaction, with digital scribe (average 4.24, range 2-5) and radiology review (average 4.00, range 2-5) being the highest, and virtual human (average 1.68, range 1-4) being the lowest. In total, five cross-cutting themes emerged: (1) validation (concerns about model reliability), (2) usability (impact on diagnostic processes), (3) transparency (expectations for disclosing AI usage), (4) opportunities (potential for AI to improve care), and (5) privacy (concerns about data security). Participants valued the co-design session and felt they had a significant say in the discussions. This study highlights the importance of incorporating patient perspectives in the design and implementation of AI tools in health care. Transparency, human oversight, clear communication, and data privacy are crucial for patient trust and acceptance of AI in diagnostic processes. These findings inform strategies for individual clinicians, health care organizations, and policy makers to ensure responsible and patient-centered AI deployment in health care.

Background: Delayed diagnosis of Type 2 diabetes (T2D) contributes to the development of diabetes-related health complications. Although signs of T2D are commonly identified in primary care, delays in diagnosis remain a significant challenge. The relationship between patient-level factors (e.g., demographics and healthcare utilization patterns), practice-related factors (e.g., primary care location), and the time to T2D diagnosis represents a critical, yet understudied, research area. Methods: We conducted a retrospective observational cohort study of 736 adults who received care from two primary care clinics within an integrated healthcare system in the mid-Atlantic region of the United States (2017--2023). Kaplan--Meier survival analysis and Cox proportional hazards models quantified diagnostic delays from the first elevated hemoglobin A1c (HgA1c \((\ge)\)5.7% [39\,mmol/mol]) to documented T2D diagnosis. Patient features, primary care location, continuity of care and visit regularity were evaluated in multivariate models as potential contributors to diagnostic delay. A Markov cohort state-transition model characterized diagnostic pathways over one year following the initial abnormal HgA1c measurement. Results: Median time to formal T2D diagnosis varied significantly between the two primary care locations (10 months vs. 6.6 months) which highlighted practice-specific gaps in timely diagnosis. High continuity and regularity of primary care visits were significantly associated with shorter time to diagnosis. Markov cohort model revealed that 60.6% of individuals remained undiagnosed one year after the initial abnormal HgA1c. Conclusion: Our study highlights the role of clinical practice influencing diagnostic delay in T2D within primary care. Improved continuity and regularity of care accelerate the T2D diagnosis, while location-specific diagnostic disparities persist. These findings underscore the urgent need for targeted clinical and policy interventions aimed at improving patient engagement, strengthening continuity of care, and facilitating earlier diagnosis.

Background:Artificial intelligence (AI) is rapidly transforming health care, offering potential benefits in diagnosis, treatment, and workflow efficiency. However, limited research explores patient perspectives on AI, especially in its role in diagnosis and communication. This study examines patient perceptions of various AI applications, focusing on the diagnostic process and communication. Objective:This study aimed to examine patient perspectives on AI use in health care, particularly in diagnostic processes and communication, identifying key concerns, expectations, and opportunities to guide the development and implementation of AI tools. Methods:This study used a qualitative focus group methodology with co-design principles to explore patient and family member perspectives on AI in clinical practice. A single 2-hour session was conducted with 17 adult participants. The session included interactive activities and breakout sessions focused on five specific AI scenarios relevant to diagnosis and communication: (1) portal messaging, (2) radiology review, (3) digital scribe, (4) virtual human, and (5) decision support. The session was audio-recorded and transcribed, with facilitator notes and demographic questionnaires collected. Data were analyzed using inductive thematic analysis by 2 independent researchers (GF and JB), with discrepancies resolved via consensus. Results:Participants reported varying comfort levels with AI applications contingent on the level of patient interaction, with digital scribe (average 4.24, range 2-5) and radiology review (average 4.00, range 2-5) being the highest, and virtual human (average 1.68, range 1-4) being the lowest. In total, five cross-cutting themes emerged: (1) validation (concerns about model reliability), (2) usability (impact on diagnostic processes), (3) transparency (expectations for disclosing AI usage), (4) opportunities (potential for AI to improve care), and (5) privacy (concerns about data security). Participants valued the co-design session and felt they had a significant say in the discussions. Conclusions:This study highlights the importance of incorporating patient perspectives in the design and implementation of AI tools in health care. Transparency, human oversight, clear communication, and data privacy are crucial for patient trust and acceptance of AI in diagnostic processes. These findings inform strategies for individual clinicians, health care organizations, and policy makers to ensure responsible and patient-centered AI deployment in health care.

Rationale: Little is known about ongoing pulmonary dysfunction following pediatric acute respiratory failure (ARF). Existing studies are limited by small sample sizes, high attrition, and insufficiently representative study populations due to the burden of returning for in-person spirometry. We assessed whether a remote spirometry device accurately measured pulmonary function following pediatric ARF, hypothesizing that remote spirometry would be highly correlated with laboratory spirometry and would be associated with patient-reported outcomes. Methods: We conducted a prospective cohort study comparing laboratory to remote spirometry using the Spirobank Smart Spirometer at hospital discharge and 3 months post-discharge among children 6-17 years surviving ARF requiring invasive or non-invasive ventilation. We compared the difference between and correlation of laboratory and remote spirometry indices using Pearson's correlation coefficient with GLI 2022 reference equations. We assessed clinically significant decline (>4.5 points) in health-related quality of life (HRQL) from pre-illness baseline at discharge and 3 months using the Pediatric Quality of Life Inventory. Results: Among 17 enrolled children, 64.7% (n=11) completed laboratory spirometry and 76.5% (n=13) completed remote spirometry at hospital discharge; 5 patients had 3-month follow-up data. Subjects were a median of 14.6 years (IQR 11.1-16.9), 58.8% (n=10) were invasively ventilated, 88.2% (n=15) met criteria for pediatric acute respiratory distress syndrome, and the median duration of invasive or non-invasive ventilation was 6.1 days (IQR 4.1-8.8). Median forced expiratory volume in 1 second (FEV1) was 63.6% predicted (IQR 55.7-79.7) at discharge using laboratory spirometry and 65.7% predicted (IQR 46.4-71.7) using remote spirometry (Table). At 3 months, median FEV1 was 85.6% (69.5-98.0) using laboratory spirometry and 87.7% (83.5-89.8) using remote spirometry. The mean intra-subject difference between laboratory and remote spirometry for all indices was ≤6% predicted at both timepoints. Correlation between methods was highest for FVC (r=0.926 at discharge; r=0.993 at 3 months) and was ≥0.7 for all indices. FEV1 and forced vital capacity (FVC) were lower among invasively versus non-invasively ventilated patients using both methods at discharge but similar at 3 months. FEV1 % predicted by remote spirometry was lower among patients with HRQL decline from baseline (61.6% [IQR 55.7-65.7] vs 86.1% [IQR 79.5-92.6]). Conclusions: In this pilot study, remote spirometry accurately measured pulmonary function among children surviving ARF and was associated with patient-reported outcomes. Remote spirometry may be a valuable and clinically meaningful tool to expand patient representation and retention in future studies of pulmonary function following pediatric ARF that could reduce disparities in clinical research participation.

OBJECTIVES: We aimed to determine the frequency and variables associated with low femoral central venous catheter (fCVC) tip position. We also examined the association between tip position and symptomatic venous thromboembolism (VTE). DESIGN: Retrospective cohort from two PICUs. SETTING: Quaternary academic children's hospitals, 2016-2021. PATIENTS: Children (age <18 yr) in the PICU who underwent temporary fCVC placement. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Low fCVC tip position occurs when the tip is inferior to the fifth lumbar vertebra (L5) on a postprocedural abdominal radiograph. Of 936 patients: 56.3% were 1-12 years old, and 80.0% had normal weight-for-age z score. fCVC tip position was low in 67.3% of patients. In the multivariable model, older age, earlier years of placement, and higher weight-for-age were associated with low fCVC tip position. Symptomatic fCVC-associated VTE occurred in 8.8% of patients, with a rate of 16.5 per 1000 CVC days (interquartile range, 13.1-20.5 per 1000 CVC days). The percentage of VTE in low vs. recommended fCVC tip position and VTE (8.6% vs. 9.2%) were equivalent (two one-sided z-tests; p < 0.001). Furthermore, in the multivariable model, we failed to identify an association between low fCVC tip position, relative to the recommended tip position, and greater odds of VTE (OR, 1.58 [95% CI, 0.92-2.69). However, we cannot exclude the possibility of low fCVC tip position being associated with up to 2.6-fold greater odds of symptomatic VTE. CONCLUSIONS: In our two PICUs, 2016-2021, low fCVC tip position occurred in two-thirds of placements and was associated with older age and higher weight-for-age patients. fCVC-associated VTE occurred in one-in-11-catheter placements, with the raw percentage of fCVCs and subsequent VTE in low and recommended tip position being equivalent. However, the multivariable modeling indicates that future research into the relationship between tip position and VTE requires ongoing surveillance and work.