COVID-19 threatens to overwhelm hospital facilities throughout the United States. We created an interactive, quantitative model that forecasts demand for COVID-19 related hospitalization based on county-level population characteristics, data from the literature on COVID-19, and data from online repositories. Using this information as well as user inputs, the model estimates a time series of demand for intensive care beds and acute care beds as well as the availability of those beds. The online model is designed to be intuitive and interactive so that local leaders with limited technical or epidemiological expertise may make decisions based on a variety of scenarios. This complements high-level models designed for public consumption and technically sophisticated models designed for use by epidemiologists. The model is actively being used by several academic medical centers and policy makers, and we believe that broader access will continue to aid community and hospital leaders in their response to COVID-19.

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.

BACKGROUND Continuous glucose monitors (CGM) are recommended as standard of care by the American Diabetes Association for individuals with type 1 diabetes on insulin. These devices generate glucose readings every 5-15 minutes and use cloud-based platforms to share data. This remotely reviewed data can be used by members of diabetes care team to provide remote care. OBJECTIVE To design an automated tool that facilitates timely, personalized, population-level guidance for glucose management through asynchronous telehealth. METHODS Using CGM data from six clinical trials and two observational datasets, we developed manufacturer-agnostic algorithms to generate generic (e.g., mean glucose (MG) > 170mg/dL) and personalized (e.g., MG increased by >10mg/dL) flags. We developed and deployed an automated tool in a pediatric type 1 diabetes clinic, measured sensitivity for identifying who may benefit from telehealth, and measured the time saved reviewing data with the use of the tool. RESULTS The eight cohorts contained 1,365 patients with 30,017 weeks of data collected by seven types of CGMs. In the cohort with the highest MG, 81.3% (26 of 32) and 3.1% (1/32) of people had a generic and personalized flag every week, respectively. In the clinic, on average, 57.2% of patients were flagged per week, corresponding to a sensitivity of 98.6% and a 42.8% reduction in the time required to review data. CONCLUSIONS The automated analysis of CGM data may help identify people requiring guidance on glucose management while reducing the workload for care providers. The rules-based approach provided fully interpretable representations of patient status relative to the latest guidelines. When deployed in a clinic, an automated tool to generate flags identified 98.6% of patients who would benefit from asynchronous telehealth contact while reducing the time required to review patient data by 42.8%. Guideline-based population health management may become more accessible through the use of automated tools.

Objective Early initiation of continuous glucose monitoring (CGM) is advocated for youth with type 1 diabetes (T1D). Data to guide CGM use on time-in-range (TIR), hypoglycemia, and the role of partial clinical remission (PCR) are limited. Our aims were to assess whether: 1) an association between increased TIR and hypoglycemia exists, and 2) how time in hypoglycemia varies by PCR status. Methods We analyzed 80 youth who were started on CGM shortly after T1D diagnosis and were followed for up to 1-year post-diagnosis. TIR and hypoglycemia rates were determined by CGM data and retrospectively analyzed. PCR was defined as (visit-HbA1c)+(4*units/kg/day) <9. Results Youth were started on CGM 8.0 (IQR 6.0-13.0) days post-diagnosis. Time spent <70mg/dL remained low despite changes in TIR (highest TIR 74.6±16.7%, 2.4±2.4% hypoglycemia at 1 month post-diagnosis; lowest TIR 61.3±20.3%, 2.1±2.7% hypoglycemia at 12 months post-diagnosis). No events of severe hypoglycemia occurred. Hypoglycemia was rare and there was minimal difference for PCR versus non-PCR youth (54-70mg/dL: 1.8% vs 1.2%, p=0.04; <54mg/dL: 0.3% vs 0.3%, p=0.55). Approximately 50% of the time spent in hypoglycemia was in the 65-70mg/dL range. Conclusions As TIR gradually decreased over 12 months post-diagnosis, hypoglycemia was limited with no episodes of severe hypoglycemia. Hypoglycemia rates did not vary in a clinically meaningful manner by PCR status. With CGM being started earlier, consideration needs to be given to modifying CGM hypoglycemia education, including alarm settings. These data support a trial in the year post-diagnosis to determine alarm thresholds for youth who wear CGM.

Background The use of continuous glucose monitors (CGMs) is recommended as the standard of care by the American Diabetes Association for individuals with type 1 diabetes (T1D). Few hardware-agnostic, open-source, whole-population tools are available to facilitate the use of CGM data by clinicians such as physicians and certified diabetes educators. Objective This study aimed to develop a tool that identifies patients appropriate for contact using an asynchronous message through electronic medical records while minimizing the number of patients reviewed by a certified diabetes educator or physician using the tool. Methods We used consensus guidelines to develop timely interventions for diabetes excellence (TIDE), an open-source hardware-agnostic tool to analyze CGM data to identify patients with deteriorating glucose control by generating generic flags (eg, mean glucose [MG] >170 mg/dL) and personalized flags (eg, MG increased by >10 mg/dL). In a prospective 7-week study in a pediatric T1D clinic, we measured the sensitivity of TIDE in identifying patients appropriate for contact and the number of patients reviewed. We simulated measures of the workload generated by TIDE, including the average number of time in range (TIR) flags per patient per review period, on a convenience sample of eight external data sets, 6 from clinical trials and 2 donated by research foundations. Results Over the 7 weeks of evaluation, the clinical population increased from 56 to 64 patients. The mean sensitivity was 99% (242/245; SD 2.5%), and the mean reduction in the number of patients reviewed was 42.6% (182/427; SD 10.9%). The 8 external data sets contained 1365 patients with 30,017 weeks of data collected by 7 types of CGMs. The rates of generic and personalized TIR flags per patient per review period were, respectively, 0.15 and 0.12 in the data set with the lowest average MG (141 mg/dL) and 0.95 and 0.22 in the data set with the highest average MG (207 mg/dL). Conclusions TIDE is an open-source hardware-agnostic tool for personalized analysis of CGM data at the clinical population scale. In a pediatric T1D clinic, TIDE identified 99% of patients appropriate for contact using an asynchronous message through electronic medical records while reducing the number of patients reviewed by certified diabetes care and education specialists by 43%. For each of the 8 external data sets, simulation of the use of TIDE produced fewer than 0.25 personalized TIR flags per patient per review period. The use of TIDE to support telemedicine-based T1D care may facilitate sensitive and efficient guideline-based population health management.