By Stephen Starkie, Group Lead - Software
Sadly, most industries are plagued with buzz words and jargon, and ours is one of the worst. In part one of this this two-part series, I will try to cut through the jargon and describe what Digital Health is and how to approach it. In the second instalment, I will describe how to approach developing a Digital Health product and what to consider on the route to market.
As eg technology works mainly in Medical Devices, we apply engineering to medicine. Both are vast subjects that are not usually studied together, though in recent years Biomedical Engineering courses have sprung up at some of the top Universities. These are general engineering courses in which students can specialise in one of the traditional disciplines (mechanical, electronics, or software engineering) while applying their studies to projects in healthcare and with background courses in medical subjects. Like the medical device industry, Universities have realised the huge benefits at the crossover, and we have hired several graduates from these courses in different disciplines. Because people are not often familiar with both medicine and engineering it can be a very confusing space, and often our clients come from neither – many have had a good idea born in an entirely different field.
Cutting through the jargon
Unfortunately, a quick search of the web will reveal reams of infographics and concept clouds, which just add to the confusion. Meaningless phrases like Gamification Based Wellness, Super-Convergence and Data Universe and acronyms like SaMD, EMC, IoT, EMR, HIS and PP are just some examples of what you might come across. For the moment, I will leave these terms hanging – but they will be revealed, as I go through what digital health means to us at eg technology. My hope is that by the end of this article you will have a clearer idea of what Digital Health means, what you need to worry about and how to navigate through the pain.
To cut through all this jargon, at eg technology, digital health is really just the application of recent advances in technology, particularly communications and software, to medicine and healthcare. Put this way it can be made much more familiar. The advances that matter include the rise of the smartphone and related technologies such as smart watches and tablets, the continuing rise in the power and decrease in size of microprocessors, the speed of both wired and wireless or mobile networks, and our ability to store and process even larger amounts of data. This usually means “the Cloud”, which is an odd term for time and storage on servers rented in whole or part from Microsoft (Azure), Amazon (AWS and EC2), or Google (Google Cloud Platform). They provide a vast range of products from simple web services and databases to massively parallel high-performance computing to Artificial Intelligence- and Machine Learning-optimised platforms and even access to Quantum Computers. There are other providers and you can even host your own servers – but it is becoming standard to use one of the big three.
To make matters worse, the field changes all the time and solutions that made sense five years ago are already being superseded. At one time, a device such as a blood-glucose monitor, would have been unconnected, as its benefit was purely in its diagnostic or therapeutic power, and that would have been as far as it went.
More recently, a device might have included some kind of WiFi or Bluetooth connection to a PC or smartphone because people realised that consumer devices can present the data well and storing or transmitting the data was becoming important and leads to fewer transcription errors.
Cutting edge devices now have their own fifth generation (5G) mobile data connection to the internet and upload their measurements directly to a (cloud) server where algorithms are used to make a diagnosis and from which the patient or clinician can access the results from a web page or app. This is where some of the acronyms start to be used – IoT, or Internet of Things, really just describes connected devices – particularly those where they are always connected and the user doesn’t have to do anything special to use them. How users see their results is sometimes called a patient portal (PP); really it’s a web page, app, email or notification tailored to the type of user – whether they are a patient who wants a simple indication or a clinician who needs a detailed report. When this appears in a Hospital Information System it is often called an Electronic Medical Record or EMR and you may hear of HL7 (Health Level 7) or FHIR (Fast Healthcare Interoperability Resources) which are standards for records in hospital information systems. If you have been unfortunate enough to have had a test for Covid-19 you have used the NHS patient portal (even if you booked by telephone!). Connected devices gain their power often not from the individual measurement or algorithm, but from gathering massive amounts of data from everyone who uses the system and learning from all the measurements. This is what people mean by Super-Convergence and a Data Universe and it is also where the power of Machine Learning and Artificial Intelligence come into play.
The next stage is to fit devices to patients (or even implant them), so that they can monitor physiological parameters and make therapeutic decisions directly. Everyone wearing a later generation Apple Watch is already being monitored for heart conditions. Where the device can also apply a therapy is the scary end of medical devices and there are some very important security and safety concerns, where a device is making a ‘decision’ rather than a medical professional. For instance, there are ‘open-source’ designs for insulin pumps that monitor blood glucose levels and could inject a diabetes sufferer with a potentially fatal dose. Equally, imagine having your internet-connected pacemaker hacked.
Regulating Digital Health
Regulators are fully aware of these advances and the next versions of the standards for software in medical devices and health systems have been amalgamated to cover this convergence. The latest pre-release of IEC 62304, which used to only cover medical devices has recently been drafted and it now also covers health systems that used to be a separate standard (IEC 82304). Medical devices fall into different categories depending on the level of harm they could cause and, considered separately, the level to which software can influence this. Another confusion can be the regulation of pure software systems, but again, it all falls under the same umbrellas; where software is used in healthcare it is considered to be Software as a Medical Device (SaMD) and falls under exactly the same standards, as if it were embedded in a gadget.
I hope this article has given you some idea of what Digital Health is and helped to get through some of the confusion that surrounds the term. In the next part, I will look at some considerations that are often missed in developing a digital health product, but that may be critical to the product or services success.
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