Future blast: a digital diabetes ecosystem

“Every once in a while, a new technology, an old problem, and a big idea turn into an innovation.” (Dean Kamen)
 
Einstein once famously suggested that to keep doing the same thing and expect different results is the definition of insanity. In our world of diabetes we seem to be guilty of this by failing to appreciate that new ways of delivering care are necessary if we want to make progress. We already know that it can be a very lonely experience for people with diabetes as, for the vast majority of their time awake and asleep, they are not interacting with healthcare professionals (Schatz et al, 2016). Unsurprisingly, the results we are seeing, even in the better-quality clinics, are underwhelming. For example, the most recent data from the Type 1 Exchange in the US suggests that average achieved HbA_1c levels are not improving, and in younger individuals they may actually be getting worse. In the type 2 diabetes space, the number of people developing the condition continues to rise, indicating a failure of population-level initiatives aimed at preventing diabetes in the first place. We are also very good at wasting money when it comes to spending on health (Berwick and Hackbarth, 2012).

Changing behaviour is difficult but surely not impossible. In diabetes, the catalyst for change in the near future is going to be from having easier access to novel, innovative technologies that provide clinically meaningful metrics of success, including (a) closed-loop systems for type 1 diabetes (the artificial pancreas); (b) continuous glucose monitoring in type 2 diabetes; (c) smart insulin pens that record the time and dose of insulin injections; (d) smartphone applications and other digital health products; and (e) the use of big data and artificial intelligence (Kerr et al, 2018).

There are a number of reasons why technology and data could provide opportunities for beneficial changes in diabetes care. For example, on a daily basis, personal data from people living with diabetes are continuously created and logged. Although the main variable of interest is glucose, with the rise in consumer tracking technologies, glucose data are being supplemented with additional information related to nutrition, physical activity and sleep. Furthermore, with the increasing availability of additional sensor technologies for physiological monitoring, as well as social media and records of internet searches, the diabetes data pool will continue to grow.

However, technology is not infallible, and there need to be improvements in the user interface and experience with devices if we are to avoid creating a digital divide. Big data and artificial intelligence will be useful tools for treating diabetes in precision medicine or precision public health paradigms. However, factors besides objective data also go into personal decision-making, such as sentiment, intuition, and experience. Computing alone is currently ill-equipped to duplicate this subjective part of reaching medical conclusions. There remains a need for humans to also be involved in treating diabetes by providing judgement, compassion and context (Kerr and Klonoff, 2018).

At Diabetes Digest we understand the requirement to not only appreciate the potential for people with diabetes, clinicians and other stakeholders to benefit from technological innovation, but also, at the same time, make sure that we do not become “slaves to the machine”. To help, we are creating this new educational platform that aims to disseminate the latest knowledge about devices and diabetes, and to put this in a clinically relevant context. For this we are indebted to Peter Hammond, who will resume his role as Clinical Editor and oversee the project.

We welcome your views and feedback on the value, or otherwise, of the impending technological revolution in diabetes care. The British computer scientist, Alan Turing, once famously asked the question “can machines think?”. In 2018, we would like to take this a stage further and ask “can people help machines to help people think?”.

Click the links below to access the latest Diabetes Digests related to devices and technology:

• PRECISE II: Accuracy of the new Eversense implantable CGM system
• Accuracy of the 18 most commonly used glucose meters in the US
• Improving hypoglycaemia unawareness: 2-year follow up of the HypoCOMPaSS trial
• Day-and-night closed-loop insulin delivery in pregnancy

References
Berwick DM, Hackbarth AD (2012) Eliminating waste in US health care. JAMA 307: 1513–6
Kerr D, Klonoff DC (2018) Digital diabetes data and artificial intelligence: a time for humility not hubris. J Diabetes Sci Technol 5 Sep [Epub ahead of print]. doi: 10.1177/1932296818796508
Kerr D, Axelrod C, Hoppe C, Klonoff DC (2018) Diabetes and technology in 2030: a utopian or dystopian future? Diabet Med 35: 498–503
Schatz D (2016) 2016 Presidential Address: Diabetes at 212° – confronting the invisible disease. Diabetes Care 39: 1657–63