It’s an exciting time to be in healthcare. Whilst healthcare historically hasn’t been the fastest sector to embrace digital it certainly has been leading the way in embracing new and exciting opportunities by leveraging data and the power of AI.
And it’s easy to see why, when you look at the potential for savings, improvements and efficiencies when deploying AI and data strategies.
Key clinical health AI applications can potentially create $150 billion in annual savings for the United States healthcare economy by 2026. Accenture
In this article we are going to analyse a few examples of HealthTech companies that have successfully used data and AI and then we’ll dive into what this means for your business and how you could be using data better to improve your business.
One of the advantages of implementing AI in healthcare is that the application and cost benefit is clear and fairly easy to identify.
Some areas where data and AI have been used in healthcare that are delivering a clear benefit to practitioners, pharmaceuticals and most importantly patients include:
Here’s the first lesson to be learned from companies looking to make the most of the data opportunity:
Do you understand what data you own? Are there areas in your business that would clearly benefit from leveraging data better or implementing AI? Particularly look out for win-win areas that benefit both your business as well as your customers.
Let’s take a look at some of these HealthTech areas and analyse what we can learn from their approach to data and AI.
From cancer pathology to symptom checkers, providers such as Freenome, Buoy Health and PathAI are just a few of the many diagnosis healthtechs which help patients and practitioners to analyse symptoms and help to diagnose diseases quicker and better. Freenome helps you to detect cancer early based on routine blood tests, Buoy Health helps you find the right treatment through providing symptoms based on a guided chatbot and PathAI is using machine learning to assist pathologists in diagnosing more accurately.
But as impressive the results in comparison to actual physicians, we still have some way to go to increase patient trust when it comes to AI based diagnosis. Even if data suggest the benefits of using AI often outperform doctors.
Research suggest that AI might replace 80% of what doctors do today. Fortune
What is one of the biggest barriers of resistance?
The reason, we found, is not the belief that AI provides inferior care. Nor is it that patients think that AI is more costly, less convenient, or less informative. Rather, resistance to medical AI seems to stem from a belief that AI does not take into account one’s idiosyncratic characteristics and circumstances. HBR
And this takes us to the second lesson when implementing AI or machine learning processes in your organisation: Data and technology is only half of the equation.
Don’t underestimate the resistance of humans to hand over personal information or trust machines to make decisions on their behalf.
Even if it rationally makes sense to choose machines over humans, trust and education is a key factor in implementing AI and data effectively. It’s a shift in culture, and it’s one you should never underestimate.
Make sure you truly understand your customers, employees and their fears as part of your AI and data strategy.
What are some ways to help gain by patients or people? Transparency on how personal data may be used is crucial coupled with security. Many feel much more comfortable with the idea of AI supporting humans or experts in decision making (AI that helps practitioners) rather than solely relying on a machine generated healthcare plan or diagnosis, so consider whether a staged approach to AI is the most sensible.
Safety of sensitive data (and let’s be honest, it doesn’t get much more personal than medical data) is another aspect that requires not just a rock solid technical infrastructure but also clear communication to go with it to reassure patients.
COVID has taught us that circumstance is a great teacher when changing habits. Many simply are not aware of what AI is already capable of, or they simply might need help in getting started. So, focus on application circumstances and behaviours rather than just benefit-driven messaging.
So here’s the third thing to be aware of:
Design and deliver great onboarding and education strategies for customers and employees as part of your AI implementation plan.
Simplicity of messaging is crucial, but so is allowing room for playful curiosity which allows people to interact with AI and new technology without handing over personal information. Those are just a few of many ways to alleviate fears and start building trust.
Tackling large data sets can seem daunting. No-one knows this better than scientists tying to predict behaviours and molecule structures who have been working with XtalPi, a pharmaceutical tech company that is helping Pfizer scientists predict and optimise the crystalline forms of drug candidates.
“To perform a single crystal structure prediction requires the computing power equivalent to one million laptops” Hancock, Global Head of Materials Science at Pfizer’s Groton, Connecticut
There is a red thread in this story that you come across many HealthTechs. Which takes us to the fourth lesson to be learned from how healthcare providers are engaging with AI: collaboration.
The reality is that setting up smart data strategies and choosing the right solution, is not something that just one quick hire Head of Data is going to be able to accomplish. It’s not the job of just one developer to create and deliver your data strategy. It takes many smart minds that are willing to collaborate and work together to make AI work for you.
AI technology is constantly evolving and implementing the right solution for your business is rewarding, but it is not a quick and easy task.
Make sure you get the right advisors on board to help you choose the right technology, set up and develop your teams and craft your strategy before jumping into the next best solution.
AI is an investment that quickly pays for itself when set up and used correctly.
A perhaps less exciting but nonetheless crucial area in the application of AI for healthcare is in patient experience and admin processes.
Anyone who has had the pleasure of flowing through the NHS system can attest to how urgently improvements in this area are needed.
Speed matters when it comes to serious health matters. Falling through the cracks of a big admin machine can leave you at best frustrated and disappointed, at its worst it can leave you or your loved one dead.
A 2016 study of 35,000 physicians reviews found that fewer than 1 in 20 online complaints cite diagnosis, treatments and outcomes in healthcare as unsatisfactory, whereas more than 19 of 20 unhappy patients said inadequate communications and disorganized operations drove them to post harsh reviews.
So, who are some of the HealthTech heroes in this space that are providing us with hope?
While providers such as Babylon Health make accessing physicians from the comfort of your home as easy as joining a zoom call, it’s the likes of Olive and Qventus that integrate new AI and machine learning processes with hospital systems to optimise patient flow, improve efficiencies and capacity.
What’s the fifth lesson we can take away? AI has greater impact if you use it to supercharge humans and automate tedious and repetitive tasks where possible. Using AI to improve the experience of both clients and employees is a good place to start.
We could go on for days writing about HealthTechs that are embracing AI and machine learning for the greater good of us all and what we can learn from them. We’ve literally just scratched the surface of what’s possible and feasible and we are excited about what is yet to come.
In summary, here is what we’ve learned from HealthTechs using AI when implementing your own data strategy:
At Brightful we have partnered up with some great experts in AI and machine learning to help you create a solid and tested data strategy and implementation plan.
So, wherever you are in your current AI data journey, get in touch to take your data strategy to the next level.