The question was the start of the project and I can tell you: it is not an easy question to answer. The importance of the question is increasing monthly as the healthcare industry is facing the imbalance between treating more people with fewer healthcare professionals.
In this blog post, I will take you with me on the journey of creating Careflow from my point of view: What my role and most important contributions were, what I learned from each step and how my understanding of AI and business strategy changed.
My main role in the project was to investigate, collect and create content for the project and create a prototype.
Careflow
Careflow is an AI agent that reduces the administrative burden of optimising Diagnose Behandel Combinatie products (DBCs) for healthcare professionals in the rehabilitation market. During our prototype testing, completing the optimisation workflow took 83% less time than the current workflow. DBCs are a container where treatment hours are collected during a period. The reimbursement for DBC products is based on an hour range. The more optimised the DBC is, the higher the net return based on the revenue of the reimbursed product and costs of treatment hours spent.
Investigation
The healthcare industry is historically part of the late majority and laggards in terms of adopting technology, which poses additional challenges when advancing from creating prototypes to selling products. Selling products to the late majority and laggards requires different business strategies than selling to early adopters and the early majority. These adopters are notorious for only adopting technologies when they are well proven or simply when they are forced to adopt to stay in business.
The healthcare industry is broad and each submarket of the healthcare industry has numerous workflows. I learned that it is not realistic to create a paradigm shift within the healthcare industry with a project that lasts 6 weeks. That shifted the goal to narrow down the scope of the project to a workflow in a submarket of the healthcare industry.
I identified a couple of challenges from the investigation:
Which part of the healthcare industry do I want to focus on and which workflow do I want to improve?
I identified the medical rehabilitation care as a suitable target, because most patients receive long-term care and there are many administrative workflows that can still be automated by using proven technologies (e.g. Augmented Reality (AR) or generative AI (genAI)) and emerging technologies (conversational AI (CAI) and agentic AI). Furthermore, the medical rehabilitation market counts 34 organisations with a shared revenue of €825 million in 2023 according to the most recent market report.
You would think: Great, the first challenge is done! However, the second challenge posed a different threat. The medical rehabilitation market has many specialised workflows that you can improve.
Where do you start when there are many workflows?
I interviewed a programme manager, project leader, information security officer and doctors from a medical rehabilitation centre. Their ideas ranged from reducing the administrative burden on analysing referrals, communication traffic between colleagues, creating prescription letters, the administration of patients’ examination scores, no-shows, appointment registration and analysis on optimising DBCs. It surprised me that they had a varying number of ideas that shared overarching topics: communication traffic and optimising DBCs.
An important decision was made to pick the workflow ‘optimising DBCs‘ over ‘communication traffic’. While ‘communication traffic’ sounds easier on paper, it is not. The candidate workflows for ‘communication traffic’ require extensive medical knowledge to create a more efficient and reliable workflow. Furthermore, testing the workflow will require experts. For the scope of the project, this is not realistic to achieve. The workflow ‘optimising DBCs’ requires manual analysis of multiple platforms to create a recommendation that a physician approves or rejects.
Prototyping
Creating the first prototype taught me a life lesson: the best way to gain hands-on experience with emerging technologies is by prototyping!
Collaborating taught me that a prototype should not be only ‘technology-push’ by a single person, but that it also requires proper testing, input and incorporating the gathered feedback from the whole group to create a prototype that all of us understand.
For example, after creating version 30 of the prototype, the prototype had the information spread out across the page. What I perceived as an easily readable spread of information was not perceived as understandable. One could say the prototype was a failure at this point. The failure impacted me so deeply that it changed my approach to put user interface design and user experience at the same level of importance as developing the code. After major changes, I asked my teammates if the changes improved the prototype. This feedback loop was kept until we finished version 87.
AI Strategy
At the start of the project, genAI was picked as the technology for Careflow. The idea was creating an analysis and DBC optimisation recommendation through prompting. However, the guest lecture by Unframe explained agentic AI thoroughly and provided evidence that agentic AI is a better fit for the target workflow. The optimisation workflow starts when DBCs are about to close, continues with an analysis across multiple platforms and ends with a recommendation. This taught me that creating an AI strategy starts with understanding the alternatives and differences.
Furthermore, the project taught me that there is more than just the ‘AI’ in the strategy that is often overlooked: the governance of an AI technology. It is not only about creating the prototype, testing and validating, but also what the logic is behind the AI technology and its workflow, which privacy laws and (international) regulations it needs to comply with and which risks the chosen AI technology poses.
Business strategy
Throughout the multiple prototyping iterations one lesson learned kept bugging me: How do we adapt the business strategy in the healthcare industry for the late-majority and laggards?
Collaborating with my group helped me understand that it is possible to create a business strategy for the target adopters to capture the value by versioning based on token usage. The logic behind this is that every organisation has a different budget and optimisation needs. This allows the organisations to pick the version that suits them. I learned that creating a value capture model is not easy, but with the right economic analysis and model it is possible.
Throughout the project I learned that you cannot just push out the product you created without validation. The created prototype should also be shown to ‘real’ customers to validate the demand. If a prototype is not validated for demand, then there is no point in continuing the development of the product. In Careflow’s case, we validated the initial demand for the product. We showed the prototype to potential customers to test if there’s demand. Their responses went as far as asking when the final product is available for sale and saying that the prototype solves the administrative burden of optimisation.
To answer the question in the title:
“You disrupt the market by investigating the need, applying the appropriate technology, creating an appropriate AI and business strategy, testing your prototype with your collaborators and validating the product with real customers”.
Thank you for reading!
Danny
