AI in healthcare: The tech is here, the users are not
Since the beginning of the year, there has been a significant uptick across health plans, healthcare providers, and analytics firms utilizing AI to change how healthcare is delivered and how patients can be more engaged in their care.
AI applications in healthcare are all over the map today. Data from my firm’s digital health intelligence database, DamoIntelTM, has identified a significant rise in the launch of AI use cases across clinical and administrative areas in 2020. An analysis of AI/ML applications deployed by the top 50 health systems across the United States identifies that AI-enabled solutions fall into multiple technology categories: machine learning, natural language processing (NLP), conversational interfaces such as chatbots, and robotic process automation (RPA). COVID-related use cases in clinical and administrative areas contributed to the growth in adoption of newer technologies such as chatbots in healthcare.
The biggest challenge for AI-enabled care is to deliver insights in real-time into the clinical workflow at the point of care. For example, voice recognition technologies are effective with lower-level tasks such as scribing doctor-patient encounters. However, they are yet to evolve into decision-support systems that deliver additional insights at the point of care for diagnostic and treatment decisions.
On the other hand, solutions that can deliver real-time insights are yet to reach scale and broad-based adoption. An example is Stanford University’s smartwatch-based COVID diagnosis app, in partnership with Amazon, that analyzes elevated heart rates and other abnormalities for pushing real-time alerts to patients suspected of COVID infection. Dr. Michael Snyder, Professor and Chair of Genetics, is working to scale the solution with the aim of creating a continuous monitoring framework for health indicators at an individual level. His goal it to cover anyone, anywhere who has a smartwatch. Amazon has offered millions in cloud computing credits for similar diagnostic solutions for digital health innovators across the world.
If there’s one new trend this year, it’s data collaboratives. Truveta, a consortium of 14 health systems launched in February, aims to pool patient data from all the member systems to drive advanced analytics for improved healthcare outcomes. Google has announced a series of partnerships with healthcare enterprises, including Mayo Clinic, Ascension Health, and Highmark. Use cases include but are not limited to data analysis for quality measures, benchmarking, and administrative reporting. In addition to its partnership with Google, Mayo Clinic has launched new data collaboration initiatives with AI startups, targeting data from remote monitoring devices. Highmark, a major health plan based in Pennsylvania, has formed a 10-year partnership with Christiana Care in Delaware to pool medical and claims data to drive better outcomes. Expect to see more consortiums as large payers and providers pool their datasets to drive efficiencies through advanced analytical insights.
