The sheer amount of press releases, news articles, and corporate announcements on digital health technologies is overwhelming. You see dozens every day, and they all sound grandiose and important – obviously.
But how can you separate the wheat from the chaff? How to decide if the given report is about something important, about a technology or method that might become really significant? This is what we try to do with the team of The Medical Futurist by providing context, but the goal is that you can do it as well.
We collected a six-question checklist to help with this. The method is simple: if the answer is yes to any 2 of these questions, the announcement is in line with the digital health trends and directions, so it is worth keeping an eye on it.
1. Why was it made? Is this addressing real clinical or patient needs?
The first question you need to answer is why a given technology was developed. One of the most typical shortcomings of new “digital health” technologies is the lack of focus on practical benefits.
This affects startups and major players alike. Do you remember when NASA announced ‘holoporting’ the first doctor to the International Space Station? The answer is brilliant, but man, what was the question? How is that a better solution than having a video call between the astronaut and the flight surgeon? Having a doctor who actually can’t see the patient, and a patient who is unable to establish eye contact with the doctor seems to entirely miss the point.
When reading about new technology, always examine if it was only developed because it was possible (like Google Glass), or is it something that meets clinical or patient needs (like aFib detection)? Keep an eye on new technologies that are addressing an existing pain point of healthcare professionals or patients.
Before we go along, here is one thing many of you will ask. We said only two of the six answers have to be yes to qualify as meaningful digital health tech, which implies that some solutions don’t have to address clinical/patient needs and still be noteworthy. How does it make sense?
Actually, it is possible for any scenario for which there is no demand yet, but will be later – and the underlying technology needs to be developed today. There is no demand for A.I. analysis of data provided by an at-home full-genome sequencing kit now, but I am sure there will be at some point in the future.
2. Are there studies and clinical trials to back those claims?
It is always worth taking a look at how much evidence there is about any given method. Can they provide peer-reviewed studies and clinical trials to back their claims? You typically don’t have to dig deep to answer this question: anyone who has scientific verification will proudly publish it.
Looking for digital health technologies like the study on the Kardia 6-lead device or the data on sepsis-watch A.I. will quickly lead you to studies and trials, be careful if you find none.
3. Do both patients and clinicians gain information?
A principle definition of any digital health technology is that it provides relevant health information to the patient and the clinician as well. Anything outside this realm is not proper digital health. Technologies accessible exclusively to doctors are either medical tech (an equally important, but totally different category) or a poorly designed digital health initiative, missing a crucial point.
The best current examples of digital health applications are giving relevant extra knowledge to both parties. You don’t have to look for anything special, simple examples illustrate this principle.
Your smartwatch, checking your heart rate throughout the day, every day provides you with data you never had before, data, that might become relevant to your general practitioner. Your doctor, who had checked your heart rate maybe once or twice a year before can now quickly look at a continuous stream of data, collected for months or years.
4. Is this helping to form a better relationship between patients and healthcare professionals?
Digital health is a road leading us toward a more balanced partnership between the patient and the clinician, where both parties add value to health management. Thus you should always take a look at how the technology in question addresses this relationship: is this helping the partnership or will just alienate the parties?
Good examples are like voice-to-text applications, which allow healthcare professionals to focus on the patient instead of spending time with the administration of the appointment.
Any technology that takes over non-essential tasks from doctors and nurses allowing them to practice empathy, and patient-focused care is helping the relationship. Similarly, remote monitoring models can vastly improve patients’ comfort and reduce anxiety, offering a simple and convenient way to make sure no medical intervention is needed and creating a platform to discuss questions without in-person appointments.
5. Can we use our existing devices to access the new features?
New technologies like to flow in direction of the least possible resistance. We could endlessly list incredible technologies and initiatives that have great potential but to become mainstream, they would require significant investment either from the healthcare system (the reason we don’t have 3D printed casts in every hospital), the physician (the reason why we don’t see a vein scanner at every GP’s surgery) or the patient (the reason why tens of millions can’t benefit from using smart canes to support their movement).
Offering additional (and relevant) health functionality for devices we already own and use scores great in terms of being low-resistance. Technologies using our phones, watches, and so on (like skin checking apps or aFib detection) make penetration much more likely.
That doesn’t mean that new technologies can only become widely used and accepted if they are based on an existing device, but not necessitating large investments significantly lowers the threshold.
6. Does that make sense on a global (or at least continental) scale?
Local solutions are working less and less, as digital health progress typically requires us to connect data and technology that could not have been connected earlier.
A prime example is how Foundation Medicine’s clinical trial matching system works: it matches patients to precision oncology trials based on genomic alterations, tumour type, and age. This method clearly benefits from broad access to data and patients.
Similarly, deep learning and machine learning algorithms require vast amounts of data, preferably from a heterogeneous sample to develop the best possible general models for diagnoses or case assessment. This can’t be done on a local scale.
Thinking global also benefits patients with rare conditions: doctors and patients are able to gain access to data from all over the globe. A uniquely interesting example is the BioBank of the Genetic Alliance, a nonprofit organization founded by non-medical professionals, that is collecting genetic samples of patients living with rare genetic diseases on all six continents, providing a never before seen abundance of genetic samples for research.
It’s much easier to say what is not revolutionary
With the 6-question checklist above you can easily screen out anything that is definitely not relevant or not digital health at all. The tricky part is that a good amount of the remaining initiatives will also fall short somewhere along the line. This checklist is far from being the sorcerer’s tone, but it will suffice to filter out most of the noise and keep you focused on potentially significant ventures.
The post Is It Revolutionary? A 6-Question Checklist To Assess Any Digital Health Announcement appeared first on The Medical Futurist.
