Patient-Generated Health Data
According to scientists and certain optimistic enthusiasts, we might be ready to send the first person to Mars as early as 2034! Even as we speak, preparations are underway to give shape to this unimaginable challenge, and the eager queue of volunteers willing to be part of the first commercial expedition is astounding…
A mission to Mars represents a combination of human courage, unprecedented expertise and unbridled confidence in the possibilities of technology. In that respect, we can see similarities with healthcare. For the mission to Mars, besides from the obvious technology such as sufficient thrust force and reliable landing gear, another technological aspect is of crucial importance for its success, namely ‘Astronaut Generated Health Data.’ This term probably hasn’t been coined yet, but the fact is that during a trip to Mars 35 different parameters will be continuously measured for each astronaut, and they will be monitored – albeit with a time delay of approximately 20 minutes – analyzed and linked back around the clock by some of the best brains on our blue planet. The measurements of oxygen, heart rate, ECG, HRV, blood pressure, temperature, activity, weight, muscle mass, muscle tension, respiration, sweat composition, location, and similar parameters will cause an unprecedented amount of data flows.
The 5 pillars of healthcare data: Determine, collect, record, process and discuss
And now, back to planet Earth anno 2017, the healthcare sector is undergoing a similar development: data collected in direct by the patient is becoming more and more important. We don’t need to wait for Martian technology to be beamed down to us. Already now, a range of patient-related parameters can be remotely read out and monitored. Now you might be raising an eyebrow and wondering if there is any kind of patient-data that hasn’t been generated by a patient. Well, it’s just a matter of semantics: the most obvious patient-data is gathered in the hospital setting and tends to also be stored, processed and discussed there. Think about it, MRI scans are not done at home, and their interpretation and explanation should be left to professionals. But, there is a growing form of data that can easily be gathered by patients in direct. Technology has made it possible for this ‘patient-generated data’ (PGHD) to be determined, collected, recorded, and processed. Just the ‘discuss’ element needs to be done with a healthcare professional. For some physicians this development is a real manna from heaven, for others it’s the exact opposite.
The scope of PGHD is made clear by Sara Riggare, a Swedish Parkinson patient: “Per year, I spend 1 hour with my doctor to discuss the progression of my illness and to adjust my medication if required. Then I spend the other 8765 hours of the year self-managing my health.” It is precisely that ‘self-management’ that is ideally suited to mobile apps on the smartphone; after all, this device hardly ever leaves one’s side, is crammed with sensors and is connected to the internet 24/7. The availability of over 260,000 health apps that all together have been downloaded 3.2 billion times (research2guidance, 2016) stands testament to the solid interest surrounding the potential of the medium. Recent research by Accenture (2016) demonstrates that the use of health apps and sensors has more than doubled over the last few years (from 16% to 33% and from 9% to 21%, respectively). But should we also just simply accept the fact that it has become impossible to make a sensible selection from among this quarter of a million available health apps?
Value for healthcare
What we are talking about is ‘Patient Generated Health Data’ (PGHD); it has the potential to generate a continuous flow of useful data for the healthcare sector. That ‘usefulness’ is the subject of many debates and discussions on which the views are much divided. Is this a case of ‘old wine in new bottles?’ wonders Dr. Eric Topol, cardiologist, author and known advocate of innovative technology in the healthcare sector. “After all, home pregnancy tests already existed in the late Seventies, and self-testing of blood glucose has also been done for years. But what we are talking about now is an unprecedented scalability of digital technology and sensors that can collect data via a smartphone. In principle, several of the measurements that are currently carried out by a doctor will soon be done by patients themselves” says Topol. In general, there are three key areas where PGHD will play an unmistakable role: 1. Value-based Health Care; 2. Clinical Research; 3. Changing Patient Behavior.
Artificial intelligence and PGHD
It is clear that PGHD is only of interest when it is accompanied by some sort of interpretation. Currently this interpretation is provided by the healthcare professional, but increasingly we see that such interpretations are supported by digital input. This is called ‘Clinical decision support.’ It is generally expected that increasingly complex algorithms that can analyze all kinds of data will emerge and that these will eventually be able to reach conclusions and give advice in a more and more autonomous manner. Artificial intelligence (AI), where self-learning computer systems based on neural networks provide services to mankind, has left the realm of science fiction a long time ago. Indeed, both Apple and Google provide tools to implement self-learning neural networks in the development of mobile apps, including medical mobile apps. Look at Watson, IBM’s medical super computer that, on the basis of many years of training with thousands of oncology related publications, ended up prescribing a better treatment plan than an actual oncologist.
In complex AI systems the reasons for why the computer has taken a certain decision are not always clear; this can be because, for example, the code itself was written by the software and can’ translate back into a humanly comprehensible logic. To what extent this causes technological singularity is still unclear, but what is certain is that this path has already been embarked upon. Without wanting to create a doomsday scenario, in which iPhones turn into a modern version of SkyNet and command an army of Terminators, a frank discussion of AI’s role in healthcare does need to take place.
Getting started with PGHD
Implementing PGHD in daily practice means bringing about a change in how we see things. It is important to start off small. The chance that data is already being collected is quite high, albeit through more traditional methods (paper questionnaire?). It all starts with a vision on the value of PGHD and the willingness to invest in it. Choose realistic objectives that are attainable as of when the expected added value for the healthcare process and/or the patient has come to fruition. Value-based healthcare (= health outcome / costs) should be taken into account. When determining the software requirements it is important to select a platform that is scalable and flexible; so, start small and expand it in accordance to the relevant phases as they actualize.
Implement a process that is as similar as possible to the existing one so that the change for the employees is minimal. Describe and train the procedure so that it’s clear to all who does what and when. Don’t forget to consider the human factor first and give the software a secondary role. We live in a world where a caregiver is expected to also have a NASA-degree in order to operate the average HIS or EPD…so let’s try to opt for software platforms that offer a good degree of user-friendliness. Finally, learn from mistakes and build on successes.
After all, this approach is what helped put mankind on the moon, and soon perhaps on Mars.
Contributing author Erik van der Zijden is CEO and Co-founder/Partner at Synappz Mobile Health as well as a Rockstart mentor. This is a republished, condensed version of a post from February 11th. You can read the full article here.
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