Can AI Address Workforce Shortages & Burnout in Healthcare?

Care quality is a critical part of value-based care success, but workforce shortages and burnout threaten healthcare organizations’ ability to deliver optimal care.  

Value-based care (VBC) is centered around core tenants:

• Optimizing patient outcomes

• Improving patient satisfaction and healthcare experiences

• Lowering total cost of care

• Addressing physicians’ and health professionals’ well-being

• Addressing health equity

A critical factor in achieving many of these goals is having a robust clinical workforce to ensure timely access to high-quality, effective, and efficient care. When the healthcare field experiences workforce shortages and extreme burnout, everyone – from high-level administrators and front-line staff to the patients coming in for care – feels the repercussions.

Workforce shortages and burnout are also a threat to our ability to forge ahead in our efforts to go from a fee-for-service to a value-based care system in America.

The Critical Link Between a Healthy Workforce & Care Quality

The dual challenges of staffing shortages and workforce burnout in healthcare have been ongoing in U.S. healthcare for decades. However, COVID-19 exacerbated both in devastating ways.

The extreme demand for highly skilled healthcare workers to care for patients through the pandemic, combined with the high-stress work environment and an environment where healthcare workers’ motives and qualifications were questioned, pushed many otherwise qualified healthcare workers out of the profession. Fewer people wanted to enter the profession, and those who were already in it reported alarming levels of burnout and stress.

Hundreds of studies have shown that workforce shortages, stress. and burnout, have direct links to care quality and patient outcomes. In one study, physicians who reported symptoms of burnout were more than twice as likely to report a medical error, and the rates of medical errors were about three times as high in medical work units (even ones rated as extremely safe) if physicians working on that unit had high burnout levels. The administrative demands of things like charting, billing, and reporting only add to the challenges.

All these issues make it more difficult to enter, and succeed in, value-based care programs. These models link quality with financial incentives, and missing quality targets are directly tied to revenue and reimbursement. Aligning financial incentives with care outcomes rather than volume is central to VBC. But when extraneous forces like workforce shortages obviously impact quality and outcomes, how can organizations that want to transition to VBC make it work?

Can AI Help Us Combat a Healthcare Workforce Shortage?

There is no simple solution to the challenge of finding enough highly skilled healthcare workers to fill current and future needs. But there are ways organizations can lighten the load for clinicians and staff who are overworked and overwhelmed.

Much of the hype over AI in medicine has centered around a future vision of curing cancer, discovering new drugs, and personalizing medicine. All of these are exciting, and worth pursuing. Most of these will also take years or decades to realize.

But there are ways AI can mitigate the impacts of workforce shortages today while simultaneously improving care quality and patient outcomes:

• Automating manual tasks, such as data entry and transferring information between systems. For example, AI can bring together information from disparate data sources. It can quickly cleanse and normalize information from multiple databases to create a single source of truth and facilitate seamless data exchange that results in better decision making and improved outcomes.

• Analyzing healthcare data to identify patterns and find critical details that humans might miss. Healthcare organizations have access to an unprecedented amount of data today. Each piece of data represents a small part of the whole picture for a patient, or a population of patients. With AI tools to analyze it and piece together critical data points into a more comprehensive treatment picture, the information provides tangible benefits to direct appropriate care.

• Predicting disease risk to intervene earlier and improve care outcomes, particularly for patients at risk of developing chronic diseases. Most conventional data tools use rules-based logic to identify chronic disease patients after diagnosis. By then, doctors can only mitigate disease progression or treat symptoms. Predictive AI can identify those at higher-than-average risk of developing these diseases, giving healthcare teams a head start to avoid or significantly delay the onset of things like diabetes, coronary artery disease, congestive heart failure, and chronic obstructive pulmonary disease (COPD). 

• Designing administrative and care workflows that streamline patient access, care, and engagement. AI tools can analyze patient populations, create cohorts based on specific care needs, and design workflows that steer patients to appropriate care for their unique clinical needs and diagnoses, all without any manual work for staff members. 

• Providing clinical decision support at the point of care to help physicians and clinical staff make more efficient and effective decisions and avoid errors that could impact care quality and outcomes.

AI won’t be the only solution to address our healthcare workforce needs moving forward. It won’t replace the need for skilled humans to work with patients and provide exceptional care. But AI – and other technologies – can be a critical tool when designed appropriately, and used ethically and responsibly, to mitigate the negative impacts of workforce shortages and burnout among healthcare workers and help us move toward a necessary value-based care future.