The Great Resignation of 2021, which has resulted in millions of Americans quitting their jobs, has – unsurprisingly – hit the healthcare industry hard.
Many resignations across all industries are spurred by an emotionally jarring and unprecedented pandemic that is now coming upon its third year, prompting many workers to reconsider the trajectory of their lives and careers. The toll has been particularly difficult for healthcare workers on the frontlines of Covid-19 care, many of whom are understandably burned out. According to the Bureau of Labor Statistics, a whopping 534,000 U.S. health care workers left their jobs voluntarily in August alone.
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Amit Phadnis Amit Phadnis is a GE Officer and Chief Digital Officer of GE Healthcare, responsible for leading the company’s Digital and AI strategy and business.
With demand for healthcare workers expected to keep increasing in the coming years, the loss of workers – especially among those who decide to leave the industry altogether – poses a severe threat for hospitals and other medical facilities already overwhelmed by patients.
There are no easy answers to the healthcare labor crisis. But there are resources medical institutions and their teams can draw on to relieve some of the burden from overworked, stressed-out physicians, nurses and staff – and many of them come in the form of technological advancements that may transform how we offer care in the years to come.
Many of these are coming to bear right now. Here are five predictions for healthcare advancements we’ll see gain further traction in 2022.
Prediction: More burnout-busting innovations are on the way.
Burnout has long been a serious issue among healthcare workers, but Covid-19 certainly made it worse. In fact, 79% of radiologists, neurologists, cardiologists and critical care physicians who say they feel burned out today actually felt similarly before the pandemic. And a key cause of that stress and fatigue is an abundance of administrative duties and the “data deluge” required to track and follow-up with patients – a longstanding issue exacerbated by the tidal wave of patients suffering from Covid-19.
A Deep-dive Into Specialty Pharma
A specialty drug is a class of prescription medications used to treat complex, chronic or rare medical conditions. Although this classification was originally intended to define the treatment of rare, also termed “orphan” diseases, affecting fewer than 200,000 people in the US, more recently, specialty drugs have emerged as the cornerstone of treatment for chronic and complex diseases such as cancer, autoimmune conditions, diabetes, hepatitis C, and HIV/AIDS.
Fortunately, improvements in technology are reducing that burden. Using new and improved algorithms that quickly and efficiently assess mounds of patient data, while also removing certain repetitive tasks, clinicians are able to unearth the information and insights needed to efficiently treat their patients. Whether it’s a device, or department or enterprise-wide workflow, we are working to use data, analytics, and AI to first provide insights and then use those insights to automate repetitive tasks and improve workflow efficiencies. We believe it is possible to see a 30% improvement in efficiency through such technologies and software. Patient flow can be managed better by providers, even in overtaxed emergency rooms, and that gives clinicians more time to do the work for which they were trained.
Prediction: Clinicians will decide which AI tools are right for them.
Building on the previous point, advancements in data analytics and artificial intelligence are giving clinicians and support staff access to numerous new tools to make their tasks easier to complete. But are they really doing the job?
As with any new advancements, the learning curve can sometimes be steep. In fact, a recent report revealed that slightly less than half of the AI tools being studied by radiologists that could directly contribute to patient care actually led to an increase in the number of exams a radiologist performs in a given amount of time. Most of the rest do not change that number (or therefore the radiologists’ efficiency) but still could directly contribute to patient care.
Clinicians are eager for tools that seamlessly integrate into their existing workflows, limit screen time and the effort required to input data. My prediction is they will embrace those AI resources that work spectacularly – such as deep-learning image reconstruction technology embedded on an MR device that delivers high-quality resolution and shorter scan times – and ignore those that don’t. The winning AI technologies will emerge in ’22, and their effect will be dramatic. When it comes to use of AI to improve off – device workflows, either operational or clinical, those AI models that factor in multi-modal datasets (population health information, social determinants of health, genetic information, economic status, multi-modal clinical data etc.) tend to be more accurate and precise as compared to those models which are built on single – factor data (single modal information).
Prediction: High-tech solutions will eliminate many healthcare inequities.
A longstanding problem in the U.S. is health inequity, as many people from disadvantaged or historically oppressed groups are often at greater risk for poor health outcomes. And the pandemic only worsened the problem.
Since its onset, for example, people of color, American Indians and Alaska Natives have had the highest hospitalization rates for Covid-19. Plus, fears about contracting the virus and the loss of health insurance led to a significant drop in the number of regular screenings for cancer and other diseases. Consequently, it is expected that these delays or missed screening appointments have had negative impacts on early detection and diagnosis, leading to an increase in deaths or severe illness.
But technology is again riding to the rescue, with advancements that hold the promise of enabling health equity for almost everyone by creating new pathways to care. Telehealth exploded in 2020, out of necessity, but is becoming the delivery method of choice for millions. Remote monitoring devices may provide the ability to check on patients in rural areas or who have difficulty finding transportation to get to the doctor. Further, the use of predictive analytics is helping identify at-risk patients before they incur a disease, so that preventive steps can be taken.
Prediction: Precision medicine will drastically improve medical outcomes.
The industry has made tremendous advances with technologies that help diagnose and prevent disease. In 2022, genomics — the study of a person’s genes or DNA — will move to center stage, as we will see the availability of tools and techniques to treat diseases and disorders based on each person’s genetic fingerprint, environment and lifestyle.
In doing so, we will be replacing the one-size-fits-all approach to medicine with precise treatment solutions that are revamping legacy care delivery models in ways that will significantly improve patient outcomes.
Secondly, use of multi-modal data, including genetic information, imaging, digital pathology and other multi-modal information will enable precise detection of disease state early and the progression, thereby making therapies a lot more effective, while at the same time, lowering the cost. One of the challenges of taking diagnostics upstream, especially in the U.S., is the current reimbursement models. The need and effectiveness of upstream diagnostics and therapies will accelerate the value-based care paradigm in 2022.
While healthcare providers have been facing tremendous burdens, with or without the pandemic, hope and help is on the horizon. As healthcare technology continues to improve, so, too, will the mental, physical and emotional states of the millions of individuals who are devoting their lives to caring for others.
Photo: Nuthawut Somsuk, Getty Images
Amit Phadnis is a GE Officer and Chief Digital Officer of GE Healthcare, responsible for leading the company’s Digital and AI strategy and business.
