24-h energy expenditure in people with type 1 diabetes: impact on equations for clinical estimation of energy expenditure.

BACKGROUND/OBJECTIVES: Type 1 diabetes (T1D) is associated with an increase in resting metabolic rate (RMR), but the impact of T1D on other components of 24-h energy expenditure (24-h EE) is not known. Also, there is a lack of equations to estimate 24-h EE in patients with T1D. The aims of this analysis were to compare 24-h EE and its components in young adults with T1D and healthy controls across the spectrum of body mass index (BMI) and derive T1D-specific equations from clinical variables. SUBJECTS/METHODS: Thirty-three young adults with T1D diagnosed ≥1 year prior and 33 healthy controls matched for sex, age and BMI were included in this analysis. We measured 24-h EE inside a whole room indirect calorimeter (WRIC) and body composition with dual x-ray absorptiometry. RESULTS: Participants with T1D had significantly higher 24-h EE than healthy controls (T1D = 2047 ± 23 kcal/day vs control= 1908 ± 23 kcal/day; P < 0.01). We derived equations to estimate 24-h EE with both body composition (fat free mass + fat mass) and anthropometric (weight + height) models, which provided high coefficients of determination (R2 = 0.912 for both). A clinical model that did not incorporate spontaneous physical activity yielded high coefficients of determination as well (R2 = 0.897 and R2 = 0.880 for body composition and anthropometric models, respectively). CONCLUSION: These results confirm that young adults with established T1D have increased 24-h EE relative to controls without T1D. The derived equations from clinically available variables can assist clinicians with energy prescriptions for weight management in patients with T1D.

COVID-19 impacts and inequities among underserved communities with diabetes.

Background: People with diabetes have higher COVID-19 morbidity and mortality. These risks are amplified for underserved communities including racial/ethnic minorities and people with lower socioeconomic status. However, limited research has examined COVID-19 outcomes specifically affecting underserved communities with diabetes. Methods: From November 2021 to July 2022, adults with insulin-requiring diabetes at federally qualified health centers in Florida and California (n = 450) completed surveys examining COVID-19 outcomes and demographics. Surveys assessed COVID-19 severity, vaccination uptake, mask-wearing habits, income changes, and healthcare access changes. Surveys also included the full Coronavirus Anxiety Scale (CAS-19). Descriptive statistics were computed for all outcomes. Between-group comparisons for state and race/ethnicity were evaluated via Chi-Squared, Fisher's Exact, Cochran-Mantel-Haenszel, One-Way ANOVA, and t-tests. Logistic regression determined factors associated with COVID-19 vaccination uptake. Data were self-reported and analyzed cross-sectionally. Results: Overall, 29.7 % reported contracting COVID-19; of those, 45.3 % sought care or were hospitalized. Most (81.3 %) received ≥ 1 vaccine. Hispanics had the highest vaccination rate (91.1 %); Non-Hispanic Blacks (NHBs) had the lowest (73.9 %; p =.0281). Hispanics had 4.63x greater vaccination odds than Non-Hispanic Whites ([NHWs]; 95 % CI = [1.81, 11.89]). NHWs least often wore masks (18.8 %; p <.001). Participants reported pandemic-related healthcare changes (62 %) and higher costs of diabetes medications (41 %). Income loss was more frequent in Florida (76 %; p <.001). NHBs most frequently reported "severe" income loss (26.4 %; p =.0124). Loss of health insurance was more common among NHBs (13.3 %; p =.0416) and in Florida (9.7 %; p =.039). COVID-19 anxiety was highest among NHBs and Hispanics (IQR = [0.0, 3.0]; p =.0232) and in Florida (IQR = [0.0, 2.0]; p =.0435). Conclusions: Underserved communities with diabetes had high COVID-19 vaccine uptake but experienced significant COVID-19-related physical, psychosocial, and financial impacts. NHBs and those in Florida had worse outcomes than other racial/ethnic groups and those in California. Further research, interventions, and policy changes are needed to promote health equity for this population.

A quantitative model to ensure capacity sufficient for timely access to care in a remote patient monitoring program.

INTRODUCTION: Algorithm-enabled remote patient monitoring (RPM) programs pose novel operational challenges. For clinics developing and deploying such programs, no standardized model is available to ensure capacity sufficient for timely access to care. We developed a flexible model and interactive dashboard of capacity planning for whole-population RPM-based care for T1D. METHODS: Data were gathered from a weekly RPM program for 277 paediatric patients with T1D at a paediatric academic medical centre. Through the analysis of 2 years of observational operational data and iterative interviews with the care team, we identified the primary operational, population, and workforce metrics that drive demand for care providers. Based on these metrics, an interactive model was designed to facilitate capacity planning and deployed as a dashboard. RESULTS: The primary population-level drivers of demand are the number of patients in the program, the rate at which patients enrol and graduate from the program, and the average frequency at which patients require a review of their data. The primary modifiable clinic-level drivers of capacity are the number of care providers, the time required to review patient data and contact a patient, and the number of hours each provider allocates to the program each week. At the institution studied, the model identified a variety of practical operational approaches to better match the demand for patient care. CONCLUSION: We designed a generalizable, systematic model for capacity planning for a paediatric endocrinology clinic providing RPM for T1D. We deployed this model as an interactive dashboard and used it to facilitate expansion of a novel care program (4 T Study) for newly diagnosed patients with T1D. This model may facilitate the systematic design of RPM-based care programs.

Project ECHO Diabetes Cost Modeling to Support the Replication and Expansion of Tele-mentoring Programs in Non-research Settings.

INTRODUCTION: Project ECHO Diabetes is a tele-education learning model for primary care providers (PCPs) seeking to improve care for patients with diabetes from marginalized communities. Project ECHO Diabetes utilized expert "hub" teams comprising endocrinologists, dieticians, nurses, psychologists, and social workers and "spokes" consisting of PCPs and their patients with diabetes. This Project ECHO Diabetes model provided diabetes support coaches to provide additional support to patients. We sought to estimate the costs of operating a Project ECHO Diabetes hub, inclusive of diabetes support coach costs. METHODS: Data from Project ECHO Diabetes from June 2021 to June 2022 and wages from national databases were used to estimate hub and diabetes support coach costs to operate a 6-month, 24-session Project ECHO Diabetes program at hubs (University of Florida and Stanford University) and spokes (PCP clinic sites in Florida and California). RESULTS: Hub costs for delivering a 6-month Project ECHO Diabetes program to five spoke clinics were $96,873. Personnel costs were the principal driver. Mean cost was $19,673 per spoke clinic and $11.37 per spoke clinic patient. Diabetes support coach costs were estimated per spoke clinic and considered scalable in that they would increase proportionately with the number of spoke clinics in a Project ECHO Diabetes cohort. Mean diabetes support coach costs were $6,506 per spoke clinic and $3.72 per patient. Total program costs per hub were $129,404. Mean cost per clinic was $25,881. Mean cost per patient was $15.03. CONCLUSION: Herein, we document real-world costs to operate a Project ECHO Diabetes hub and diabetes support coaches. Future analysis of Project ECHO Diabetes will include estimates of spoke participation costs and changes in health care costs and savings. As state agencies, insurers, and philanthropies consider the replication of Project ECHO Diabetes, this analysis provides important initial information regarding primary operating costs.

A model to design financially sustainable algorithm-enabled remote patient monitoring for pediatric type 1 diabetes care.

Introduction: Population-level algorithm-enabled remote patient monitoring (RPM) based on continuous glucose monitor (CGM) data review has been shown to improve clinical outcomes in diabetes patients, especially children. However, existing reimbursement models are geared towards the direct provision of clinic care, not population health management. We developed a financial model to assist pediatric type 1 diabetes (T1D) clinics design financially sustainable RPM programs based on algorithm-enabled review of CGM data. Methods: Data were gathered from a weekly RPM program for 302 pediatric patients with T1D at Lucile Packard Children's Hospital. We created a customizable financial model to calculate the yearly marginal costs and revenues of providing diabetes education. We consider a baseline or status quo scenario and compare it to two different care delivery scenarios, in which routine appointments are supplemented with algorithm-enabled, flexible, message-based contacts delivered according to patient need. We use the model to estimate the minimum reimbursement rate needed for telemedicine contacts to maintain revenue-neutrality and not suffer an adverse impact to the bottom line. Results: The financial model estimates that in both scenarios, an average reimbursement rate of roughly $10.00 USD per telehealth interaction would be sufficient to maintain revenue-neutrality. Algorithm-enabled RPM could potentially be billed for using existing RPM CPT codes and lead to margin expansion. Conclusion: We designed a model which evaluates the financial impact of adopting algorithm-enabled RPM in a pediatric endocrinology clinic serving T1D patients. This model establishes a clear threshold reimbursement value for maintaining revenue-neutrality, as well as an estimate of potential RPM reimbursement revenue which could be billed for. It may serve as a useful financial-planning tool for a pediatric T1D clinic seeking to leverage algorithm-enabled RPM to provide flexible, more timely interventions to its patients.

Closing Disparities in Pediatric Diabetes Telehealth Care: Lessons From Telehealth Necessity During the COVID-19 Pandemic.

The coronavirus disease 2019 (COVID-19) pandemic necessitated using telehealth to bridge the clinical gap, but could increase health disparities. This article reports on a chart review of diabetes telehealth visits occurring before COVID-19, during shelter-in-place orders, and during the reopening period. Visits for children with public insurance and for those who were non-English speaking were identified. Telehealth visits for children with public insurance increased from 26.2% before COVID-19 to 37.3% during shelter-in-place orders and 34.3% during reopening. Telehealth visits for children who were non-English speaking increased from 3.5% before COVID-19 to 17.5% during shelter-in-place orders and remained at 15.0% during reopening. Pandemic-related telehealth expansion included optimization of workflows to include patients with public insurance and those who did not speak English. Increased participation by those groups persisted during the reopening phase, indicating that prioritizing inclusive telehealth workflows can reduce disparities in access to care.

Tele-education model for primary care providers to advance diabetes equity: Findings from Project ECHO Diabetes.

Introduction: In the US, many individuals with diabetes do not have consistent access to endocrinologists and therefore rely on primary care providers (PCPs) for their diabetes management. Project ECHO (Extension for Community Healthcare Outcomes) Diabetes, a tele-education model, was developed to empower PCPs to independently manage diabetes, including education on diabetes technology initiation and use, to bridge disparities in diabetes. Methods: PCPs (n=116) who participated in Project ECHO Diabetes and completed pre- and post-intervention surveys were included in this analysis. The survey was administered in California and Florida to participating PCPs via REDCap and paper surveys. This survey aimed to evaluate practice demographics, protocols with adult and pediatric T1D management, challenges, resources, and provider knowledge and confidence in diabetes management. Differences and statistical significance in pre- and post-intervention responses were evaluated via McNemar's tests. Results: PCPs reported improvement in all domains of diabetes education and management. From baseline, PCPs reported improvement in their confidence to serve as the T1D provider for their community (pre vs post: 43.8% vs 68.8%, p=0.005), manage insulin therapy (pre vs post: 62.8% vs 84.3%, p=0.002), and identify symptoms of diabetes distress (pre vs post: 62.8% vs 84.3%, p=0.002) post-intervention. Compared to pre-intervention, providers reported significant improvement in their confidence in all aspects of diabetes technology including prescribing technology (41.2% vs 68.6%, p=0.001), managing insulin pumps (41.2% vs 68.6%, p=0.001) and hybrid closed loop (10.2% vs 26.5%, p=0.033), and interpreting sensor data (41.2% vs 68.6%, p=0.001) post-intervention. Discussion: PCPs who participated in Project ECHO Diabetes reported increased confidence in diabetes management, with notable improvement in their ability to prescribe, manage, and troubleshoot diabetes technology. These data support the use of tele-education of PCPs to increase confidence in diabetes technology management as a feasible strategy to advance equity in diabetes management and outcomes.

Population-level management of type 1 diabetes via continuous glucose monitoring and algorithm-enabled patient prioritization: Precision health meets population health.

OBJECTIVE: To develop and scale algorithm-enabled patient prioritization to improve population-level management of type 1 diabetes (T1D) in a pediatric clinic with fixed resources, using telemedicine and remote monitoring of patients via continuous glucose monitor (CGM) data review. RESEARCH DESIGN AND METHODS: We adapted consensus glucose targets for T1D patients using CGM to identify interpretable clinical criteria to prioritize patients for weekly provider review. The criteria were constructed to manage the number of patients reviewed weekly and identify patients who most needed provider contact. We developed an interactive dashboard to display CGM data relevant for the patients prioritized for review. RESULTS: The introduction of the new criteria and interactive dashboard was associated with a 60% reduction in the mean time spent by diabetes team members who remotely and asynchronously reviewed patient data and contacted patients, from 3.2 ± 0.20 to 1.3 ± 0.24 min per patient per week. Given fixed resources for review, this corresponded to an estimated 147% increase in weekly clinic capacity. Patients who qualified for and received remote review (n = 58) have associated 8.8 percentage points (pp) (95% CI = 0.6-16.9 pp) greater time-in-range (70-180 mg/dl) glucoses compared to 25 control patients who did not qualify at 12 months after T1D onset. CONCLUSIONS: An algorithm-enabled prioritization of T1D patients with CGM for asynchronous remote review reduced provider time spent per patient and was associated with improved time-in-range.

Improved individual and population-level HbA1c estimation using CGM data and patient characteristics.

Machine learning and linear regression models using CGM and participant data reduced HbA1c estimation error by up to 26% compared to the GMI formula, and exhibit superior performance in estimating the median of HbA1c at the cohort level, potentially of value for remote clinical trials interrupted by COVID-19.

Uninterrupted continuous glucose monitoring access is associated with a decrease in HbA1c in youth with type 1 diabetes and public insurance.

OBJECTIVE: Continuous glucose monitor (CGM) use is associated with improved glucose control. We describe the effect of continued and interrupted CGM use on hemoglobin A1c (HbA1c) in youth with public insurance. METHODS: We reviewed 956 visits from 264 youth with type 1 diabetes (T1D) and public insurance. Demographic data, HbA1c and two-week CGM data were collected. Youth were classified as never user, consistent user, insurance discontinuer, and self-discontinuer. Visits were categorized as never-user visit, visit before CGM start, visit after CGM start, visit with continued CGM use, visit with initial loss of CGM, visit with continued loss of CGM, and visit where CGM is regained after loss. Multivariate regression adjusting for age, sex, race, diabetes duration, initial HbA1c, and body mass index were used to calculate adjusted mean and delta HbA1c. RESULTS: Adjusted mean HbA1c was lowest for the consistent user group (HbA1c 8.6%;[95%CI 7.9,9.3]). Delta HbA1c (calculated from visit before CGM start) was lower for visit after CGM start (-0.39%;[95%CI -0.78,-0.02]) and visit with continued CGM use (-0.29%;[95%CI -0.61,0.02]), whereas it was higher for visit with initial loss of CGM (0.40%;[95%CI -0.06,0.86]), visit with continued loss of CGM (0.46%;[95%CI 0.06,0.85]), and visit where CGM is regained after loss (0.57%;[95%CI 0.06,1.10]). CONCLUSIONS: Youth with public insurance using CGM have improved HbA1c, but only when CGM use is uninterrupted. Interruptions in use, primarily due to gaps in insurance coverage of CGM, were associated with increased HbA1c. These data support both initial and ongoing coverage of CGM for youth with T1D and public insurance.

The Neighborhood Deprivation Index and Provider Geocoding Identify Critical Catchment Areas for Diabetes Outreach.

PURPOSE: In designing a Project ECHO™ type 1 diabetes (T1D) program in Florida and California, the Neighborhood Deprivation Index (NDI) was used in conjunction with geocoding of primary care providers (PCPs) and endocrinologists in each state to concurrently identify areas with low endocrinology provider density and high health risk/poverty areas. The NDI measures many aspects of poverty proven to be critical indicators of health outcomes. METHODS: The data from the 2013-2017 American Community Survey (ACS) 5-year estimates were used to create NDI maps for California and Florida. In addition, geocoding and 30-minute drive-time buffers were performed using publicly available provider directories for PCPs and endocrinologists in both states by Google Geocoding API and the TravelTime Search Application Programming Interface (API). RESULTS: Based on these findings, we defined high-need catchment areas as areas with (1) more than a 30-minute drive to the nearest endocrinologist but within a 30-minute drive to the nearest PCP; (2) an NDI in the highest quartile; and (3) a population above the median (5199 for census tracts, and 1394 for census block groups). Out of the 12 181 census tracts and 34 490 census block groups in California and Florida, we identified 57 tracts and 215 block groups meeting these criteria as high-need catchment areas. CONCLUSION: Geospatial analysis provides an important initial methodologic step to effectively focus outreach efforts in diabetes program development. The integration of the NDI with geocoded provider directories enables more cost-effective and targeted interventions to reach the most vulnerable populations living with T1D.

HbA1c Levels in Type 1 Diabetes from Early Childhood to Older Adults: A Deeper Dive into the Influence of Technology and Socioeconomic Status on HbA1c in the T1D Exchange Clinic Registry Findings.

Objective: The T1D Exchange Clinic Registry figure of HbA1c levels according to age has become a classic picture of how average HbA1c varies from childhood to elderly. To further assess the course of HbA1c across the life span in T1D, we created similar figures stratified by device use and socioeconomic status (SES). Methods: Mean HbA1c was plotted versus age for 21,253 T1D Exchange Clinic Registry participants with an HbA1c measurement between January 1, 2016 and March 31, 2018 according to device use, race/ethnicity, and measures of SES. Results: Across the age range from childhood to elderly, continuous glucose monitoring (CGM) use without an insulin pump had better average HbA1c than pump without CGM; and among CGM users, pump and injection users had similar HbA1c levels. Any device use (pump or CGM) was associated with better HbA1c levels than no device use across the age range. Lower SES and African American race were associated with higher HbA1c across the age range. Across all device use, SES, and race/ethnicity factors, average HbA1c levels were highest in adolescents and young adults. Conclusion: Although the plot of average HbA1c from early childhood to elderly shifts according to device use and SES factors, the shape of the plots remains reasonably constant with highest HbA1c levels in adolescents and young adults. These findings emphasize the importance of targeting adolescence and early adulthood as the ages with the greatest need for improving diabetes management irrespective of device use and SES.

Assessment of a Precision Medicine Analysis of a Behavioral Counseling Strategy to Improve Adherence to Diabetes Self-management Among Youth: A Post Hoc Analysis of the FLEX Trial.

Importance: The Flexible Lifestyles Empowering Change (FLEX) trial, an 18-month randomized clinical trial testing an adaptive behavioral intervention in adolescents with type 1 diabetes, showed no overall treatment effect for its primary outcome, change in hemoglobin A1c (HbA1c) percentage of total hemoglobin, but demonstrated benefit for quality of life (QoL) as a prespecified secondary outcome. Objective: To apply a novel statistical method for post hoc analysis that derives an individualized treatment rule (ITR) to identify FLEX participants who may benefit from intervention based on changes in HbA1c percentage (primary outcome), QoL, and body mass index z score (BMIz) (secondary outcomes) during 18 months. Design, Setting, and Participants: This multisite clinical trial enrolled 258 adolescents aged 13 to 16 years with type 1 diabetes for 1 or more years, who had literacy in English, HbA1c percentage of total hemoglobin from 8.0% to 13.0%, a participating caregiver, and no other serious medical conditions. From January 5, 2014, to April 4, 2016, 258 adolescents were recruited. The post hoc analysis excluded adolescents missing outcome measures at 18 months (2 participants [0.8%]) or continuous glucose monitoring data at baseline (40 participants [15.5%]). Data were analyzed from April to December 2018. Interventions: The FLEX intervention included a behavioral counseling strategy that integrated motivational interviewing and problem-solving skills training to increase adherence to diabetes self-management. The control condition entailed usual diabetes care. Main Outcomes and Measures: Subgroups of FLEX participants were derived from an ITR estimating which participants would benefit from intervention, which would benefit from control conditions, and which would be indifferent. Multiple imputation by chained equations and reinforcement learning trees were used to estimate the ITR. Subgroups based on ITR pertaining to changes during 18 months in 3 univariate outcomes (ie, HbA1c percentage, QoL, and BMIz) and a composite outcome were compared by baseline demographic, clinical, and psychosocial characteristics. Results: Data from 216 adolescents in the FLEX trial were reanalyzed (166 [76.9%] non-Hispanic white; 108 teenaged girls [50.0%]; mean [SD] age, 14.9 [1.1] years; mean [SD] diabetes duration, 6.3 [3.7] years). For the univariate outcomes, a large proportion of FLEX participants had equivalent predicted outcomes under intervention vs usual care settings, regardless of randomization, and were assigned to the muted group (HbA1c: 105 participants [48.6%]; QoL: 63 participants [29.2%]; BMIz: 136 participants [63.0%]). Regarding the BMIz univariate outcome, mean baseline BMIz of participants assigned to the muted group was lower than that of those assigned to the intervention and control groups (muted vs intervention: mean difference, 0.48; 95% CI, 0.21 to 0.75; P = .002; muted vs control: mean difference, 0.86; 95% CI, 0.61 to 1.11; P < .001); this group also had a higher proportion of individuals with underweight or normal weight using weight status cutoffs (95 [69.9%] in muted group vs 24 [54.6%] in intervention group and 11 [30.6%] in control group; χ24 = 24.67; P < .001). The approach identified subgroups estimated to benefit based on HbA1c percentage (54 participants [25.0%]), QoL (89 participants [41.2%]), and BMIz (44 participants [20.4%]). Regarding the HbA1c percentage outcome, participants expected to benefit from the intervention did not have significantly higher baseline HbA1c percentages than those expected to benefit from usual care (9.4% vs 9.2%; difference, 0.2%; 95% CI, -0.16% to 0.56%; P = .44). However, participants in the muted group had higher mean HbA1c percentages at baseline than those assigned to the intervention or control groups (muted vs intervention: 9.9% vs 9.4%; difference, 0.5%; 95% CI, 0.13% to 0.89%; P = .02; muted vs control; 9.9% vs 9.2%; difference, 0.7%; 95% CI, 0.34% to 1.08%; P = .001). No significant differences were found between subgroups estimated to benefit in terms of the composite outcome from the FLEX intervention (91 participants [42.1%]) vs usual care (125 participants [57.9%]). Conclusions and Relevance: The precision medicine approach represents a conceptually and analytically novel approach to post hoc subgroup identification. More work is needed to understand markers of positive response to the FLEX intervention. Trial Registration: ClinicalTrial.gov identifier: NCT01286350.

Exploring Variation in Glycemic Control Across and Within Eight High-Income Countries: A Cross-sectional Analysis of 64,666 Children and Adolescents With Type 1 Diabetes.

OBJECTIVE: International studies on childhood type 1 diabetes (T1D) have focused on whole-country mean HbA1c levels, thereby concealing potential variations within countries. We aimed to explore the variations in HbA1c across and within eight high-income countries to best inform international benchmarking and policy recommendations. RESEARCH DESIGN AND METHODS: Data were collected between 2013 and 2014 from 64,666 children with T1D who were <18 years of age across 528 centers in Germany, Austria, England, Wales, U.S., Sweden, Denmark, and Norway. We used fixed- and random-effects models adjusted for age, sex, diabetes duration, and minority status to describe differences between center means and to calculate the proportion of total variation in HbA1c levels that is attributable to between-center differences (intraclass correlation [ICC]). We also explored the association between within-center variation and children's glycemic control. RESULTS: Sweden had the lowest mean HbA1c (59 mmol/mol [7.6%]) and together with Norway and Denmark showed the lowest between-center variations (ICC ≤4%). Germany and Austria had the next lowest mean HbA1c (61-62 mmol/mol [7.7-7.8%]) but showed the largest center variations (ICC ∼15%). Centers in England, Wales, and the U.S. showed low-to-moderate variation around high mean values. In pooled analysis, differences between counties remained significant after adjustment for children characteristics and center effects (P value <0.001). Across all countries, children attending centers with more variable glycemic results had higher HbA1c levels (5.6 mmol/mol [0.5%] per 5 mmol/mol [0.5%] increase in center SD of HbA1c values of all children attending a specific center). CONCLUSIONS: At similar average levels of HbA1c, countries display different levels of center variation. The distribution of glycemic achievement within countries should be considered in developing informed policies that drive quality improvement.

Implementation of Depression Screening and Global Health Assessment in Pediatric Subspecialty Clinics.

PURPOSE: Adolescents with chronic illness face greater risk of psychosocial difficulties, complicating disease management. Despite increased calls to screen for patient-reported outcomes, clinical implementation has lagged. Using quality improvement methods, this study aimed to investigate the feasibility of standardized screening for depression and assessment of global health and to determine recommended behavioral health follow-up, across three pediatric subspecialty clinics. METHODS: A total of 109 patients aged 12-22 years (median = 16.6) who were attending outpatient visits for treatment of diabetes (80% type 1), inflammatory bowel disease, or cystic fibrosis completed the 9-item Patient Health Questionnaire (PHQ-9) depression and Patient-Reported Outcomes Measurement Information System (PROMIS) Pediatric Global Health measures on electronic tablets. Patients screening positive on the PHQ-9 received same-day behavioral health assessment and regular phone check-ins to facilitate necessary follow-up care. RESULTS: Overall, 89% of 122 identified patients completed screening during a 6-month window. Patients completed measures in a timely manner (within 3 minutes) without disruption to clinic flow, and they rated the process as easy, comfortable, and valuable. Depression scores varied across disease type. Patients rated lower global health relative to a previously assessed validation cohort. Depression and global health related significantly to certain medical outcomes. Fifteen percent of patients screened positive on the PHQ-9, of whom 50% confirmed attending behavioral health appointments within 6 months of screening. CONCLUSIONS: A standardized depression and global health assessment protocol implemented across pediatric subspecialties was feasible and effective. Universal behavioral health screening for adolescents and young adults living with chronic disease is necessary to meet programmatic needs in pediatric subspecialty clinics.