By Stewart Whiting

A study by Human Resources for Health projects there will be a shortage of 15 million healthcare providers worldwide by 2030. At the same time, the largest living generation — the Baby Boomers — is aging; meaning that as we anticipate an influx of patients requiring critical care, we’re dealing with a waning pool of people equipped to treat them. Today’s providers and patients are already feeling the effects of this crisis. With doctors and nurses rushing from patient to patient, overloaded with patient data, it’s a challenge to absorb critical information and make accurate, efficient diagnoses. As a result, patients do not always get the care they need when they need it.

The good news is that artificial intelligence (AI) can help. Here’s a look at how the technology can help diagnose patients, monitor their health over time and inform provider actions to ensure optimal care is delivered.

Efficiently diagnose patients
Today’s patients come into healthcare environments with a significant amount of data already attached to them. From paper and electronic health records to family histories and their vital signs at intake, healthcare providers are overloaded with information before they’ve even seen a patient.

This, of course, can be problematic. It’s difficult enough to review and draw correlations between the various threads of patient information; that challenge becomes much harder when doctors and nurses are scrambling to determine the appropriate care plan while juggling a host of other patients.

AI-backed platforms can rectify this issue. By extracting patient data from existing records and from monitors and machines they may be hooked up to during a hospital stay, AI platforms can analyze millions of data points in seconds and learn how to identify innocuous patterns and combinations of early symptoms. With this information, providers can focus only on pertinent data that can help make decisions, and in some cases highlight information that may have otherwise gone overlooked.

For example, if a patient came in displaying early symptoms that may be indicative of a number of illnesses, an AI-backed platform would have the ability to quickly analyze all of the patient’s data and understand that the patient’s family has a history of an illness that these symptoms could point to. This would then allow the provider to quickly test for that illness and come to a conclusion quickly, as opposed to weighing or testing for numerous possible options.

With the ability to identify diagnoses, sometimes years ahead of what humans can do, the U.S. healthcare system can use AI to practice more proactive, preventative medicine.

Monitor patients — wherever they are
Once a diagnosis has been made, AI-powered solutions, such as remote patient monitoring (RPM) platforms, can help monitor patients — both in a healthcare environment and at home. By collecting, analyzing and sharing information about a patient’s vital signs or other health indicators, AI solutions can empower providers to ensure appropriate patient treatment, even for patients not in the hospital.

This is especially useful when monitoring patients with chronic illnesses or those recovering from surgery. By keeping tabs on the patient’s state after they’ve left the hospital, doctors and nurses can ensure the patient’s well-being, without having to sacrifice a hospital bed. Plus, remote patient monitoring often quells patient anxiety, especially after a health-related episode, since they know they’re being watched by trusted professionals, even when at home.

In addition to supporting care at the individual level, AI solutions can help providers better manage their entire patient roster by providing insights into which patients require care most urgently. For example, if Patient A is recovering faster than expected following surgery, providers might decide to prioritize Patient B, who is in more dire need of care.

While AI can have a significant impact in clinical settings, it is not a magic bullet. The industry is still new, and providers must be wary of relying too heavily on AI. For instance, we’re still waiting on clear ethical guidelines on how AI should be used, and there are technical challenges, such as interoperability and poor data, that make implementation difficult.

So while human intervention is still critical, we can look forward to a future when AI gives clinicians the information they need to make more informed decisions — and more quickly.

About the author: Stewart Whiting is the co-founder and chief technology officer of Current Health.