Previously published in HIT Consultant
A century ago, X-rays transformed medicine forever. For the first time, doctors could see inside the human body, without invasive surgeries. The technology was so revolutionary that in the last 100 years, radiology departments have become a staple of modern hospitals, routinely used across medical disciplines.
Today, a new technology is once again radically reshaping medicine: artificial intelligence (AI). Like the X-ray before it, AI gives clinicians the ability to see the unseen, and has transformative applications across medical disciplines. As its impact grows clear, it’s time for health systems to establish departments dedicated to clinical AI, much as they did for radiology 100 years ago.
Radiology, in fact, was one of the earliest use cases for AI in medicine today. Machine learning algorithms trained on medical images can learn to detect tumors and other malignancies that are, in many cases, too subtle for even a trained radiologist to perceive. That’s not to suggest that AI will replace radiologists, but rather that it can be a powerful tool for aiding them in the detection of potential illness — much like an X-ray or a CT scan.
AI’s potential is not limited to radiology, however. Depending on the data it is trained on, AI can predict a wide range of medical outcomes, from sepsis and heart failure to depression and opioid abuse. As more of patients’ medical data is stored in the EHR, and as these EHR systems become more interconnected across health systems, AI will only become more sensitive and accurate at predicting a patient’s risk of deteriorating.
However, AI is even more powerful as a predictive tool when it looks beyond the clinical data in the EHR. In fact, research suggests that clinical care factors contribute to only 16% of health outcomes. The other 84% are determined by socioeconomic factors, health behaviors, and the physical environment. To account for these external factors, clinical AI needs external data.
Fortunately, data on social determinants of health (SDOH) is widely available. Government agencies including the Census Bureau, EPA, HUD, DOT and USDA keep detailed data on relevant risk factors at the level of individual US Census tracts. For example, this data can show which patients may have difficulty accessing transportation to their appointments, which patients live in a food desert, or which patients are exposed to high levels of air pollution.
These external risk factors can be connected to individual patients using only their address. With a more comprehensive picture of patient risk, Clinical AI can make more accurate predictions of patient outcomes. In fact, a recent study found that a machine learning model could accurately predict inpatient and emergency department utilization using only SDOH data.
Doctors rarely have insight on these external forces. More often than not, physicians are with patients for under 15 minutes at a time, and patients may not realize their external circumstances are relevant to their health. But, like medical imaging, AI has the power to make the invisible visible for doctors, surfacing external risk factors they would otherwise miss.
But AI can do more than predict risk. With a complete view of patient risk factors, prescriptive AI tools can recommend interventions that address these risk factors, tapping the latest clinical research. This sets AI apart from traditional predictive analytics, which leave clinicians with the burden of determining how to reduce a patient’s risk. Ultimately, the doctor is still responsible for setting the care plan, but AI can suggest actions they may not otherwise have considered.
By reducing the cognitive load on clinicians, AI can address another major problem in healthcare: burnout. Among professions, physicians have one of the highest suicide rates, and by 2025, the U.S. The Department of Health and Human Services predicts that there will be a shortage of nearly 90,000 physicians across the nation, driven by burnout. The problem is real, and the pandemic has only worsened its impact.
Implementing clinical AI departments can play an essential role in reducing burnout within hospitals. Studies show burnout is largely attributed to bureaucratic tasks and EHRs combined, and that physicians spend twice as much time on EHRs and desk work than with patients. Clinical AI can ease the burden of these administrative tasks so physicians can spend more time face-to-face with their patients.
For all its promise, it’s important to recognize that AI is as complex a tool as any radiological instrument. Healthcare organizations can’t just install the software and expect results. There are several implementation considerations that, if poorly executed, can doom AI’s success. This is where clinical AI departments can and should play a role.
The first area where clinical AI departments should focus is the data. AI is only as good as the data that goes into it. Ultimately, the data used to train machine learning models should be relevant and representative of the patient population it serves. Failing to do so can limit AI’s accuracy and usefulness, or worse, introduce bias. Any bias in the training data, including pre-existing disparities in health outcomes, will be reflected in output of the AI.
Every hospital’s use of clinical AI will be different, and hospitals will need to deeply consider their patient population and make sure that they have the resources to tailor vendor solutions accordingly. Without the right resources and organizational strategies, clinical AI adoption will come with the same frustration and disillusionment that has come to be associated with EHRs.
Misconceptions about AI are a common hurdle that can foster resistance and misuse. No matter what science fiction tells us, AI will never replace a clinician’s judgement. Rather, AI should be seen as a clinical decision support tool, much like radiology or laboratory tests. For a successful AI implementation, it’s important to have internal champions who can build trust and train staff on proper use. Clinical AI departments can play an outsized role in leading this cultural shift.
Finally, coordination is the bedrock of quality care, and AI is no exception. Clinical AI departments can foster collaboration across departments to action AI insights and treat the whole patient. Doing so can promote a shift from reactive to preventive care, mobilizing ambulatory and community health resources to prevent avoidable hospitalizations.
With the promise of new vaccines, the end of the pandemic is in sight. Hospitals will soon face an historic opportunity to reshape their practices to recover from the pandemic’s financial devastation and deliver better care in the future. Clinical AI will be a powerful tool through this transition, helping hospitals to get ahead of avoidable utilization, streamline workflows, and improve the quality of care.
A century ago, few would have guessed that X-rays would be the basis for an essential department within hospitals. Today, AI is leading a new revolution in medicine, and hospitals would be remiss to be left behind.