Preventing Clinician Burnout with AI

By Arnaud Rosier, CEO, Implicity.

Burnout among doctors and nurses was already a major problem facing healthcare organizations, even before the COVID pandemic – maybe even more for healthcare professionals working in the extremely demanding field of cardiology.

It’s not just a matter of long hours and overwhelming patient loads (although these factors certainly don’t help). Burnout is also the result of the pressure that healthcare providers put on themselves to be perfect when the stakes are literally a matter of life or death. According to one recent survey, 43 percent of cardiologists report feeling burnt out, compared to 39 percent in academic medicine and 32 percent of nurses.

Those numbers are alarmingly high, but healthcare organizations often lack good options for addressing the problem. The COVID-19 pandemic stretched already thin healthcare staff nearly to their breaking point, and the ensuing “Great Resignation” has made it difficult for clinics and hospitals to recruit and retain new talent. Even if a healthcare organization is able to find prospects, budget limitations often prevent them from expanding their staff enough to meaningfully reduce burnout.

Technology (in particular, artificial intelligence) may become a viable solution for many healthcare organizations. Here are four ways that AI can prevent and reduce burnout for clinicians.

1. Reduce “Noise” – Healthcare providers – and cardiac teams, in particular – have increasingly turned to remote patient monitoring (RPM) in recent years to help detect potential health problems in patients before they become more serious. A cardiologist might track metrics such as physiologic monitoring (i.e. weight, blood pressure) from external devices as well as feeds from devices such as pacemakers or implanted loop recorders. But despite the opportunity to deliver care more proactively to improve outcomes, remote monitoring also presents challenges for clinicians. The volume of false positive alerts is a significant burden. Up to 90 percent of alerts from RPM systems are merely “noise” that do not require intervention. However that’s only after having spent enough time reviewing each patient clinical context that you can conclude this actually was noise. Monitoring devices are designed to be extremely sensitive so that no critical event is missed. But it can be extremely taxing for clinicians to constantly monitor these systems and weed out the false positives. AI-powered tools can do this work on behalf of clinicians, allowing them to focus their attention on the patients who truly require care.

2. Serve More Patients – By reducing the number of non-events on their radar, clinicians can increase the number of patients they are able to serve. In fact, we’ve seen this effect even without the use of AI. We worked with one clinic that was able to use our universal data platform to scale up their remote monitoring program from 200 patients to 2,500 patients over the course of 18 months. Again, this sort of growth is possible just by adopting a platform that centralizes data and increases clinician efficiency. By adding AI to triage that data, clinicians will be able to reach even more patients by enabling physicians to target care to those truly in need.

3. Simplify Billing – The reimbursement process for RPM can be a tedious and time-consuming process for clinicians without the use of technology to help streamline data. Without some kind of intelligence (even good old fashion AI based on formal logical rules) in the mix, clinicians may be leaving huge amounts of money on the table. Most U.S. hospitals currently only bill for 10 to 20 percent of their remote monitoring of pacemakers, for instance, simply because they don’t have the staff and resources to handle the increased volume of data and information generated from remote monitoring devices. This lost opportunity equates to up to $3 to $4 million a year in lost revenue at many organizations. By automating billing, hospitals and clinics can recoup this money, while keeping clinicians’ focus on healthcare. And healthcare organizations can dedicate a portion of these funds to paying for new staffers, helping to alleviate burnout for their existing clinicians.

4. Improve Outcomes – As I mentioned before, clinicians aren’t merely overworked. They’re also overburdened by the stress of trying to achieve positive outcomes for as many of their patients as possible. AI in healthcare is still something of an emerging technology, and we admittedly don’t yet have a great deal of hard evidence around exactly how – and how much – AI will improve outcomes. But it’s worth noting that industry observers widely expect this to happen. In a 2021 survey, large portions of healthcare executives said they were excited about the potential for AI to improve virtual care (41 percent), diagnosis (40 percent), and the interpretation of medical images (36 percent).

And then there’s this snippet from a 2020 McKinsey & Company report, which neatly sums up what many of us expect to see in the coming years: “AI can lead to better care outcomes and improve the productivity and efficiency of care delivery. It can also improve the day-to-day life of healthcare practitioners, letting them spend more time looking after patients and in so doing, raise staff morale and improve retention.” I think while we certainly hear a lot about AI, we see it less that we should, but its impact will be greater than we think.

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