The Future of the Healthcare Workforce: 5 Predictions For 2022

It has been another torrid year for the world’s healthcare workers. COVID-19 has only compounded the emotional, physical and mental strain on a workforce that was already reporting burnout before the pandemic. The cracks are beginning to show: Healthcare workers are now retiring at a faster rate than anticipated, while demand for them is set to increase in coming years. 

But new technology is riding to the rescue for the world’s overburdened doctors, nurses and support staff. Clinicians are increasingly harnessing artificial intelligence (AI) — which works by gathering and analysing mountains of data, and then churning out actionable insights — to lighten their workload. For example, AI-enhanced software can help reduce the steps involved in imaging a patient, quickly identify abnormalities in a scan, and manage the flow of patients to an emergency room. This frees up clinicians to focus on their core mission: diagnosing and treating patients.
Despite the potential of AI, the industry will need to tread carefully as it adopts the technology or they run the risk of increasing the burden on their workforce and aggravating burnout. In this series of predictions for the coming year, GE Healthcare explains how AI, if correctly applied, can bring relief to the world’s overloaded healthcare workforce.

Prediction #1: Innovation to Reduce Burnout Will Continue

It’s no secret that the world’s clinicians are at a breaking point. Nearly half of all critical care physicians, neurologists and cardiologists say that they’re burned out, along with over 1 in 4 radiologists.[i] It’s a long-standing issue, too: Close to 8 in 10 respondents to the same survey said they experienced burnout before the COVID-19 pandemic.[ii]

Many clinicians blame an abundance of tasks,[iii] such as contacting, coordinating and following up with patients about their appointments and health issues, as the main culprit for their stress and fatigue. These problems have been exacerbated by the pandemic, as clinicians grapple with an influx of patients suffering from COVID-19, and the worsening condition of patients who have postponed routine visits for several months.

Amit Phadnis, chief digital officer at GE Healthcare, explains that “data deluge” can exacerbate the problem. “Clinicians are just seeing so much data; there’s a risk they’ll be completely fatigued just a few hours into their workday,” he says.

But there is good news on this front. Technologists and data scientists will continue to improve the ability of algorithms to gather and analyse those avalanches of data, and then generate a wealth of valuable clinical insights. That has the potential to increase the quality of healthcare, and allow for the fairer distribution of work and decrease the risk of burnout. “This AI assists clinicians by removing a lot of their repetitive tasks and easing their cognitive load, allowing them to focus on the real task at hand,” says Phadnis.

He cites the Venue Family of point-of-care-ultrasound (POCUS) products, with advanced AI and intuitive scanning tools, an easy-to-clean touchscreen, and a small footprint designed to fit in tight spaces such as the ER and ICU. “A lot of tasks are being automated with that device, simplifying the complex and enabling faster assessments that support clinicians in making lifesaving decisions,” says Phadnis. “By deploying AI and automated workflows on our ultrasound devices, clinicians have built-in assistance when viewing high-quality images of areas of the body such as the heart and lung.”

It’s the same story with Edison True PACS, GE Healthcare’s next-generation, cloud-based Picture Archive and Communication System (PACS), to help the right radiologist read exams at the right time. “If I’m a radiologist, I get an AI-enabled system to help segment, quantify and measure images, and provide a truly efficient clinical workflow,” says Phadnis. “It makes my life much easier.”

Prediction #2: Clinicians Will Separate the Wheat From the Chaff When It Comes to AI

There is no doubt that AI tools have enabled clinicians to reshape how they deliver patient care, broaden patient access, and ease their workload. But as AI becomes the new ‘must-have’ technology, especially for triage, the healthcare industry needs to be careful that it doesn’t unwittingly choose systems that add to clinicians’ burden. A recent report revealed that nearly half of AI-enhanced imaging tools would result in an increase in radiologists’ tasks. [iv]

“If AI is not done correctly, or well-integrated into workflows, it can end up increasing the load on clinicians,” warns Phadnis. He explains that some AI systems demand significant interpretation, post-processing and acquisition times, and increase the risk of “workflow fragmentation,” which requires a clinician to divide their attention between several tasks. Over the long term, these issues can damage clinicians’ confidence in AI-enhanced systems.

It suggests that the healthcare industry will become increasingly selective about its AI. Clinicians will seek out tools that limit their time in front of a screen and reduce the number of clicks required to input data. Tools that don’t reduce the workload will be ignored.

Phadnis cites GE Healthcare’s Revolution Ascend with Effortless Workflow and AIR Recon DL as examples of “multimodal” AI systems that harness data and insights into a single model, thereby minimising the burden on clinicians. “You’re getting multiple sets of validation simultaneously in one place, and seamlessly integrating AI into clinical workflow to automate and simplify time-consuming tasks,” he says.

For example, AIR Recon DL is deep-learning image reconstruction technology that works across all anatomies, delivering excellent image quality and resolution, even with significantly shorter scan times [v].  Revolution Ascend with Effortless Workflow utilises AI to automate nearly every step in existing workflows for CT scans, leading to a potential 66% reduction in clicks and a 21% timesaving for the entire exam[vi], [vii].  

Prediction #3: Technology Will Help the Workforce To Reduce Healthcare Inequities

COVID-19 highlighted the health inequities present in the U.S.  Data collected by the University of Minnesota indicates that Black and Hispanic Americans, along with American Indians and Alaska Natives, have higher hospitalisation rates for COVID-19 than white Americans.[viii] Meanwhile, statistics from the Centers for Disease Control and Prevention show that rural Americans are more likely to die from heart disease, cancer, unintentional injury, chronic lower respiratory disease, and stroke than those living in urban environments.[ix]

But clinicians will increasingly harness technology such as handheld technology, remote monitoring devices and telehealth to help address these disparities. Phadnis cites handheld solutions such as GE Healthcare’s Vscan Air, a wireless, pocket-sized ultrasound device that provides exceptionally clear image quality, whole body scanning capabilities and intuitive software, that can fit in the palm of a clinician’s hand. It enables clinicians to quickly triage patients and make fast decisions by ruling in or out a variety of conditions, outside the traditional clinical setting in the field or transit.

He also points to GE Healthcare’s Mural Virtual Care Solution, which offers hospitals a broad view across the mechanically ventilated patient population in their network, and helps clinicians identify patients at risk of deterioration. “It’s awesome to be able to monitor patients in ICU in a remote hospital and keep tabs on the protocol that the facility is using to treat the patient,” he says. “That transfer of expertise and distribution of technology is essential.” A hospital system in the U.S is also using the Mural Solution for Labor and Delivery to monitor patients, including underserved Black, Native American and Latina women post-delivery to help prevent hypertension and hemorrhage.  

GE Healthcare is also aiming to level the MRI learning curve with the recent introduction of SIGNA Prime[1], a 1.5T system designed to make MRI adoption simpler and seamless. SIGNA Prime boasts numerous features for an intuitive user experience and is designed to help deliver consistently high-quality images using cutting-edge technology. The system is designed to help users reduce potential errors in exams, thanks to a guided workflow with step-by-step instructions and a simple protocol selection. To further streamline the experience, technologists can monitor and scan their current patient while the system processes their previous patient’s images.

Prediction #4: Telemedicine Will Become Even More Integral to Healthcare Delivery

The COVID-19 pandemic led to wider adoption of telemedicine worldwide, transforming this once underutilised form of care delivery from obscure to ubiquitous almost overnight. Hospitals will continue to adopt this reliable and cost-effective technology to deliver care as a matter of urgency in 2022.

Phadnis explains how remote monitoring solutions, along with remote clinical training to optimise the capabilities of equipment, can significantly boost the delivery and quality of care. There is a big opportunity in critical care, because of the acute shortage of ICU intensivists, the field’s data integrity issues, and the interoperability of many traditional ICU monitoring systems, he says.   

“The faster we can get the expertise to the patient, detect their condition and treat them, the better the outcome for the patient,” he says. “The costs of care can also be dramatically lower,” he adds.

Prediction #5: Precision Health Will Revolutionise Healthcare Delivery

For decades, treatment for several diseases and genetic disorders was a one-size-fits-all process. In the case of cancer, patients underwent surgery to remove a tumor, and then endured several rounds of chemotherapy or radiation to kill cancer cells.

But nowadays, it is almost unthinkable to treat cancer patients without accounting for their individual genes and specific disease. That is why nearly all cancer drugs that are currently in development have tests that allow clinicians to quickly diagnose if a tumor has a specific genetic change or biomarker that is targeted by the drug.

Thanks to technological advances, especially within genomics, clinicians will increasingly harness precision health in 2022 to treat diseases and disorders based on an individual’s genetic fingerprint, environment and lifestyle. The solutions currently in development are expected to radically change care delivery models and improve outcomes for generations to come.

“Patient diagnosis and therapies will move upstream, with an emphasis on detecting specific disease states early,” says Phadnis. “When combined with precision therapies, we will see improved access and efficacy and reduced costs.”

He adds: “We have to make use of all the patient information available,” and reels off a list: lab reports, scans, genomic information, population risk stratification and information from wearable devices. “As soon as you do that, you get a much deeper insight into the specifics of the patient’s condition.”

GE Healthcare is rolling out a range of solutions that harness precision medicine, allowing clinicians to improve patient outcomes in bone procedures, cardiology, neurology, oncology and other medical specialties. Earlier this year, it introduced StarGuide[x], a next-generation SPECT/CT system whose 12 CZT Digital Focus Detectors scan patients in 3D to provide more information to clinicians. The system is also optimised for theranostic procedures, which combines the delivery of therapy to patients with diagnosis to monitor disease.

GE Healthcare has also announced collaborations with SOPHiA GENETICS, the University of Cambridge, and Optellum to broaden access to precision health for cancer patients, and ultimately improve outcomes for them. The collaboration with SOPHiA GENETICS focuses on digital oncology and radiogenomic analysis, while GE Healthcare is working together with Cambridge academics to develop an application to help improve cancer care. U.K.-based Optellum is a leader in AI-enhanced decision support for the early diagnosis and optimal treatment of lung cancer.