Objective To rapidly deploy a digital patient-facing self-triage and self-scheduling tool in a large academic health system to address the COVID-19 pandemic. Materials and Methods We created a patient portal-based COVID-19 self-triage and self-scheduling tool and made it available to all primary care patients at the University of California, San Francisco Health, a large academic health system. Asymptomatic patients were asked about exposure history and were then provided relevant information. Symptomatic patients were triaged into 1 of 4 categories—emergent, urgent, nonurgent, or self-care—and then connected with the appropriate level of care via direct scheduling or telephone hotline. Results This self-triage and self-scheduling tool was designed and implemented in under 2 weeks. During the first 16 days of use, it was completed 1129 times by 950 unique patients. Of completed sessions, 315 (28%) were by asymptomatic patients, and 814 (72%) were by symptomatic patients. Symptomatic patient triage dispositions were as follows: 193 emergent (24%), 193 urgent (24%), 99 nonurgent (12%), 329 self-care (40%). Sensitivity for detecting emergency-level care was 87.5% (95% CI 61.7–98.5%). Discussion This self-triage and self-scheduling tool has been widely used by patients and is being rapidly expanded to other populations and health systems. The tool has recommended emergency-level care with high sensitivity, and decreased triage time for patients with less severe illness. The data suggests it also prevents unnecessary triage messages, phone calls, and in-person visits. Conclusion Patient self-triage tools integrated into electronic health record systems have the potential to greatly improve triage efficiency and prevent unnecessary visits during the COVID-19 pandemic.

SUMMARY The screening of healthcare workers for COVID-19 symptoms and exposures prior to every clinical shift is important for preventing nosocomial spread of infection but creates a major logistical challenge. To make the screening process simple and efficient, UCSF Health designed and implemented a digital chatbot-based workflow. Within one week of forming a team, we conducted a product development sprint and deployed the digital screening process. In the first two months of use, over 270,000 digital screens have been conducted. This process has reduced wait times for employees entering our hospitals during shift changes, allowed for physical distancing at hospital entrances, prevented higher-risk individuals from coming to work, and provided our healthcare leaders with robust, real-time data for make staffing decisions.

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Background: In type 1 diabetes (T1D), periodic review of blood glucose and insulin dosing should be performed, but it is not known how often patients review these data on their own. We describe the proportion of patients with T1D who routinely downloaded and reviewed their data at home.Materials and Methods: A cross-sectional survey of 155 adults and 185 caregivers of children with T1D at a single academic institution was performed. “Routine Downloaders” (downloaded four or more times in the past year) were also considered “Routine Reviewers” if they reviewed their data most of the time they downloaded from devices. Logistic regression was used to identify factors associated with being a Routine Reviewer.Results: Only 31% of adults and 56% of caregivers reported ever downloading data from one or more devices, whereas 20% and 40%, respectively, were considered Routine Downloaders. Only 12% of adults and 27% of caregivers were Routine Reviewers. Mean hemoglobin A1c was lower in Routine Reviewers compared with non-Routine Reviewers (7.2±1.0% vs. 8.1±1.6% [P=0.03] in adults and 7.8±1.4% vs. 8.6±1.7% [P=0.001] in children). In adjusted analysis of adults, the odds ratio of being a Routine Reviewer of one or more devices for every 10-year increase in age was 1.5 (95% confidence interval, 1.1, 2.1 [P=0.02]). For every 10 years since diabetes diagnosis, the odds ratio of being a Routine Reviewer was 1.7 (95% confidence interval, 1.2, 2.4 [P=0.01]). For caregivers, there were no statistically significant factors associated with being a Routine Reviewer.Conclusions: A minority of T1D patients routinely downloads and reviews data from their devices on their own. Further research is needed to understand obstacles, provide better education and tools for self-review, and determine if patient self-review is associated with improved glycemic control.

Objective Develop a device-agnostic cloud platform to host diabetes device data and catalyze an ecosystem of software innovation for type 1 diabetes (T1D) management.Materials and Methods An interdisciplinary team decided to establish a nonprofit company, Tidepool, and build open-source software.Results Through a user-centered design process, the authors created a software platform, the Tidepool Platform, to upload and host T1D device data in an integrated, device-agnostic fashion, as well as an application (“app”), Blip, to visualize the data. Tidepool’s software utilizes the principles of modular components, modern web design including REST APIs and JavaScript, cloud computing, agile development methodology, and robust privacy and security.Discussion By consolidating the currently scattered and siloed T1D device data ecosystem into one open platform, Tidepool can improve access to the data and enable new possibilities and efficiencies in T1D clinical care and research. The Tidepool Platform decouples diabetes apps from diabetes devices, allowing software developers to build innovative apps without requiring them to design a unique back-end (e.g., database and security) or unique ways of ingesting device data. It allows people with T1D to choose to use any preferred app regardless of which device(s) they use.Conclusion The authors believe that the Tidepool Platform can solve two current problems in the T1D device landscape: 1) limited access to T1D device data and 2) poor interoperability of data from different devices. If proven effective, Tidepool’s open source, cloud model for health data interoperability is applicable to other healthcare use cases.

Diabetes management is well suited to use of telehealth, and recent improvements in both diabetes technology and telehealth policy make this an ideal time for diabetes providers to begin integrating telehealth into their practices. This article provides background information, specific recommendations for effective implementation, and a vision for the future landscape of telehealth within diabetes care to guide interested providers and practices on this topic.Note: This article was written prior to the COVID19 pandemic, and does not include information about recent telehealth policy changes that occurred during or as a result of this public health crisis.

Background: During the COVID-19 pandemic, telemedicine use rapidly and dramatically increased for management of diabetes mellitus. It is unknown whether access to telemedicine care has been equitable during this time. This study aimed to identify patient-level factors associated with adoption of telemedicine for subspecialty diabetes care during the pandemic. Methods: We conducted an explanatory sequential mixed-methods study using data from a single academic medical center. We used multivariate logistic regression to explore associations between telemedicine use and demographic factors for patients receiving subspecialty diabetes care between March 19 and June 30, 2020. We then surveyed a sample of patients who received in-person care to understand why these patients did not use telemedicine. Results: Among 1292 patients who received subspecialty diabetes care during the study period, those over age 65 were less likely to use telemedicine (OR: 0.34, 95% CI: 0.22-0.52, P < .001), as were patients with a primary language other than English (OR: 0.53, 95% CI: 0.31-0.91, P = .02), and patients with public insurance (OR: 0.64, 95% CI: 0.49-0.84, P = .001). Perceived quality of care and technological barriers were the most common reasons cited for choosing in-person care during the pandemic. Conclusions: Our findings suggest that, amidst the COVID-19 pandemic, there have been disparities in telemedicine use by age, language, and insurance for patients with diabetes mellitus. We anticipate telemedicine will continue to be an important care modality for chronic conditions in the years ahead. Significant work must therefore be done to ensure that telemedicine services do not introduce or widen population health disparities.

Background: A novel software application, Blip, was created to combine and display diabetes data from multiple devices in a uniform, user-friendly manner. The objective of this study was to test the usability of this application by adults and caregivers of children with type 1 diabetes (T1D). Methods: Patients (n = 35) and caregivers of children with T1D (n = 30) using an insulin pump for >1 year ± CGM were given access to the software for 3 months. Diabetes management practices and the use of diabetes data were assessed at baseline and at study end, and feedback was gathered in a concluding questionnaire. Results: At baseline, 97% of participants agreed it was important for patients to know how to interpret glucose data. Most felt that clinicians and patients should share the tasks of reviewing data, finding patterns, and making changes to their insulin plans. However, despite valuing shared responsibility, at baseline, 43% of participants never downloaded pump data, and only 9% did so at least once per month. At study end, 72% downloaded data at least once during the 3-month study, and 38% downloaded at least once per month. Regarding the software application, participants liked the central repository of data and the user interface. Suggestions included providing tools for understanding and interpreting glucose patterns, an easier uploading process, and access with mobile devices. Conclusions: Collaboration between developers and researchers prompted iterative, rapid development of data visualization software and improvements in the uploading process and user interface, which facilitates clinical integration and future clinical studies.