Using Generative AI to Prevent Physician Burnout

If you've ever watched Hollywood's take on the medical life, and wondered if there really is as much drama and fatigue among physicians as Hollywood depicts, you'd be surprised. This is one instance where fiction mirrors the facts accurately. Physician burnout is no Hollywood exaggeration. It's real and it's happening.

A recent study indicated that 50% of physicians have experienced at least one classic symptom of burnout, ranging from a reduced sense of personal accomplishment to emotional exhaustion, depersonalization, inability to connect with family members, and more¹. In a 2021 Mayo Clinic Proceedings study, more than 3 per 5 physicians surveyed were experiencing burnout symptoms². The Medscape Physician Burnout & Depression Report 2023: "I Cry and No One Cares" showed that rates of burnout increased to 53% this past year, from 47% in 2021, and jumped 26% since 2018³.

Clearly, America's physicians are struggling. Why does this matter? Because if we do not find a solution to keep our caregivers healthy, the whole nation's health suffers.

Impact of Physician Burnout

The repercussions of burnout are far-reaching. For one, burned-out physicians are often incapable of providing high-quality, compassionate care. Secondly, it minimizes their potential—doctors end up reducing their work hours, changing careers, retiring early, or exiting the medical industry altogether. This exacerbates the growing problem of physician shortage and affects an already overburdened healthcare system. However, that is not the worst of it. The estimated cost of burnout to the US healthcare system is $4.6 billion per year, so it negatively impacts the economy as well.⁴

Physician burnout also directly impacts patients and has been linked to lower patient satisfaction, adherence to treatment plans, and major medical errors. Clinicians suffering from burnout are twice as likely to be involved in patient safety incidents according to research published in a BMJ Journal.⁵ And a study led by researchers at the Stanford University School of Medicine states, “The epidemic of physician burnout may be the source of even more medical errors than unsafe medical workplace conditions”⁶. Not only does this put patients at risk, but it raises ethical concerns.

The Association of American Medical Colleges (AAMC) has declared physician burnout as a public health crisis⁷. They state that burnout leads to cynicism, lower productivity, substance abuse, and suicide. With the physician shortage expected to leave the system short of nearly 139,000 doctors by 2033, healthcare delivery will suffer if the burnout crisis is not resolved ⁸.

This is where we believe that Generative AI can change the narrative and emerge as a definite game changer. Even the AAMC has called for innovative strategies like artificial intelligence to ease the documentation burden on physicians.

Why AI in Healthcare Could Be the Next Big Thing

A Forbes article, titled, “Generative AI: The Next Frontier of Healthcare,” notes that with the digitization of medical records, rising costs, and administrative burdens, the healthcare industry's readiness for AI integration has never been more evident⁸. They cite three primary reasons for this receptiveness.

One—the wealth of healthcare data in various formats pouring in from various quarters, including IoT health devices, smart watches, digitized patient records, clinical notes, and medical imaging. All of these are now accessible for natural language processing and computer vision analysis.

Two—a slew of regulatory changes, like the adoption of the Fast Healthcare Interoperability Resources standard, that have facilitated data exchange and interoperability.

Three—with nearly 94% of HCOs lacking any established generative AI strategy, there is a strong incentive for early adopters.

Potential Use Cases of Generative AI in Healthcare

While Gen AI is a powerful tool that can revolutionize healthcare, choosing use cases carefully is especially important because of security and privacy concerns. Some of the critical ways in which AI could help are:

1. AI-powered scribes: Physicians spend a lot of valuable time taking accurate patient notes, summarizing patient encounters, completing forms, and handling other administrative clerical work. By leveraging voice recognition, natural language processing, and other AI techniques, physicians could automate these tasks. Reducing the entire burden of paperwork would help physicians prioritize patient care.
   
   
2. Summarizing clinical data: Usually, doctors spend several minutes analyzing a patient’s data from disparate sources, including electronic health records, imaging reports, lab results, clinician notes, and more. Gen AI synthesizes data in seconds, presenting doctors with a concise dashboard for informed decision-making. It can flag the most relevant health status details, current and past medications, procedures, historical trends, abnormal lab values, potential warning signs, and other key issues.
   
   
3. Treatment recommendations: Advanced AI systems function like an intelligent clinical decision support system, helping physicians identify the most appropriate therapies and interventions. It sifts through voluminous medical datasets, clinical guidelines, and the latest research findings and suggests personalized treatment plans. AI-generated recommendations also provide a second set of eyes, ensuring that all care protocols are followed.
   
   
4. Predictive analytics: AI predictions help improve patient outcomes by equipping physicians to deliver proactive and preemptive care. By analyzing extremely large and diverse health datasets using deep learning techniques, AI systems accurately forecast future risks of diseases, complications, readmissions, and other adverse events. As a result, physicians can avoid potentially negative outcomes.
   
   
5. Sentiment analysis: Advanced NLP and speech recognition tools can listen in on patient-physician interactions and assess the patient’s emotions, state of mind, personality dynamics, and subtle psychological conditions. As a result, physicians gain insight into the patient’s mental health and can better address critical stressors, anxieties, fears, or early signs of depression that may otherwise go undetected.

Generative AI and the Advent of Medicine 3.0

Gen AI will also likely play a prominent role in ushering in Medicine 3.0—a term coined by Peter Attia, M.D. — that shifts the model of care from disease treatment to prevention. With its ability to analyze and synthesize data from various touchpoints, Gen AI could be the very life force behind this transition. By sifting through varied streams of health data—ranging from electronic health records and genomics to wearable device outputs and environmental factors—Gen AI can detect subtle patterns and correlations at a speed and scale that far outweighs human capability.

Physicians can identify potential health risks and predict disease onset long before symptoms manifest. This would not only revolutionize patient care, enhance health outcomes, and reduce the incidence of late-stage diseases and extensive treatment, but it would also align with the broader federal objectives of reducing healthcare costs and alleviating the pressures on overburdened medical systems.

What Lies Ahead: Addressing Concerns

Despite its obvious potential, Generative AI adoption in healthcare has not been met with universal fanfare. Perhaps, one reason for the lackluster reception is the fear that GenAI will negatively impact patients. Can outcomes be trusted? Can recommendations be taken at face value? Will the gain be worth the pain? These are valid concerns. One way to address these is to realize that the rationale for using GenAI can never (and should never) replace the physician’s diagnosis.

Instead, the goal of GenAI adoption would be to assist the physician and help offload administrative tasks. By augmenting the physician’s capabilities and time, GenAI would allow caregivers to spend more time with patients, improving the overall outcome.

Yet another problem is the one of bias. Most AI systems need vast amounts of data to train on. If the training data does not reflect the diversity of patients, the system would unintentionally bake in prejudices, which could lead to severe ramifications like inaccurate diagnoses, treatments that work for some groups but not others, and even life-threatening outcomes. Before it can be scaled or integrated into routine clinical care, AI must find ways to learn without perpetuating the biases and gaps in existing data.

Gen AI developers should also focus on increasing transparency and explainability so physicians can trust AI recommendations. The model should offer supporting evidence and logic behind its suggestions so they can be validated.

Ultimately, change management will be critical, since AI will involve major workflow changes and require organizational buy-in. When used well, Gen AI has the potential to make medicine more efficient and personalized while empowering doctors to focus on human aspects.

In Conclusion

The physician burnout epidemic is at a critical juncture. If unresolved, it will exacerbate the doctor shortage issue, diminish the quality of care, increase risks to patients, and inflict tremendous costs. While concerns about generative AI in healthcare are valid, its judicious use could provide much-needed relief to overwhelmed clinicians.

With AI automating tedious administrative tasks, synthesizing patient data, augmenting decision-making, and predicting risks, physicians can turn their focus to delivering compassionate, personalized care. Happier, more satisfied physicians lead to better health outcomes. While it is not without potential risks, these can be averted when developers prioritize transparency, address biases in training data, and position AI as an assistive rather than autonomous technology. With prudent integration and change management, AI is poised to address the looming public health crisis. When caregivers are empowered, patients ultimately stand to benefit the most.

References

1. Patel, Rikinkumar S., et al. "A Review on Strategies to Manage Physician Burnout." National Library of Medicine, 2019, https://doi.org/10.7759/cureus.4805. Accessed 5 Apr. 2024.
2. Shanafelt, Tait D., et al. "Changes in Burnout and Satisfaction with Work-Life Integration in Physicians During the First 2 Years of the COVID-19 Pandemic." Mayo Clinic Proceedings, vol. 97, no. 12, 2022, https://doi.org/10.1016/j.mayocp.2022.09.002. Accessed 5 Apr. 2024.
3. "Medscape Physician Burnout and Depression Report: Burnout Worsening, Depression Increasing." Medscape, 27 Jan. 2023, www.prnewswire.com/news-releases/medscape-physician-burnout-and-depression-report-burnout-worsening-depression-increasing-301732504.html. 
4. Powell, Alvin. "Study: Doctor Burnout Costs Health Care System $4.6 Billion a Year." Harvard Gazette, 12 Jul. 2019. https://news.harvard.edu/gazette/story/2019/07/doctor-burnout-costs-health-care-system-4-6-billion-a-year-harvard-study-says/
5. Wise, Jacqui. "Burnout Linked to Suboptimal Patient Care, Study Finds." The BMJ, 2018, https://doi.org/10.1136/bmj.k3771. Accessed 5 Apr. 2024.
6. White, Tracie. "Medical Errors May Stem More from Physician Burnout than Unsafe Health Care Settings." Stanford Medicine News Center, 8 Jul. 2018, med.stanford.edu/news/all-news/2018/07/medical-errors-may-stem-more-from-physician-burnout.html. Accessed 5 Apr. 2024.