Technology adoption in Life Sciences and Healthcare industry
I am sure you all know about technology adoption lifecycle, whether because you have read Geoffrey Moore’s book Crossing the Chasm, or empirically, as you are all sitting on this curve for several technologies that you use.
According to Moore each section of the Technology Adoption Lifecycle curve (see above) has its challenges, however the most critical one is at the early adopters, after moving form the innovators. This is the famous “Chasm” that needs to be crossed! There are many examples to explain it better; my favorite one is about smartphones, including iPhone, Blackberry and Android (you can imagine for sure where each of them is sitting on).
Thanks to a nice presentation I followed on Blockchain recently, I looked again at the principles of the Gartner’s Technology Adoption Curve. Below is a screenshot depicting the status around 2012.
What the figure says, is that most technologies follow the above graph in terms of expectations (more or less). They start with the technology trigger, moving to the peak of inflated expectations, the trough of disillusionment :( and those that survive the go to the slope of enlightenment, before they reach the plateau of productivity. In the above example, one can see the status, according to Gartner, for several technologies.
Are the above graphs connected and if yes, where does the R&D stand? Many people and researchers connect these two graphs by placing the chasm in the valley between the Trough of Disillusionment and the Slope of Enlightenment. I do not disagree, but there are cases in which is this not happening. In my view it is easier to connect the 2 graphs with the R&D stake. They are obviously connected, however there is not a one-to-one connection between the segments. Below I tried to map the segments of the 2 graphs, also highlighting how R&D is reduced when moving from segment to segment.
Now that we refreshed our memory about the Technology Adoption Lifecycle and Garnter’s Curve about technology expectations, let’s see the status of technologies in the Life Sciences and Healthcare industry. First of all, let’s list the technologies that are involved:
- Big Data: One thing is certain… the industry has Big Data to offer. Talking about Big Data in Life Sciences and Healthcare industry all 4 Vs of Big Data are met, namely the Volume, the Velocity, the Variety Veracity. Big Data goes hand-by-hand with the digital transformation of any organization and this is what happens in this case too. Prior to the introduction of EHR, EDC systems, Real World Data, digital imaging, etc, the data was not big enough and certainly not actionable. As I mention in almost every presentation on Big Data, during the digital transformation, we go through a journey of raw data → information → knowledge → wisdom, before we reach the actionable state.
- Internet of (Medical) Things: IoT and IoMT are already used and significantly contribute to the digital transformation of the sector, as well as the generation of Real World Data (RWD), through wearable technologies. Imagine what happens if for example you team up with Garmin, getting access to the activity profile of users, lifestyle patters, heart rate, sleeping quality, sport activities and nutrition through MyFitnessPal. Fusion of this data with clinical observations can result in non obvious results that can enhance drugs and treatments.
- Artificial Intelligence: In an earlier post (How #AI are you? Or is it just the hashtag?), I have highlighted the misuse of the term AI. In most of the cases, the life sciences and healthcare sector is using today much less than pure AI mechanics. However the need for machine learning, delivering systems that take decisions in a human-like process is enormous, with lots of applications, among them: image reading, diagnosis, treatment selection, decisions making, prevention, intelligent intervention and faster drug development. This can be significant, but the lack of AGI (aka Artificial General Intelligence) is still prohibiting the offering of holistic services.
- Blockchain: We don’t talk about Bitcoin (#BTC) here. We talk about a distributed Peer to Peer (P2P) ledger that can facilitate the secure and transparent log of transactions, the automatic execution of actions, e.g. smart contracts, while eliminating 3rd parties that need to intervene. Blockchain seems to be a solid solution for EHR, transactions between health organizations, insurance companies and patients. However, there are still technological challenges that are looking for solution, such as how to address the GDPR requirement on the “right to be forgotten” for those interested to remove their data. In the clinical research sector a Blockchain approach of the eCRF and ePRO systems could also create a trust environment to ensure the Good Clinical Practices, strengthening the transparency throughout the period of the study.
- Robotics: The healthcare sector has glorious moments related to robotics. The da Vinci robot opened new horizons on robotic surgery since 2000, while numerous robots are used in hospitals to assist patients, doctors and caregivers. The new era of health & care robotics brings robots to the houses making them affordable and helpful. The past years we have started a fruitful collaboration with a startup from Luxembourg, LuxAI, that produces social assistive robots for children with autism and seniors with dementia.
- Industry 4.0: Healthcare is certainly an industry that can benefit from the Industry 4.0 paradigm, focusing on its cyber physical systems. The nature of the sector though is more physical rather than virtual and therefore the expectations are not set very high yet.
- Cloud Computing: Cloud computing has penetrated the healthcare domain long time ago. Stakeholders in Healthcare sector are convinced more than ever about the privileges of cloud solutions and computing, so we can consider these technologies more mature than others.
Summing up, below you can see a table with my view on technology adoption lifecycle and Gartner’s curve of expectations for the above technologies and in Life Sciences and Healthcare industry.
I emphasize that this is my view, as this is a very subjective thing. I would be happy to see your responses and therefore I have created a simple, anonymous survey. It is not any scientific practice, but I am eager to see how you react and what is your thought about the above technologies and their application in Life Science and Healthcare.
After receiving a good number of responses, I will extend the article with a summary.
Looking forward to your input.