How Do You Get Started with AI in Healthcare Organizations?

Everyone is aware of AI now, and a lot of people are using it, but many are still sitting on the side lines wondering if it is relevant to them and if so, how to get started and get the value from it that they keep hearing about. The key things foremost in the minds of the non-users appear to be ‘I understand it can be beneficial for increasing productivity and generating faster revenue – but how?’ and ‘Will it take my job?’

I believe that the best way to think of AI is a tool that will save you time and allow you to do your job better to enable you to achieve better results and your company growth gets supercharged. AI is a powerful tool for helping people do a far better job by taking away the guesswork and being able to take in and process more data than a human could ever dream of, and then find the important patterns and connections that humans would miss due to being physically impossible to synthesize that level of data.

Take strategic planning for example. I am currently involved with the most amazing piece of AI that actually combines data and does your strategic planning for you – more effectively than humans – and generates stronger results with real time constantly market monitoring. Imagine how many weeks/months of time and cost that would free up – while also generating stronger results from more data inputs and analysis. This is obviously not a plug-and-play solution as infrastructure and data needs to be set up for your specific use cases – but it is amazing. And quite frankly, where the future is heading. You need to at least understand AI and what it can do now or you will most certainly be left behind. That is not even a question.

May I suggest, if you are one of those still sitting on the side-lines, that you take a staged approach to AI implementation. This way you can get an understanding of it, while generating initial results and getting familiar with I, and work your way up to a fully integrated AI approach.

Stage 1: Understanding what AI is all about – Without needing to be a Techie
The commercial side of organizations across healthcare are struggling to identify and act upon opportunities generated by large amounts of data and AI. Many organizations start with the data or the technology when considering innovation, rather than beginning with the strategic innovation differentiators that are needed to create the winning organizations of tomorrow. So, often, the solution is to “hire a data scientist” or “get your CTO to figure it out” and neither of these solutions is optimal alone. Data scientists start looking at data, and CTOs start looking at tech infrastructure – both of which are valuable components in the transformation – but they should not be the first step.

What is needed – but sadly lacking in most healthcare companies – is people who can bridge the gap between data science and business strategy. Who can turn analysis into reason and action. Who can zero in on the best opportunities that big data and AI and FutureTech technologies and Industry 4.0 can offer in the healthcare arena, and speak the same language as the tech teams and data science teams so that they can deliver on the promise of data science.

If you are your team are struggling with this, we designed a comprehensive – simple and certified – training course ( Artificial Intelligence: From Understanding to Strategy to Implementation for Healthcare Commercial Teams) to give commercial teams an easy to follow, on demand, certified training course so that commercial teams are able to lead and work effectively with the tech and data science teams. This program is designed to give healthcare execs with a non-technical background the critical foundations of understanding what AI is and can and cannot do, how it applies in healthcare and how to plan a strategy with strong ROI to implement to solve your immediate and future challenges. It’s for those people who want to remain at the forefront of their field and who know that with hybrid roles (functional specialists with data science understanding and strategy) increasing, these skills will immediately add value to their organization and their resume.

It is designed for commercial folk in healthcare companies but it helps the data science teams in a lateral way. A top 10 pharma global data science team lead had this to say about the training ‘As a data scientist team, we are frustrated at the fact that marketers and other people in the organization don’t know how to ask the data scientists the right questions, so that we can work up a solution for them. This course would be beneficial for all non-digital people as it would give them the foundations to being able to speak to the data science team in a more productive way.’ Check it out here. https://eularis.com/understanding-artificial-intelligence-training-for-healthcare-teams/

Stage 2: The AI Assistant
This is the simplest way to start with AI. Use AI as an Assistant. One of the easiest AI tools I use that saves me time is an AI Scheduling Assistant – Amy. She helped me avoid the ping pong of emails about meeting times and location or video conference set up. All I need do is cc her in when scheduling a meeting and then put in the email ‘Amy – can you organize this?’ and she would go to work, checking my calendar, reaching out to the other parties (she could only handle up to 5 in those days – she has improved over the intervening 6 years). All I had to do was check my calendar. She has organized everything – and often people thought she was my human assistant and asked me to thank her for organizing it all as she had communicated with them while setting it up. There are many other assistant AI tools that you can easily start using within hours like Amy. Once you start using them you will wonder how you ever did it the old-fashioned way. What about sorting data? This is another great use – and one of the earliest uses of AI – and is how Amazon and Netflix got started. Another simple way to start is tasks like filling in forms or summarizing documents. Or maybe sift through language data to pull out patterns and differences? Or even using it to write the first drafts of your reports. These are all very simple jobs for AI and will save you a lot of time and help your team start to really appreciate AI. By starting your team with simple existing (and often inexpensive) AI tools that save them time, they will start to appreciate AI for what it can do, and they will start to see the potential of it for other uses in their roles.

Stage 3: AI for Prediction and Forecasting
Prediction is a core strength of AI and AI can be trained to predict what will happen in specific situations. This could be used for simple sales forecasting or predicting what sales or marketing message will generate a stronger result. In fact, one such system I found (Eularis members can see it in the membership gameplans ‘Word have power’) is utterly incredible at this. Most pharma seem unaware of it as only 2 are using it but with outstanding results. For an ad for a vaccine, the AI changed one word and sales went up around 300%. I saw it used for a furniture company and it changed the image and a few words in th copy and again, sales went up dramatically. These are the kind of results you get when you let AI predict the impact of words and actions.

Stage 4: The AI Advisor
There are many ways AI can be used as an advisor. For sales reps in pharma there are 2 that are commonly used. The first is getting suggestions for what to say and what messages to focus on for a specific physician at a specific point in time. from an AI assistant and you can also get things like soft skills coaching that (increases sales by around 26% based on one study of one system I am aware of) from reading and predicting results from individual sales call interactions. The system can then drop notes on the sales reps screen to advise them on things to do during the sales call itself that make the results far more impressive while also training the sales reps in reading their audience better. In fact, another great tool for sales reps is a personality tool that can read the targets personality profile from their online presence and predict the best way to interact with them. These are all powerful advisor tools that exist with AI. In all these cases, the AI is not forcing the person to take their advice, although they would get better results it they do, but it is helping them make better decisions

Stage 5: The AI Colleague
We are already at the stage in which many companies are benefiting from AI colleagues – or more commonly known as digital humans. Do not get me wrong – I am not speaking about chatbots here. There is a big distinction. Chatbots, no matter how sophisticated in their language ability, tend to be scripted and designed to replace a customer facing person. If the script doesn’t work as the person goes off-piste with their questions, then the chatbot cannot adapt that well. Digital humans on the other hand, at least the best ones, can context switch in milliseconds and offer most of the aspects of interacting with a human which include conversation, expression, emotion and understanding. They use NLP so recognize intent and unstructured discussions. Because they are like interacting with a real human, their colleagues do not need any technical expertise to work with them as it is simply like having a new human colleague. The experience of interacting with them is far superior to chatbots or RPA that only understands structured data. You should see my favorite one in action. I had a tour of her brain and as a former neuroscientist – I was impressed!

There are some impressive digital humans that work alongside humans in roles such as tech support, HR, customer support, sales and even in medicine. I have pulled out the best I have found in the membership and you even can get tours of their brain, but this is where we are heading and when I asked a digital human about how it is working with humans she said it is a collaboration in which they bring out the best in each other. I like that sentiment and it is true. Having a digital human colleague really helps the humans save time and improve their results. She learns in multiple ways but one way is observational learning so when she is trained on everything, there is invariably things missed. In those cases, she hands over to a human colleague but she continues to observe how the human colleague deals with it. The next time she encounters that situation, she can handle it as she learned from observation. What is fascinating with her is that the only complaint her human colleagues have is that she doesn’t gossip or banter between calls. At the moment she tends to respond only when spoken to. So, the company is working on that now to make her even more human. I am not sure about the gossip but soon she will be able to chit chat to colleagues between calls as they requested