Can AI Save Physicians from Burnout?

In the past decade, physician burnout has evolved from a serious concern to a troubling epidemic, affecting 50 percent of physicians and physicians-in-training. Excessive workloads, process inefficiencies, and administrative burdens related to electronic medical records (EMR) documentation are hampering productivity and significantly impacting physician well-being.

In the United States, physicians spend between 34 and 55 percent of their workday compiling clinical documentation and reviewing EMRs. While some of this contributes to ongoing patient care, much is done in the name of billing documentation, litigation defense, and regulatory compliance. These excessive documentation requirements also strain the patient-physician relationship, reducing the time patients spend with their doctors and hampering effective communication and care.

“Many health care organizations are turning to artificial intelligence (AI) as a potential solution to physician overwork and burnout.”

Studies have shown that medical scribes are efficient substitutes for relieving administrative burdens and increasing physician productivity. However, they are not widely used due to high initial cost, patient unease, and high turnover rates.

Now, many health care organizations are turning to artificial intelligence (AI) as a potential solution to physician overwork and burnout. After all, AI offers long-sought-after benefits for clinicians, such as reduced clinical documentation time and improved focus on patients during visits, plus more accurate visit notes.

However, clinicians’ well-being is unfortunately not always the top priority for hospital executives. Currently, most US hospitals generate revenue based on “fee-for-service” payment models, which pay health care providers a certain price for each procedure performed or patient seen, without explicit rewards for the quality of the services rendered. This model translates internally into an incentive system that prioritizes patient volume over patient value. That is, physicians are incentivized to maximize revenue by seeing and treating as many patients as possible.

Rewards for quality and value creation are rare, if at all present, in physician compensation plans. As time previously devoted to compiling EMR documentation becomes available, institutions may increase their expectations of the patient and procedure volumes physicians perform. That is, AI could inadvertently serve to exacerbate the patient-volume problem, rather than relieve physician workloads and improve the patient experience.

Health care is at a turning point: We need policies and organizational practices that align physician well-being with patient value, rather than putting them at odds, allowing AI to be used for the greater good.

Many health care organizations are starting to use AI

AI, which encompasses machines and software capable of reproducing human behavior in solving complex problems, can be trained on large datasets and perform tasks related to searching, reasoning, and learning. In health care, AI assists doctors in diagnosing diseases, prescribing optimal treatment plans, and enhancing patient engagement with personalized approaches. Additionally, AI can handle administrative tasks, promising to ease clinician workloads, increase job satisfaction, decrease burnout, and improve patient care quality.

Many health care organizations are starting to harness the transformative potential of AI. For example:

CommonSpirit Health recently announced the launch of “Insightli,” an AI tool aimed at streamlining workflows and creating customized content.
In 2022, Amazon introduced Amazon Clinic, a virtual health care service using AI to provide affordable treatment for common conditions.
In October 2023, 10,000 clinicians and staff at the Permanente Medical Group
started using AI to reduce clinicians’ EMR documentation time and facilitate “more personal, meaningful, and effective patient interactions,” according to the organization.
Several hospitals across the United States have piloted AI scribes to address the growing burden of data entry and clinical documentation. Generally, these tools use smartphone microphones to transcribe physician‒patient encounters, then leverage machine learning and natural language processing to convert verbal interactions into summarized, accurate visit notes and suggest billing codes—all without retaining audio recordings to protect sensitive information.

AI’s ability to enhance clinical documentation, billing, and reimbursement processes while allowing physicians to focus on patients has the potential to decrease burnout rates, increase job satisfaction, and improve patient care. Given these early successes, more widespread adoption of AI seems only a matter of time.

What’s getting in the way of progress?

But there’s a problem: Most US physicians do not practice within systems that prioritize physician or even patient well-being. The predominance of fee-for-service payment models often leads to incentive systems prioritizing patient volumes over patient value
to maximize hospital revenue and physician compensation.

Unfortunately, attempting to save time with an AI tool in taking visit notes may actually translate into increased patient loads. If volume-driven financial incentives remain unchanged, there may be requests to use that freed-up time to see even more patients. Therefore, AI could perpetuate a cycle of higher workloads, leading to burnout, clinical errors, and poorer patient outcomes. History has shown that payers are hesitant to self-regulate in changing their profit-driven models, necessitating intervention from other parts of the health care system.

In a country primarily using the fee-for-service model while struggling to control costs, there is also a risk of embedding questionable practices into AI algorithms. For instance, upcoding—when providers inflate the severity of a patient’s condition for higher reimbursement—could be institutionalized in AI systems that are trained on past behaviors. This could lead to AI scribes being biased toward upcoding, reflecting volume-based revenue maximization incentives.

What needs to be done?

The promise of AI to improve provider well-being and align the health care system toward value delivery for patients is unlikely to materialize without changing payment models and incentives to promote value over volume. Achieving such a seismic shift in incentive structures will require time, energy, and collaborative efforts.

As technology advances, so must the policies and systems that define and regulate its use, including updating the associated incentive mechanisms. For example, European countries, particularly the United Kingdom’s National Health Service, have gradually adopted bundled payments to prioritize patient outcomes and best practices in their reimbursement structures. Other countries may want to follow suit.

“AI automations of clinical documentation offer significant benefits for physician productivity and well-being.”

Critics of the push from volume to value often argue that reducing the emphasis on patient volume may impact access to health care. Thus, it is imperative that expectations for clinicians include a healthy balance between volume and value. Without proper boundaries, a short-sighted response to volume maximization, even when justified as much-needed access expansion, will likely come at the cost of increasing burnout, leading to physician shortages in the long term.

AI automations of clinical documentation offer significant benefits for physician productivity and well-being. However, to ensure AI does more good than harm, complementary changes to financial incentive systems must accompany their adoption. We cannot afford to wait for payer organizations to make the first move. Health care institutions must lead the charge by updating technologies and internal incentive structures to protect providers and patients now and in the future.

Susanna Gallani is the Tai Family Associate Professor of Business Administration in the Accounting and Management Unit at Harvard Business School, where she studies performance management systems in health care and value-based health care implementation strategies. Lidia Moura is a clinical neurologist and director of neurology population health at Massachusetts General Hospital (MGH) and associate professor of neurology at Harvard Medical School. She is also an OpEd Project Fellow and director of the Center for Value-based Healthcare and Sciences with MGH. Katie Sonnefeldt is a research associate at HBS, supporting research in performance management in health care and health equity.