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Healthcare AI: Adapt or Become Obsolete

With Google CEO Sundar Pichai warning society to brace for impact of A.I. acceleration, saying “every product of every company” will be impacted by the development of AI, health systems must quickly start gaining experience in how AI can be applied to health system operations.

What should health systems do to gain capabilities to adapt to this new environment?


We believe there are four things health systems can do right now to start gaining the capabilities to thrive in the new changes.


Apply existing AI models to support existing workflows

Today early AI models are being applied to healthcare delivery, with relatively poor, but improving results. These poor results have created skepticism. Yet, the results are not a result of the technology but of the application. Today, AI tools can be used to best support the workforce rather than replace the workforce. For instance, AI works very well in helping front line staff off-load work from the clinicians through AI-guided interviews and documentation.


Pick areas where errors do not create catastrophic injury

Rather than using AI to substitute for clinical decision making, health systems can gain experience by using AI to support patient intake and documentation in the low-risk settings such as urgent care and diagnostic testing intake.


When applied to a field like urgent care, AI can support the MA or LPN as they register the patient and conduct the history and physical. With AI support the questions asked can be tailored based on the earlier responses to create detailed clinical notes supporting coding and give the provider an organized understanding of the situation before they enter the room.


Evaluate all existing processes with an eye for what inputs can be standardized and quickly incorporate AI tools

Standardization of healthcare’s common processes have been an opportunity for decades. Today, those that can quickly identify segments can be standardized will be better prepared for application of AI support. Moreover, with AI eliminating mountains of documentation and decision-making, the benefits gained from standardization are far larger than before. This allows segments to be much smaller because scale economies curves are much steeper. Smaller segments mean more incremental standardization and change can be accomplished.  


Don’t wait, work within the current revenue models

Because the scale economies are so large, how AI-led treatments will be reimbursed is likely to be highly contested. Over time, AI will dramatically improve productivity and reduce the payments for activities AI models can complete for virtually free. This means massive change for the workers doing these tasks and the companies that employ these works.


Instead of looking today for AI payments, applying the technology to existing workflows to improve productivity allows more work to be done by the same staff and paid under the same reimbursement models in place today.


Healthcare is not ready for more disruption, yet it is coming. Unless the non-profit health system leaders drive organizational AI-capabilities to help guide the application of AI, investors will. Health systems have a responsibility to not put their heads in the sand but quickly build the capabilities to guide application of AI models in future healthcare delivery.


About HSA:

Health System Advisors advises health systems on their competitive market positioning. Our team of motivated, engaged, and inspired strategists brings analysis, insight, and expertise as we facilitate your teams to new ways of thinking and strategies that advance your organization.

Kate Lovrien and Luke C. Peterson are principals at HSA. They can be contacted at and

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