SaMD Diabetes Software Experts

The Rapidly Evolving Diabetes Technology Ecosystem

The diabetes technology ecosystem is growing at unprecedented rates with advanced technology that will help millions live longer, healthier lives. But sustaining this growth will require input from innovative software and medical device companies with a goal of creating interoperable solutions.

In 2015, Nathaniel D. Heintzman, Ph.D., and author of A Digital Ecosystem of Diabetes Data and Technology, painted an inspiring picture of the future of diabetes technology. He theorized that it would soon be possible for a person with diabetes to walk into a restaurant and instantly be provided recommendations on their smart device for what to order along with suggested dosing strategies and predicted postprandial glycemic responses. Mind you, this was back in a time when the first smartphone-connected CGM had just been launched.

Heintzman’s scenario of the future of the diabetes digital ecosystem has yet to be realized, but there is no doubt we are closer than ever to achieving it. 

Today, CGMs aren’t the only piece of diabetes tech that seamlessly communicates with smart personal devices. Pumps, pens, and biosensors of all kinds now work together with highly capable smartphone apps to help people with diabetes all over the world take better control of their health. 

In this article, we’ll take a look at the current state of the digital ecosystem of diabetes technology and how advancements in connected applications and software are changing the lives of people living with diabetes. We’ll also look at the opportunities for development that still exist and discuss what it will take to achieve Heintzman’s vision of the future and surpass his greatest expectations. 

What is the Diabetes Technology Ecosystem?

When we talk about technology ecosystems, we’re referring to a network of interconnected technologies with the ability to share and exchange data in order to work off and support one another. In the diabetes technology ecosystem, this tech consists of numerous devices, software, and applications that communicate data in order to improve quality of life and increase positive outcomes for those living with diabetes. 

In the beginning, much of this ecosystem revolved around collecting, transmitting, and interpreting physiological data. Today, sensors and applications also share and utilize behavioral and environmental information to create a more complete picture of the various factors that influence blood sugar trends.

The diabetes technology ecosystem of today is made up of glucose monitoring systems, insulin delivery systems, wearables, mobile medical apps, and algorithms that help interpret and visualize all of this data for the user and their care team.

The Current State of the Diabetes Technology Ecosystem

While the exact scenario that Heintzman described in his paper has not come to fruition, most of the technology needed to make it happen already exists. From highly advanced algorithms capable of predicting glycemic response to wearables that capture and share endless physiological data, the diabetes technology ecosystem has reached impressive heights in just eight short years.

Connected Glucose Monitoring Systems

Blood glucose meters, which have been a vital tool for managing diabetes since the 80s are now smart, connected devices with abilities that go beyond just measuring blood sugar. Almost all meters used today connect to smartphone apps that allow users to track their blood sugar trends over time. Many are also interoperable with insulin delivery systems, which saves time by automatically sending data to be considered for insulin dosing in real time.

Even more advanced are today’s continuous glucose monitors (CGMs). These wearable devices continually track blood sugar levels (every one to five minutes) to give users a complete picture of their blood glucose trends. This data is automatically shared with connected smartphone applications and platforms that create various means of visual representation. Many of these platforms are capable of analyzing the data and suggesting changes in basal and bolus needs to better control glucose levels. 

More impressive are the newest generations of CGMs which connect directly to insulin dosing systems and AI algorithms to automatically adjust insulin dosing in response to changing blood glucose levels. 

Most of the devices on the market today utilize sensors that must be inserted under the skin in order to read interstitial glucose levels. But the newest technology, which has yet to make it to market, utilizes multiple noninvasive sensors, including radio frequency and infrared stroke photoplethysmography to provide real-time blood glucose readings. These new-age sensors also have the ability to provide data on other physiological actions, such as heart rate, respiration, and galvanic skin response, all of which can be used as indicators of behavioral patterns that might affect blood sugar. This plethora of data is interpreted and shared with the user via a simple mobile application.

Insulin Delivery Systems

Far behind us are the days of boiling reusable glass syringes to dose insulin. Today, people with diabetes have various options for intelligent, connected insulin delivery systems.

The most basic of these systems is the smart pen. These easy-dosing insulin pens work with special casings or “smart buttons” to record dosing information, including the amount and timing of doses, and share it with a connected app. Most of these apps also have the ability to provide dosing reminders. Some interconnect with CGM applications to provide feedback on potential adjustments needed for more precise blood sugar control.

More complex are the insulin pump and pumpless system options. Today, there are a number of different insulin pumps to choose from. Most offer connectivity with a CGM and meter for automatic blood sugar data collection and many newer models work with a connected application for easy control without having to input commands into the device itself. Pumpless systems have always been app-connected and utilize a simple wearable insulin reservoir that is controlled by a connected smartphone app.

Many options of both types now utilize hybrid-closed loop technology that works with a connected CGM to automatically adjust insulin dosing based on current blood sugar levels. 

Ancillary Wearables

Because blood sugar is affected by far more than just carb intake and insulin dosing, many med tech companies are starting to incorporate ancillary wearables into their connected systems. These devices measure everything from heartbeat for detecting exercise to movement in order to pinpoint when a user might be eating. Many of these sensors are integrated with more typical diabetes devices using third-party applications, such as the Apple Health app. But a number of diabetes companies are working on launching their own integrated software and connected devices.

Mobile Medical Apps

The first connected devices worked with receivers that provided users with minimal data while the bulk of the collected metrics were sent to a platform for their care team to view. Today’s devices share their data with user-friendly medical mobile applications (MMAs) with a multitude of features. For many users, the ease of use and functionality of the mobile app is what determines which device they choose.

Equally important is the interoperability of the MMA. Not only do highly interoperable apps reduce how many separate applications a user needs, but they provide integrated data for a more comprehensive picture of the user’s blood sugar control. More companies than ever are entering into partnerships in order to create devices, applications, and software that work together for the benefit of the user. In order to survive in what is certain to be a highly interoperable future, all companies need to be focused on creating agile software that lends itself to easy integration into various types of applications and platforms.

Algorithms 

MMAs are the environment where the various devices of the diabetes technology ecosystem gather. But it is the algorithms used within these applications, and sometimes within devices themselves, that create the true value of this tech ecosystem. 

Today’s algorithms are smarter than ever. They not only analyze incoming data to create meaningful visual representations for the user, but they turn that data into actionable insights that the devices themselves can operate off of. As the interoperability of devices increases, so must the intelligence of the algorithms that are used to process multiple different inputs to create a single coherent picture. We are just now beginning to see these highly advanced algorithms being used in MMAs that integrate pumps, CGMs, and ancillary wearables into a single system.

Opportunities within the Diabetes Technology Ecosystem

Given all the technology currently floating around in the diabetes technology ecosystem, it’s a wonder we haven’t fulfilled Heintzman’s future vision. The truth is, much of the technology needed to do it existed back in 2015 and that tech has only gotten more advanced over the past eight years. So what is missing? 

The answer is integration.

The devices and wearables necessary for gathering data on all aspects of behavior and physiology that affect blood glucose already exist. The highly advanced algorithms capable of interpreting that data into actionable information are out there. What is missing is the technology that would bring all these separate parts of the diabetes ecosystem together to create a single application that could interpret menu items, current user state, and past data patterns to provide dosing strategies and their associated postprandial glycemic response predictions. 

In a very general sense, widespread integration is the missing key in the diabetes tech ecosystem. But there are also some specific versions of this idea that present tangible opportunities for innovative diabetes tech companies to capitalize on. 

Behavior Integration

From heart rate variability and blood oxygen levels to pupil dilation and the sodium content of perspiration, there is a wearable out there to measure just about any physiological activity. What the diabetes tech ecosystem is missing are applications that could integrate all this data and algorithms to interpret it in terms of blood sugar response.

Just as important will be developing multi-sensors that can collect this behavioral data using one or two wearable devices. What form these devices take—watch, smart clothing, contact lens, or other—matters less than the fact that users will gravitate toward products that reduce how many devices they must purchase and how many apps they have to download to use them. The obvious choice would be to integrate these extra sensors into existing automated insulin dosing systems (AID) using both the pump and the CGM to harbor extra sensors.

Geospatial Interaction

While many diabetes tech companies are looking into ways to utilize additional behavioral and physiological feedback, far fewer are looking to integrate geospatial data. But where a person is at any given time can also impact their blood sugar. This is true in terms of physical space—elevation, climate, weather—and in terms of actual location—school, doctor, work, gym, etc.

Adding GPS capabilities to any wearable would be easy. The trick is developing an algorithm that could interpret the data to find trends in blood sugar caused by changes in weather, location, and more. 

Emergency Detection

The newest Apple Watch series has the ability to sense whether the wearer has passed out and alert emergency services to their exact location. It’s no surprise this technology exists, given the advancements in physiologic and gyroscopic sensors. What is surprising is that a consumer good was the first to utilize this functionality. 

As many as 1 in 10 type 1 diabetics will die of hypoglycemia(1). Every insulin pump and CGM out there should include emergency detection technology and the ability to alert medical personnel if a user’s low blood sugar is combined with indications of a fall or unconsciousness. Filling this gap in the diabetes ecosystem is not just an amazing opportunity, it’s imperative. 

Interoperability Is the Future of the Diabetes Tech Ecosystem

Heintzman very accurately proclaimed in his diabetes digital ecosystem paper that advancements in this technology will revolutionize the healthcare industry by making diagnosis, treatment, and prevention widely accessible at a fraction of current costs. He went on to say that the greatest impact of these advancements would be on the lives of people living with chronic diseases(2).

There is no doubt that the digital technology ecosystem will continue to grow rapidly throughout the healthcare industry. But the impacts of that growth will be felt the most by people living with diabetes and other chronic diseases.

In order to take advantage of the many opportunities that exist within the diabetes technology ecosystem and to help better the lives of people living with this condition, device and software companies need to put connectivity and interoperability first when designing any new technology. 

Sequenex, the leader in software and SaMD for the diabetes and connected devices markets, is here to help you do just that. We design, develop and sustain software systems that are purpose-built for innovation, connectivity, and interoperability. And we are here to be your knowledgeable and experienced partner to help your company become a permanent fixture in the diabetes tech ecosystem. Contact us today to find out more about what we can help you accomplish.

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