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.

Smart Start - Designing Powerful Clinical Trials Using Pilot Study Data.

BACKGROUND: Digital health interventions may be optimized before evaluation in a randomized clinical trial. Although many digital health interventions are deployed in pilot studies, the data collected are rarely used to refine the intervention and the subsequent clinical trials. METHODS: We leverage natural variation in patients eligible for a digital health intervention in a remote patient-monitoring pilot study to design and compare interventions for a subsequent randomized clinical trial. RESULTS: Our approach leverages patient heterogeneity to identify an intervention with twice the estimated effect size of an unoptimized intervention. CONCLUSIONS: Optimizing an intervention and clinical trial based on pilot data may improve efficacy and increase the probability of success. (Funded by the National Institutes of Health and others; ClinicalTrials.gov number, NCT04336969.)

Equitable implementation of a precision digital health program for glucose management in individuals with newly diagnosed type 1 diabetes.

Few young people with type 1 diabetes (T1D) meet glucose targets. Continuous glucose monitoring improves glycemia, but access is not equitable. We prospectively assessed the impact of a systematic and equitable digital-health-team-based care program implementing tighter glucose targets (HbA1c < 7%), early technology use (continuous glucose monitoring starts <1 month after diagnosis) and remote patient monitoring on glycemia in young people with newly diagnosed T1D enrolled in the Teamwork, Targets, Technology, and Tight Control (4T Study 1). Primary outcome was HbA1c change from 4 to 12 months after diagnosis; the secondary outcome was achieving the HbA1c targets. The 4T Study 1 cohort (36.8% Hispanic and 35.3% publicly insured) had a mean HbA1c of 6.58%, 64% with HbA1c < 7% and mean time in the range (70-180 mg dl-1) of 68% at 1 year after diagnosis. Clinical implementation of the 4T Study 1 met the prespecified primary outcome and improved glycemia without unexpected serious adverse events. The strategies in the 4T Study 1 can be used to implement systematic and equitable care for individuals with T1D and translate to care for other chronic diseases. ClinicalTrials.gov registration: NCT04336969 .

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.

Disparities in Hemoglobin A1c Levels in the First Year After Diagnosis Among Youths With Type 1 Diabetes Offered Continuous Glucose Monitoring.

Importance: Continuous glucose monitoring (CGM) is associated with improvements in hemoglobin A1c (HbA1c) in youths with type 1 diabetes (T1D); however, youths from minoritized racial and ethnic groups and those with public insurance face greater barriers to CGM access. Early initiation of and access to CGM may reduce disparities in CGM uptake and improve diabetes outcomes. Objective: To determine whether HbA1c decreases differed by ethnicity and insurance status among a cohort of youths newly diagnosed with T1D and provided CGM. Design, Setting, and Participants: This cohort study used data from the Teamwork, Targets, Technology, and Tight Control (4T) study, a clinical research program that aims to initiate CGM within 1 month of T1D diagnosis. All youths with new-onset T1D diagnosed between July 25, 2018, and June 15, 2020, at Stanford Children's Hospital, a single-site, freestanding children's hospital in California, were approached to enroll in the Pilot-4T study and were followed for 12 months. Data analysis was performed and completed on June 3, 2022. Exposures: All eligible participants were offered CGM within 1 month of diabetes diagnosis. Main Outcomes and Measures: To assess HbA1c change over the study period, analyses were stratified by ethnicity (Hispanic vs non-Hispanic) or insurance status (public vs private) to compare the Pilot-4T cohort with a historical cohort of 272 youths diagnosed with T1D between June 1, 2014, and December 28, 2016. Results: The Pilot-4T cohort comprised 135 youths, with a median age of 9.7 years (IQR, 6.8-12.7 years) at diagnosis. There were 71 boys (52.6%) and 64 girls (47.4%). Based on self-report, participants' race was categorized as Asian or Pacific Islander (19 [14.1%]), White (62 [45.9%]), or other race (39 [28.9%]); race was missing or not reported for 15 participants (11.1%). Participants also self-reported their ethnicity as Hispanic (29 [21.5%]) or non-Hispanic (92 [68.1%]). A total of 104 participants (77.0%) had private insurance and 31 (23.0%) had public insurance. Compared with the historical cohort, similar reductions in HbA1c at 6, 9, and 12 months postdiagnosis were observed for Hispanic individuals (estimated difference, -0.26% [95% CI, -1.05% to 0.43%], -0.60% [-1.46% to 0.21%], and -0.15% [-1.48% to 0.80%]) and non-Hispanic individuals (estimated difference, -0.27% [95% CI, -0.62% to 0.10%], -0.50% [-0.81% to -0.11%], and -0.47% [-0.91% to 0.06%]) in the Pilot-4T cohort. Similar reductions in HbA1c at 6, 9, and 12 months postdiagnosis were also observed for publicly insured individuals (estimated difference, -0.52% [95% CI, -1.22% to 0.15%], -0.38% [-1.26% to 0.33%], and -0.57% [-2.08% to 0.74%]) and privately insured individuals (estimated difference, -0.34% [95% CI, -0.67% to 0.03%], -0.57% [-0.85% to -0.26%], and -0.43% [-0.85% to 0.01%]) in the Pilot-4T cohort. Hispanic youths in the Pilot-4T cohort had higher HbA1c at 6, 9, and 12 months postdiagnosis than non-Hispanic youths (estimated difference, 0.28% [95% CI, -0.46% to 0.86%], 0.63% [0.02% to 1.20%], and 1.39% [0.37% to 1.96%]), as did publicly insured youths compared with privately insured youths (estimated difference, 0.39% [95% CI, -0.23% to 0.99%], 0.95% [0.28% to 1.45%], and 1.16% [-0.09% to 2.13%]). Conclusions and Relevance: The findings of this cohort study suggest that CGM initiation soon after diagnosis is associated with similar improvements in HbA1c for Hispanic and non-Hispanic youths as well as for publicly and privately insured youths. These results further suggest that equitable access to CGM soon after T1D diagnosis may be a first step to improve HbA1c for all youths but is unlikely to eliminate disparities entirely. Trial Registration: ClinicalTrials.gov Identifier: NCT04336969.

The evolving role of data & safety monitoring boards for real-world clinical trials.

Introduction: Clinical trials provide the "gold standard" evidence for advancing the practice of medicine, even as they evolve to integrate real-world data sources. Modern clinical trials are increasingly incorporating real-world data sources - data not intended for research and often collected in free-living contexts. We refer to trials that incorporate real-world data sources as real-world trials. Such trials may have the potential to enhance the generalizability of findings, facilitate pragmatic study designs, and evaluate real-world effectiveness. However, key differences in the design, conduct, and implementation of real-world vs traditional trials have ramifications in data management that can threaten their desired rigor. Methods: Three examples of real-world trials that leverage different types of data sources - wearables, medical devices, and electronic health records are described. Key insights applicable to all three trials in their relationship to Data and Safety Monitoring Boards (DSMBs) are derived. Results: Insight and recommendations are given on four topic areas: A. Charge of the DSMB; B. Composition of the DSMB; C. Pre-launch Activities; and D. Post-launch Activities. We recommend stronger and additional focus on data integrity. Conclusions: Clinical trials can benefit from incorporating real-world data sources, potentially increasing the generalizability of findings and overall trial scale and efficiency. The data, however, present a level of informatic complexity that relies heavily on a robust data science infrastructure. The nature of monitoring the data and safety must evolve to adapt to new trial scenarios to protect the rigor of clinical trials.

A New Technology-Enabled Care Model for Pediatric Type 1 Diabetes.

In July 2018, pediatric type 1 diabetes (T1D) care at Stanford suffered many of the problems that plague U.S. health care. Patient outcomes lagged behind those of peer European nations, care was delivered primarily on a fixed cadence rather than as needed, continuous glucose monitors (CGMs) were largely unavailable for individuals with public insurance, and providers' primary access to CGM data was through long printouts. Stanford developed a new technology-enabled, telemedicine-based care model for patients with newly diagnosed T1D. They developed and deployed Timely Interventions for Diabetes Excellence (TIDE) to facilitate as-needed patient contact with the partially automated analysis of CGM data and used philanthropic funding to facilitate full access to CGM technology for publicly insured patients, for whom CGM is not readily available in California. A study of the use of CGM for patients with new-onset T1D (pilot Teamwork, Targets, and Technology for Tight Control [4T] study), which incorporated the use of TIDE, was associated with a 0.5%-point reduction in hemoglobin A1c compared with historical controls and an 86% reduction in screen time for providers reviewing patient data. Based on this initial success, Stanford expanded the use of TIDE to a total of 300 patients, including many outside the pilot 4T study, and made TIDE freely available as open-source software. Next steps include expanding the use of TIDE to support the care of approximately 1,000 patients, improving TIDE and the associated workflows to scale their use to more patients, incorporating data from additional sensors, and partnering with other institutions to facilitate their deployment of this care model.

Teamwork, Targets, Technology, and Tight Control in Newly Diagnosed Type 1 Diabetes: the Pilot 4T Study.

CONTEXT: Youth with type 1 diabetes (T1D) do not meet glycated hemoglobin A1c (HbA1c) targets. OBJECTIVE: This work aimed to assess HbA1c outcomes in children with new-onset T1D enrolled in the Teamwork, Targets, Technology and Tight Control (4T) Study. METHODS: HbA1c levels were compared between the 4T and historical cohorts. HbA1c differences between cohorts were estimated using locally estimated scatter plot smoothing (LOESS). The change from nadir HbA1c (month 4) to 12 months post diagnosis was estimated by cohort using a piecewise mixed-effects regression model accounting for age at diagnosis, sex, ethnicity, and insurance type. We recruited 135 youth with newly diagnosed T1D at Stanford Children's Health. Starting July 2018, all youth within the first month of T1D diagnosis were offered continuous glucose monitoring (CGM) initiation and remote CGM data review was added in March 2019. The main outcomes measure was HbA1c. RESULTS: HbA1c at 6, 9, and 12 months post diagnosis was lower in the 4T cohort than in the historic cohort (-0.54% to -0.52%, and -0.58%, respectively). Within the 4T cohort, HbA1c at 6, 9, and 12 months post diagnosis was lower in those patients with remote monitoring than those without (-0.14%, -0.18% to -0.14%, respectively). Multivariable regression analysis showed that the 4T cohort experienced a significantly lower increase in HbA1c between months 4 and 12 (P < .001). CONCLUSION: A technology-enabled, team-based approach to intensified new-onset education involving target setting, CGM initiation, and remote data review statistically significantly decreased HbA1c in youth with T1D 12 months post diagnosis.

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.