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An entire workflow can sit idle because nobody has noticed that one step has finished and the next can begin. Someone in your workflow needs to research, compile, organize, draft, tabulate, and complete any number of laborious tasks requiring hyperfocus and… time.
Waiting is the death of productivity, and time is the assassin of getting things done.
Recurring tasks are often good candidates for automation. They consume our attention, and the gaps or interruptions between steps can introduce errors.
A healthcare AI system can do exactly what it was built to do and still leave a care team with more work. Flagging a change between appointments may help a patient receive care sooner, but it also creates a review task for someone already caring for other patients.
Before expanding AI-supported monitoring, I would want evidence that it reduces avoidable review work and that care teams have the time, context, and authority to act on changes that matter.
Earlier this year, I wrote that browser automation was a bridge period.
At the time, my focus was trust. Agents could technically complete workflows, but the systems around them were not really designed to know who was behind the request, what the agent was authorized to do, or whether the interaction should be trusted.
In June, the U.S. Department of Health and Human Services announced that more than one billion health records had been exchanged through TEFCA, the national interoperability network. The volume grew from 10 million to more than one billion in less than a year. Healthcare is developing the infrastructure required to exchange information across the system at a scale we have not seen before.
