Maddox: I’m a cardiologist. Typically, patients I see include those who may have had a heart attack, or patients who are dealing with heart failure. Traditionally, we monitor these patients by having them come to our cardiology clinic for follow-up visits. For example, I might have a patient come and see me every six months. But this follow-up time period is basically chosen at random—there’s no specific medical reason for it.
With AI, in theory, I would no longer schedule these standard follow-up appointments. Instead, I would review the integrated, AI-analyzed, data streams of my thousand-patient practice every day. The AI algorithms would integrate multiple data streams that can paint a picture of my patients’ cardiac health on any given day.
For example, I could see my patients’ activity levels measured by their wearable devices, their medication adherence patterns from pharmacy data, their breathing and pulse rates from their home-based sensors, their weight fluctuations from their “smart” scale, their exposure to air pollution from environmental sensors, and recent ER visits from their electronic medical record. Taking all that together, the AI could identify any patients who are at high risk for cardiac problems.
From a heart-attack risk point of view, there might be a patient who is a smoldering fire, so to speak, and the system prompts me and my team to reach out to that patient for an appointment as soon as possible. For the remainder of my cardiac patients who don’t have any current high-risk features, then there would be no need for them to come to my clinic. We would just continue to monitor them remotely and be available for any problems.
Payne: What Tom just described is a smart health-care system—where the consumption of health care is driven by actual need. In his example, the AI sorted through a massive amount of different kinds of data and pulled out the one patient he needed to see and interact with. Some of the best ways to reduce health-care costs and improve health-care outcomes will be to eliminate the care that patients don’t need.
Right now, the challenges we need to address as we try to bring AI into medical practice include improving the quality of the data that we feed into AI systems, developing ways to evaluate whether an AI system is actually better than standard of care, ensuring patient privacy and making sure not only that AI doesn’t disrupt clinical work flow but in fact improves it. But if we do our jobs right and build these systems well, much of what we have described will become so ingrained in the system, people won’t even refer to it separately as informatics or AI. It will just be medicine.