Hospital workers have always been under immense pressure, but over the past decade, this strain has only continued to rise. Even before the onset of the COVID-19 pandemic, hospital staff were facing numerous challenges that were taking a toll on their well-being. From an aging population to the shift of less acute patients to outpatient options, healthcare workers have been dealing with the reality of a patient population getting increasingly sicker each year. At the same time, staffing levels remain constant or, in some cases, are declining, leaving workers feeling overburdened and stretched thin.
In fact, a recent study by McKinsey & Company found that the current nursing shortage will have a gap of over 200,000 positions by 2025. Caring for patients at their most vulnerable can be incredibly stressful in the best of times. When stretched too thin, healthcare staff face feelings of guilt because patients do not receive the levels of care they deserve – calling into question even staying in the profession.
This is a growing issue – a study in the journal JAMA Health Network found that burnout was reported by 40 percent to 45 percent of clinicians in early 2020, 50 percent of clinicians in late 2020, and 60 percent of clinicians in late 2021. As this issue becomes more prevalent, we are facing the reality of an unprecedented amount of healthcare workers leaving or on the verge of leaving the profession.
It’s evident that the financial constraints facing hospitals will not allow for humans alone to fill this growing gap. While technology innovation – including AI – is not new in healthcare, previous solutions felt too few and too limited or constrained to truly make an impact. Now, by turning to AI that can sense what is happening – and pairing it with cost-effective virtual care models – we can integrate with existing clinical workflows to support and extend the reach of nurses and doctors.
The Early Promises of AI Fall Short
The early mystique of AI solutions (i.e Watson Health) quickly faded and the miracles that received attention masked an overall disappointment for three key reasons. First, in mission-critical environments, the consequences of failure are incredibly high. Just like self-driving cars, almost perfect leaves room for dramatic, irreversible consequences. This meant that there was a certain hesitancy on using the solutions until it was clear they could be trusted. And as we have learned through self-driving cars, driver assist is easier to approach than fully-autonomous self-driving cars. Hospital adoption requires similar caution.
Secondly, the initial AI solutions were not designed with the end user in mind. It was as if your car’s driver assist just outputted a score on your current risk level instead of beeping or warning when you were steering into traffic. The lack of consideration for how nurses and doctors cared for patients today made it difficult for hospital workers to trust the solutions or use them effectively.
Finally, AI solutions were narrow in scope because it was beyond the current state to do more. They were applied in very specific circumstances instead of broadly being relevant to every patient’s care. This positioned them as niche innovations rather than broad strategies to help providers care for all of their patients around the clock.
Shifting AI to Serve the Ones Who Save
The next generation of AI solutions for hospitals have the opportunity to go beyond simple crunching of information collected through existing sensors to themselves sensing what is happening with patients in a manner similar to how nurses and doctors do. This is a field of artificial intelligence known as Computer Vision – where the computer sees what is happening and can break it down into the same kinds of observations that a human would make.
For example, the AI can identify who is in the room, how the patient is moving, if they are stable in bed or trying to rise out of it, whether they are restless in the middle of the night or lethargic in the afternoon, assess the light and sound level in the room, and much more. Rather than reaching a conclusion, the AI is first making observations like a staff member might.
Once the AI has collected this data, it can see patterns in simple and complex ways. The AI no longer feels like a black box to clinical staff because it becomes an extra set of eyes that is watching the patient like you would if you were there. As a result, this form of AI can be instrumental in alleviating burnout by providing support and assistance to the hospital staff in familiar ways.
AI Alone Will Not Be Enough
Ultimately AI in hospitals will incorporate computer vision and instantly analyze the EHR, vital sign data and more to instantly make or aid in key decisions. This however, is even earlier in maturity than proclamations of self-driving cars were a decade ago. We need AI today to prompt nurses and doctors on where their judgment is needed – just like a driver assist lane change warning prompts us to the need for our attention.
Virtual staff members, nudged by AI, can quickly scan across all patients and investigate patients based on known patterns and gather relevant context based on the specific scenario (e.g. assessing patients who are immobile in bed for long stretches for potential pressure injury interventions). They can conduct virtual visits with the patient and communicate with the bedside team to coordinate further actions. Furthermore, this type of human + AI integration limits the amount of change imposed on the bedside team – for the frontline team it is just some extra help they desperately need with the familiar ‘interface’ of another clinical team member.
A Final Note
Hospital staff care deeply about their patients, and helping them provide the best care possible – even when they have to be with other patients – is the single highest return action we can take to reduce burnout in hospital workers. Computer Vision and virtual inpatient teams can team up to help relieve the impossible pressures facing frontline care teams in hospitals. This integrated model can better monitor patients and allow clinical staff to apply their judgment where it is most needed and help create the positive impact they want to have on their patients’ lives.
