SUMMARY:Fetal seizures represent one of the rarest phenomena in prenatal medicine, with fewer than 50 documented cases. Diagnosis has traditionally relied on maternal reports and ultrasound visualization of abnormal movements, lacking the neurophysiologic confirmation available in neonatal care. This case report presents the first neurophysiologic assessment of fetal seizures using fetal magnetoencephalography (fMEG), demonstrating a novel diagnostic approach for prenatal neurologic evaluation. A 22-year-old primigravida with controlled epilepsy presented with a fetus showing severe growth restriction and multiple anomalies at 23 weeks gestation. At 32 weeks, repetitive rhythmic jerking movements suggested in utero seizures and were confirmed with real-time ultrasound. fMEG was performed using the SQUID Array for Reproductive Assessment (SARA) system, revealing immature patterns indicative of encephalopathy with alternating periods of severe bilateral suppression and low-amplitude activity. Bursts lasting up to 6 seconds coincided with erratic body movements confirmed by actogram. The infant was delivered at 37 weeks weighing 1,200 g and continued to exhibit myoclonic and clonic seizures postnatally. Postmortem whole exome sequencing identified biallelic pathogenic variants in PSAT1 , confirming Neu-Laxova syndrome. The infant died on the fifth day of life after transition to comfort care. This case demonstrates the feasibility of direct neurophysiologic assessment of fetal brain activity using fMEG, providing objective confirmation of fetal seizures for the first time. The technology offers potential for distinguishing true fetal seizures from other conditions that mimic seizure-like movements, enabling more accurate prenatal counseling and informed decision making. This represents a significant advancement in prenatal neurologic assessment, with implications for early diagnosis and targeted interventions.

Social determinants of health (SDoH) diagnosis codes (also known as Z-codes) are used to document patients’ SDoH, which could affect a patient’s health. However, their use during pregnancy is unknown. Additionally, with the growth in telehealth use in recent years, how the Z-codes are documented across in-person outpatient/office and telehealth prenatal visits is undetermined. Therefore, the objective of this study was to document trends in prenatal Z-code diagnosis in telehealth (audio-only and audio-visual) and in-person outpatient/office visits, and the association between visit type and Z-codes during pregnancy. Repeated cross-sectional and case-control designs were employed using the Epic Cosmos database (July 2019–March 2025). All pregnancies with start dates on or after July 01 2020 and visits until March 31, 2025 were considered. Total in-person outpatient/office and telehealth visits (denominators) were summed, and visits with a Z-code were determined (numerators). Rates were expressed per 1,000 visits. Case-control analyses, stratified by provider type, were conducted to estimate the relationship between visit type and Z-code incidence. The first documentation of Z-codes (a wash-out period of 365 days) in each pregnancy episode was identified (case visits), and control visits were gathered. Multivariable logistic regressions were estimated and odds ratios (OR) were reported. A total of 3,435,577 pregnancies were included, totaling 33,376,363 in-person outpatient and telehealth visits. Overall average Z-code rates per 1,000 visits were: 8.64 (in-person), 6.71 (audio-only), and 12.35 (audio-visual). The rates increased on average for all three visit types over the study period, with rates slightly higher for audio-visual visits. In the provider sub-group of family practice/ob-gyn/other clinicians, audio-visual visits had no significant association with Z-code incident diagnosis (OR: 1.04 [0.99; 1.10]), while audio-only visits had lower odds (OR: 0.60 [0.47; 0.78]), compared to in-person visits. Among nursing/midwife provider sub-group, audio-visual visits were associated with 42

Objective: In Arkansas, only 55.8% of infants receive breastmilk during the first six months of life, despite recommendations from the American Academy of Pediatrics. Breast feeding rates are significantly lower among Medicaid users. Telelactation aims to address challenges that cause early breastfeeding cessation. This study evaluated patient perspectives on telelactation and explored barriers to breastfeeding among a Medicaid population. Methods: Participants received four virtual lactation visits followed by a semistructured interview. Results: 85.5% of participants were breastfeeding at one month and 79% were still breastfeeding at two months. The overall participant perception of telelactation was positive, with 93% of participants stating their confidence in breastfeeding increased. Conclusion: This study highlights the wide acceptance and benefit of telelactation for sustaining breastfeeding within a Medicaid population. Innovation: This study utilized digital technologies to bring professional lactation support directly to breastfeeding mothers. Virtual consultations expand access to specialty health services and education by overcoming geographical limitations and reducing burdens to care access. While telehealth technologies have been used in other healthcare settings, the use of telelactation is an innovative tool to address gaps in in care and improve continuity of care.

Fetal magnetoencephalography (fMEG) enables non-invasive monitoring of fetal brain function with high temporal resolution. However, how can we isolate low signal-to-noise ratio signals of the developing brain when disruptive artifacts arise from maternal and fetal movements? Addressing this challenge is critical for understanding brain development. We present Advanced Localization and Processing of fMEG Signals based on Maternal and Gross fetal body Movement Exclusion (ALPS-FMEG), a MATLAB-based framework that improves fetal brain signals by removing fetal and maternal movement artifacts. ALPS-FMEG integrates Independent Component Analysis for separation and reconstruction of fetal brain, fetal and maternal cardiac signal components in sensor space, Empirical Mode Decomposition for noise reduction, and a movement artifact detection-and-exclusion technique based on actogramCOG associated with heart rate patterns. This novel integration modifies the actogramCOG approach by pre-interpolating R waves for enhanced robustness and combines it with HRV-based logic gates, representing a first in fMEG processing to achieve artifact-free signals while preserving physiological latencies. ALPS-FMEG was applied to 50 fMEG datasets from 28 to 39 weeks of gestation, enhancing signal quality. For group analysis, 45 datasets were retained after excluding recordings with auditory event-related field (fAEF) latencies < 70 ms. In these, it significantly improved signal-to-noise ratio and fAEF amplitudes (p < 0.0001), with preserved latencies. fAEF latency showed a significant negative correlation with gestational age (p < 0.001). ALPS-FMEG improves fetal brain signal extraction by addressing movement artifacts. This method supports robust fetal brain analysis and may be adaptable to future fMEG systems, including optically pumped magnetometers, enhancing prenatal neurophysiology and clinical research, though manual steps currently limit scalability and could be addressed via automation for broader practical use.

BACKGROUND:Fetal magnetocardiography (fMCG) noninvasively measures the magnetic fields generated by the fetal heart to assess cardiac electrophysiology. The advent of optically pumped magnetometers (OPMs) enables portable, cryogen-free acquisition of high-fidelity fMCG signals. However, maternal and fetal characteristics may introduce variability affecting signal quality and cardiac time-interval (CTI) detectability. OBJECTIVE:This study quantifies the influence of maternal and fetal parameters (placental position, body mass index, fetal heart distance, and gestational age [GA]) on the signal-to-noise ratio (SNR) and CTI detectability in OPM-based fMCG recordings. METHODS:A total of 107 OPM recordings were obtained longitudinally from 32 pregnant participants (28-38 weeks of gestation) using a bed-based, stand-alone OPM array system. SNR was derived from fMCG signals, and maternal and fetal characteristics were obtained from clinical data. Mixed-effects linear and logistic regression models were used to evaluate associations among these parameters, SNR, and CTI detectability. RESULTS:Mean SNR and variance were 10.95 dB and 14.56 dB2, respectively. Multivariable mixed-effects linear regression reduced total variance to 12.55 dB2 and yielded a fixed-effects R2 of 19.8%. SNR increased significantly with GA and decreased with fetal heart distance. Higher SNR was associated with improved CTI detectability, particularly for T wave and QT interval. GA remained a significant predictor of T-wave detectability, whereas fetal heart distance exerted an independent negative effect. CONCLUSION:Fetal heart distance and GA are the primary determinants of OPM-fMCG signal quality. Accounting for maternal physiological variability is essential for optimizing acquisition protocols and improving the interpretability of fetal cardiac data.

Intrauterine growth restriction (IUGR), a condition marked by impaired fetal growth, is associated with long-term neurodevelopmental challenges. However, early functional biomarkers of altered brain development remain limited. In this study, we used fetal magnetoencephalography (MEG) during spontaneous activity to examine power spectral density (PSD) as a measure of oscillatory brain activity and neural network maturation, providing an early functional index of cortical development and potential dysmaturation in IUGR fetuses compared to normally growing controls. Recordings were obtained in the third trimester (approximately 28–34 weeks’ gestation), and spectral power was analyzed across conventional frequency bands (delta, theta, alpha, and beta). While no significant group-level differences were found in most frequency bands, alpha power was significantly lower in the IUGR group. Moreover, greater fetal delta power and lower alpha and beta power were correlated with poorer postnatal neurobehavioral performance as assessed by the NICU Network Neurobehavioral Scale (NNNS), particularly in the domains of regulation, tone, and attention. These findings suggest that fetal MEG could potentially detect both group differences and subtle neural signatures linked to later neurobehavior.

Maternal diabetes is associated with increased systemic inflammation and has been linked to adverse neonatal outcomes, including developmental delays that persist into early childhood. In this study we sought to characterize and compare the maternal levels and fetal cord-blood levels of the inflammatory markers C-reactive protein (CRP) and IL-6, as well as the neurotrophin brain-derived neurotrophic factor (BDNF) between mothers with pre-gestational Type-1 diabetes (T1DM) or Type-2 diabetes (T2DM), and non-diabetic controls (nonDM).A prospective cohort design was employed, analyzing biomarker concentrations during the third trimester in 98 pregnant women ages 18-40 years of age including 16 participants with T1DM, 49 participants with T2DM and 33 control participants matched for gestational age and body mass index (BMI) to control for confounding factors such as obesity. Plasma samples were collected at 28-30 weeks, 34-36 weeks, delivery, and from cord blood. The biomarkers CRP, IL-6, and BDNF were measured using standardized assays, and concentrations were compared among groups using ANOVA.In T2DM mothers, CRP levels were 2x higher in the third trimester as compared to nonDM controls. In T1DM mothers, IL6 levels were 3x lower than nonDM controls and 3.4x lower than T2DM. While not reaching statistical significance, cord-blood levels of IL6 were higher in T2DMs than other groups (p = 0.052). When examining BDNF levels, no differences were observed between groups.This study emphasizes the importance of addressing inflammation-related risks in pregnancies affected by diabetes. Targeted interventions may mitigate adverse neonatal outcomes and improve health trajectories. Future research should explore direct pathways linking maternal inflammation to fetal neural function to inform clinical strategies.

Background Fetal growth restriction (FGR) is associated with adverse neurodevelopmental outcomes, yet reliable prenatal functional biomarkers of altered brain maturation remain limited. Fetal magnetoencephalography (MEG) provides a noninvasive method to assess spontaneous brain activity and may identify early electrophysiological alterations associated with later neurobehavioral outcomes. We evaluated whether fetal MEG power spectral density (PSD) differs between fetuses with FGR and normally growing controls and whether fetal PSD is associated with postnatal neurobehavior. Methods In this prospective observational study, fetuses with FGR (n = 9) and normally growing controls (n = 15) underwent fetal MEG recordings between 28 and 34 weeks’ gestation. Absolute log-transformed PSD was calculated for the delta, theta, alpha, and beta frequency bands. Group comparisons were performed using analysis of covariance with total spectral power as a covariate. Associations between fetal PSD and postnatal neurobehavior, assessed using the NICU Network Neurobehavioral Scale (NNNS), were evaluated using partial correlation analyses controlling for total spectral power. Results No significant group differences were observed in the delta or theta frequency bands. Alpha power was significantly lower in the FGR group than in controls (difference = 1.10 dB; p = 0.013), while beta power demonstrated a larger, but nonsignificant decrease (difference = 1.59 dB; p = 0.061). Exploratory analyses demonstrated moderate-to-large associations between fetal spectral power and several NNNS domains. Greater delta power and lower alpha and beta power were associated with less favorable neurobehavioral performance, particularly in domains related to regulation, tone, and attention. Conclusions Fetal MEG identified subtle alterations in spontaneous brain activity associated with fetal growth restriction and demonstrated associations between prenatal electrophysiological measures and early postnatal neurobehavior. Although these findings require confirmation in larger prospective studies, they support further investigation of fetal MEG as a potential biomarker of early neurodevelopmental vulnerability.

There are associations between maternal diabetes and neurodevelopmental disorders, such as autism spectrum disorders, attention-deficit/hyperactivity disorder, and intellectual disabilities. Using this knowledge, our objective is to characterize the effects of type 1 (T1DM) and type 2 diabetes mellitus (T2DM) on the neurodevelopment of infants. We performed a prospective study on 54 infants of mothers with T1DM (n = 10), T2DM (n = 24), and non-diabetic controls (n = 20). To evaluate their neurodevelopment in multiple developmental domains, we used four assessments on 1-2-month-old infants: The Hammersmith Neonatal Neurological Examination (HNNE), The Dubowitz exam, The Capute Scales, and The General Movement Assessment (GMA). Differences in neurodevelopmental outcomes did not reach statistical significance in any of the four assessments, individually or combined. However, we note the following trends: 20 infants had suboptimal neuromotor development (HNNE score < 30.5). T1DM group infants had lower mean language scores (83.7 vs 105.85) when compared to the control group. Using GMA, T2DM group infants had more abnormal writhing movements when compared to the other groups. Our overall findings suggest that the T1DM group had lower language scores and the T2DM group had lower visual-motor (cognitive) scores which may indicate developmental delays. Further research is needed to confirm these findings. We suggest that these infant's parents consider developmental therapy, to promote early identification and treatment of potential developmental delays. To our knowledge this study would be the first to use the combination of these assessments to evaluate 1-month-olds in this patient population. Given the small sample size, these findings should be interpreted with caution.

Background: This study assesses the trend in remote patient monitoring (RPM) utilization among Medicare beneficiaries in the United States with differing rural/urban and racial/ethnic statuses. Methods: Using Medicare fee-for-service claims from January 2018 to December 2020, monthly rates of beneficiaries utilizing RPM per 100,000 beneficiaries enrolled in both Medicare Parts A and B were calculated. Comparative interrupted time series models delineated differences in level and trend of RPM utilization between beneficiaries with differing rural/urban status, race/ethnicity, and race/ethnicity as stratified by rural/urban status using March 2020, the start of COVID-19 public health emergency (PHE) in the United States, as interruption time. Results: RPM utilization increased from 2 to 240 RPM claims per 100,000 Medicare beneficiaries from January 2018 to December 2020. Urban beneficiaries experienced a 24.20 RPM user-level change per month at the start of PHE. Trend difference for urban versus rural beneficiaries increased by 7.85 RPM users per month before and after the start of PHE (p < 0.0001). The trend difference for non-Hispanic Black versus White beneficiaries significantly increased by 12.43 RPM users per 100,000 beneficiaries per month after declaration of PHE (p < 0.0001). Similarly, the trend difference for beneficiaries of Hispanic and of other races significantly increased by 7.48 and 16.93 RPM users per 100,000 beneficiaries per month, respectively, after the declaration of the PHE (p < 0.0001 for both). Trends for racial/ethnic minorities stratified by rural/urban status were similar to the overall trends by racial/ethnic group. Discussion: Inequities in RPM utilization exist and were exacerbated by the COVID-19 pandemic. Targeted interventions are needed to increase RPM utilization broadly, particularly among rural residents.

Fetal movement (FM) is a key indicator of fetal health and development. Fetal magnetocardiography (fMCG) has been used to assess FM by analyzing fetal heart rate (FHR). Low-cost, non-cryogenic optically pumped magnetometers (OPMs) provide similar biomagnetic fMCG data to SQUIDs (Superconducting Quantum Interference Devices) while eliminating the need for cryogenic cooling. In this study, we used a bed-based stand-alone OPM array housed in a cylindrical three-layer shield to obtain fMCG signals. The 14-sensor OPM array operates in dual-axis mode, measuring biomagnetic fields in the y-z directions. Sensor geometry was obtained using a planar disc with dipolar coils, HALO (QuSpin Inc.). FM was computed with an algorithm that combines the magnetic field strength of the fMCG signal and the OPM sensor locations. Data were collected from four pregnant women, aged 28 to 36 weeks. fMCG signals were isolated with a projection operator algorithm, and FHR and FM were quantified. Our preliminary data demonstrate the use of OPM sensors to measure FHR and FM in a bed-based stand-alone system. FHR and FM metrics obtained in this study fall within the range reported in previous SQUID-based studies, highlighting the potential of OPM technology for fetal monitoring and research.

ABSTRACT: Background: Interstate licensure portability has become a significant issue for US healthcare providers. Healthcare compacts have emerged as a promising solution to facilitate interstate licensure portability for many healthcare specialties while maintaining individual state autonomy. Objective: This study aimed to describe the landscape of interstate healthcare compacts in the US. Methods: We systematically analyzed compact and legislative websites to determine state-level healthcare licensure compact participation over time. Results: More healthcare compact bills have been passed over time, as established compacts recruit new states and new compacts emerge. Of the 15 active healthcare compacts identified, all 15 compacts saw the first state/territory to pass compact-specific legislation in at least 1 state/territory in the year of or following the approval of the drafted model legislation. However, the time between when compacts are first discussed to the approval of the drafted model legislation varied considerably between compacts, ranging from 1 year to 20 years. Very few states/territories pass more than 1 or 2 healthcare compact-related bills in any year. Conclusions: In the last decade, the landscape of interstate healthcare practice has changed dramatically, and state and territory participation in interstate healthcare licensure compacts has expanded over time.

PURPOSE:To examine factors associated with rural hospital telehealth adoption during the COVID-19 public health emergency (PHE), and evaluate its relationship with rural hospital financial performance before and during the PHE. METHODS:This panel study used retrospective data (2017-2021) from the American Hospital Association Annual Survey, the Centers for Medicare & Medicaid Services Healthcare Cost Report Information Systems, and the Area Health Resource File. Rural hospitals were categorized as persistent adopters, persistent nonadopters, or switchers based on telehealth adoption status. Bivariate analyses assessed differences in subgroup means and frequencies, while a difference-in-difference model estimated the impact of telehealth adoption on rural hospital financial performance. FINDINGS:Telehealth adoption varied among rural hospitals. Before the PHE, 75% (751) of rural hospitals had adopted telehealth, while 25% (247) were nonadopters. Despite efforts to promote remote care delivery during the PHE, 58% (144) of pre-PHE nonadopters did not adopt telehealth. Among the 42% (103) that did adopt telehealth during the PHE, no statistically significant effect was observed on operating or total margins. CONCLUSION:Rural hospitals in economically disadvantaged and sparsely populated areas, which stand to benefit the most from telehealth adoption, often face substantial barriers that limit their ability to adopt this technology. Financial constraints and limited resources continue to hinder adoption, underscoring the need for targeted policies and investments to expand telehealth access and improve health care outcomes in rural communities.

Objectives/Goals: Evaluate the impact of intrauterine growth restriction (IUGR) on neonatal brain development using magnetoencephalography (MEG) and correlate findings with NICU Network Neurobehavioral scale (NNNS) scores at 1 month Methods/Study Population: In this prospective cohort study, we will enroll 30 participants, consisting of 15 neonates diagnosed with IUGR and 15 healthy controls, matched by gestational age, from the University of Arkansas for Medical Sciences. We will perform MEG scans at three key developmental stages: during fetal life, at 1 month, and at 3 months of age and a Bayley IV exam at 12 months of age. The NNNS assessments will be conducted at the 1-month visit to evaluate neurobehavioral outcomes. All MEG data will be synchronized with clinical evaluations and maternal health records to ensure comprehensive analysis. Results/Anticipated Results: We anticipate that the study will reveal significant differences in brain maturation and neural activity patterns between IUGR-affected infants and healthy controls. Specifically, we expect to find altered neural connectivity and delayed maturation in the delta and theta frequency bands during the early neonatal period in the IUGR group. These anticipated neuroimaging findings will be correlated with NNNS scores to assess functional implications of the observed brain activity differences. If our hypotheses are confirmed, the study will provide robust biomarkers for early identification of neurodevelopmental delays in IUGR-affected infants, paving the way for targeted early interventions. Discussion/Significance of Impact: This study could significantly enhance early detection and intervention strategies for IUGR, potentially reducing long-term neurodevelopmental challenges and improving clinical outcomes

BACKGROUND:A substantial proportion of maternal morbidity and mortality occurs in the postpartum period. In 2018, the American College of Obstetricians and Gynecologists (ACOG) published new guidelines recommending that all women have contact with a maternity care provider by 3 weeks postpartum. Although this revised schedule addresses the evident need for enhanced postpartum care, it has not been tested in a clinical trial and implementation has been hampered by logistical and financial constraints. The goal of this study is to evaluate the effect on postpartum outcomes of the Telehealth Multi-Component Optimal Model (MOM) of postpartum care, which delivers the early postpartum visit through telehealth. DESIGN:We will conduct a type 1 hybrid effectiveness-implementation trial of the Telehealth MOM postpartum care model. This randomized-controlled trial will enroll 1500 diverse pregnant women at five obstetric clinics across Arkansas. We will randomize participants 1:1 to receive enhanced standard of care or the Telehealth MOM model, with remote monitoring of blood pressure and temperature for 14 days postpartum and a telehealth screening for complications between 6 and 14 days after birth. Outcomes are obtained from health records (postpartum visit completion, early detection of complications, readmissions) and surveys at 9 weeks, 6 months, and 13 months postpartum. Qualitative interviews with patients, providers, and study staff will inform development of an implementation blueprint. CONCLUSION:This study will contribute much needed evidence regarding the effectiveness of telehealth and remote monitoring in the early postpartum period and can inform policies and strategies for implementing the 2018 postpartum care guidelines.

Maternal birth injury contributes to future pelvic floor disorders, yet we possess an incomplete understanding of the levator ani muscles during pregnancy. We applied a noninvasive magnetomyography technique to characterize levator ani muscle activity in pregnancy with ultrasound and clinical exam. Magnetomyographic measures of levator ani muscle activity were collected using a noninvasive biomagnetic sensor from 53 pregnant women during rest and voluntary muscle contractions of varying intensity. Power spectral density was calculated using Welch's method to obtain the mean power of each Kegel exercise. Levator hiatus circumference was measured using ultrasound, and contraction strength was measured via the Brink scale. Magnetomyography data revealed a mean root mean square (RMS) rest of 39.7 ± 8.6 femtoTesla (fT) and Kegel of 52.9 ± 17.1 fT. Mean power spectral density (PSD) in log10 (fT2/Hz) was 0.9 ± 0.2 at rest and 1.1 ± 0.2 during Kegel. Ultrasound measures of levator hiatus circumference were 13.3 ± 1.6 cm at rest and 11.6 ± 1.7 cm during maximum Kegel. Magnetomyographic correlations with levator hiatus circumference were stronger for amplitude and PSD at rest (-0.35 and -0.33) than for Kegel (-0.20 and -0.19). Magnetomyography-based amplitudes of pelvic floor activity directly correlate with ultrasound levator hiatus circumference during rest and Kegel.

Fetal magnetocardiography (fMCG) is a non-invasive technique that measures the magnetic fields associated with fetal heart electrical activity outside of the maternal abdomen. fMCG has high temporal precision for measuring fetal heart rate and its variability which reflects fetal neurodevelopment. Free of cryogenics and low-cost sensors called microfabricated optically pumped magnetometers (OPMs) have emerged as an alternate to cryogenic SQUID (Superconducting Quantum Interference Device) systems to record fMCG. Previous research has demonstrated the ability of the OPMs to measure the fMCG at different maternal positions by taking the advantage of the conformal and geometric flexibility of the sensors. In this work, we designed and configured a bed-based stand-alone array of OPMs to obtain serial recordings of fMCG. 72 combined OPM-SQUID recordings were conducted at different gestational ages in 22 pregnant women. We were able to obtain fMCG with similar detectability as the gold standard SQUID from OPM sensors mounted on a novel belly-shape patient interface design with movable sensor holders. While additional translational research is needed, the outcome of this study can further facilitate the development of a non-cryogenic low-cost smaller footprint device to increase the use of OPMs for fetal research and clinical applications.

Background Project ECHO has emerged as a leading telementoring modality for continuing medical education, particularly for providers practicing in rural and underserved areas with limited access to specialty care. The efficacy and utility of the ECHO model in healthcare training is well documented, though there is less literature focused on the determinants of ECHO implementation.Objective This study aims to assess facilitators and barriers to implementing the ECHO model.Methods We conducted virtual focus groups with eight Project ECHO implementation teams (n = 29 individuals) across the United States. Guided by the Consolidated Framework for Implementation Research (CFIR), focus groups explored experiences implementing the ECHO model and assessed facilitators and barriers to program uptake, delivery, and sustainability.Results Qualitative analysis revealed implementation determinants across CFIR levels. Participants recognized the advantage of ECHO’s virtual, learner-centric, case-based learning approach compared to other continuing medical education modalities. Participants recommended recruiting subject matter expert presenters with skills as educators and understanding of the ECHO model. Because of Project ECHO’s emphasis on case-based learning, participants highlighted the importance of balancing didactics with case presentations and discussion. Scheduling and finding time to participate was reported as a challenge for provider engagement, though most participants suggested that the length, frequency of sessions, and number of participants can be tailored for each program to accommodate needs. Providing CME credit and setting expectations for attendance and case presentation were said to improve provider engagement. Support and mentorship from the ECHO Institute was described as a facilitator in planning for ECHO implementation and delivery. Funding was reported as a barrier to sustainability.Conclusion By addressing barriers prior to implementing the ECHO model, future ECHOs can be tailored to leverage program resources, maximize attendance, expand reach, and ultimately improve outcomes.