Six Digital Health Technologies That Will Transform Diabetes

Abstract

The digital health revolution is transforming the landscape of medicine through innovations in sensor data, software, and wireless communication tools. As one of the most prevalent chronic diseases in the United States, diabetes is particularly impactful as a model disease for which to apply innovation. As with any other newly developed technologies, there are three key questions to consider: 1) How can the technology benefit people with diabetes?, 2) What barriers must be overcome to further advance the technology?, and 3) How will the technology be applied in the future?. In this article, we highlight six areas of innovation that have the potential to reduce the burden of diabetes for individuals living with the condition and their families as well as provide measurable benefits for all stakeholders involved in diabetes care. The six technologies which have the potential to transform diabetes care are (i) telehealth, (ii) incorporation of diabetes digital data into the electronic health record, (iii) qualitative hypoglycemia alarms, (iv) artificial intelligence, (v) cybersecurity of diabetes devices, and (vi) diabetes registries. To be successful, a new digital health technology must be accessible and affordable. Furthermore, the people and communities that would most likely benefit from the technology must be willing to use the innovation in their management of diabetes.

‘Much more convenient, just as effective’: Experiences of starting continuous glucose monitoring remotely following Type 1 diabetes diagnosis

“…Findings from this study are in line with recent literature demonstrating the acceptability and benefits of telehealth-based delivery of diabetes care, which is increasingly becoming a recommended avenue to increase access to care, increase frequency of visits while minimizing travel and time burden, and to monitor patients remotely between visits. [19][20][21][22] The SARS-CoV-2 pandemic led to a rapid acceleration of expanding the use of telehealth-based care 23,24 and highlighted its potential as major paradigm shift in diabetes care 25,26 as well as some challenges (e.g., risk of widening disparities and gaps in access particularly with rural populations; concern about less personalized care over time; lower rates of anthropometric and lab measurements). [27][28][29] Recent research has demonstrated that most patients and providers have been satisfied with telehealth-based diabetes care delivery.…”

The Need for Data Standards and Implementation Policies to Integrate CGM Data into the Electronic Health Record

“…2 This data can be collected, transmitted, presented, stored, and processed in real time to be used in decision support software or algorithms for an HCP to review in real time or asynchronously. 3 To be most useful, the data must integrate into a patient’s electronic health record (EHR). 4…”

“…2 This data can be collected, transmitted, presented, stored, and processed in real time to be used in decision support software or algorithms for an HCP to review in real time or asynchronously. 3 To be most useful, the data must integrate into a patient's electronic health record (EHR). 4 Currently, very few healthcare organizations (HCOs) have successfully integrated continuous glucose monitor (CGM) data directly into the EHR, and most of those have done so with the assistance of third-party integration engines or data aggregators.…”

Regarding “Detection of Hypoglycemia and Hyperglycemia Using Noninvasive Wearable Sensors: ECG and Accelerometry”

“…Based on the known physiologic responses to a low blood glucose level, this novel approach could be used to qualitatively and accurately detect the onset of clinically significant hypoglycemia. 2 We commend Dave and colleagues for their work. However, we would like to point out two additional factors to consider in any discussion of technology for qualitative detection of hypoglycemia.…”

“…Finally, we believe that greater accuracy of hypoglycemia detection can be achieved through incorporating multiple data streams into an aggregate multiple sensor-data platform, 2 rather than relying on information from a single sensor. Dave et al created their model by modifying ECG data with contextual information provided by an accelerometry sensor.…”