Aiming for AI Adoption By Designing for Economic Value
In the last series of posts, I’ve discussed the importance of carefully examining a use case before developing or funding a health AI solution. A use case analysis should include the economic impact of your solution on the business model of the enterprise buying it. In healthcare, most of the sales happens business to business […]
The Business Case for AI in Healthcare V
One salient issue is deciding whether to build or invest in a health AI solution is whether it solves a big enough problem. There are now hundreds of solutions that have been approved by the FDA and have not seen much commercial traction. There could be multiple reasons for this but one of the key […]
The Business Case for AI in Healthcare IV
If you’ve determined that the workflows will be user-friendly and the data required for the AI solution to create its output will be available in a reliable and timely manner, you’re off to a good start of the business case analysis. One of the next areas to focus on will be the economic benefits of […]
The Business Case for AI in Healthcare III
Beyond workflows, the issue of data is salient. AI solutions have no benefit if there’s not enough data. Often, a medical algorithm shows great results in development and validation but when it gets to the real world, the results are disappointing. That is because it may not be getting the data it needs in a […]
The Business Case for AI in Healthcare II
In the last post, we discussed the myriad of issues that need to be analyzed from the point of view of builders or buyers of health AI solutions. Each one of those issues can make or break a promising solution. The business analysis for building or buying a health AI solution should include issues such […]
The Business Case for AI in Healthcare I
If the first decade of digital health has taught us anything, it’s that having an innovative product does not necessarily mean success. There are many problems in healthcare and there are technologies today for many of those issues. So why are those technologies not being used to solve those problems? Well, because bringing digital technologies […]
AI in Chronic Disease Management III
Chronic pain is another area that’s a source of significant morbidity for patients, leading to a high amount of use for healthcare resources. Its treatment often involves a variety of procedures and can lead to long-term narcotic addiction. Interesting areas that are being explored for the management of chronic pain include immersive experiences with virtual […]
AI in Chronic Disease Management II
In the last post, we discussed the collaboration between University of Utah Health, the Regenstrief Institute and Hitachi for the development of a new artificial intelligence approach that could improve treatment for patients with Type 2 diabetes mellitus. Pooling data from different institutions enabled an AI-based approach that groups patients with similar disease states and […]
AI in Chronic Disease Management I
Chronic disease management is complicated and onerous, both for the care team and for the patients. Most chronic disease management programs haven’t shown significant ROI. It’s a daily process that requires attention, follow-through, follow-ups, and ongoing communication. Most patients don’t have the time or patience for it and most care teams don’t have enough resources […]
AI in Precision Medicine II
AI can have a big impact in oncology through the analysis of genomics and mutations and matching those with the best treatment combinations and the right clinical trials. Tempus taps into clinical and biological data to create a digital twin and uses nearest neighbor analysis to find the best course of management. Another startup developing […]