Mitigating clinician burnout in healthcare with GenAI

Mitigating clinician burnout in healthcare with GenAI


Amidst the rising shortage of healthcare workers, GenAI shows significant promise in reducing the burden of care delivery on burnt-out clinicians.

Clinician burnout is a rampant issue in the most sophisticated healthcare systems today. Surveys indicate that there is a shortage of 7m healthcare workers globally, which will rise to 13m by 2035.[1] This, in addition to the rising disease burden, has turned physician and clinician burnout into a pressing issue in care delivery. What’s more, burnout rates are above 50% in critical specialties like emergency medicine, obstetrician gynecology, oncology, and radiology.[2]

Why clinician burnout needs an urgent solution?

Clinician burnout causes depression among front-line care delivery workers. But more importantly, it also has a negative impact on patient outcomes. Nearly 40% doctors get easily exasperated with patients, and over 25% are less careful with patient notes when experiencing burnout. Both of these factors erode the efficacy of care delivery and lead to a lack of empathy in doctor-patient interactions.

A closer look at the causes of clinician burnout

Interestingly, a plethora of bureaucratic tasks like charting and paperwork is the top cause of burnout according to 62% physicians.[3] This is not surprising, as care delivery involves a great deal of paperwork, note-taking, and charting – most of which is carried out by doctors as they attend to patients. This leads to too many hours at work – something that is felt by over 40% doctors today.[4]

But what is surprising, is that the top cause of clinician burnout can be readily addressed by a key piece of modern technology – and that technology is generative AI (GenAI).

How to mitigate clinician burnout with GenAI

Generative AI finds numerous important applications that can accelerate clinicians’ workflows. Amongst these, here are the top use cases that can help mitigate clinician burnout.

#1. Applying Generative AI for documentation and summarization

GenAI can significantly reduce the time clinicians spend on documentation, summarizing, and reporting tasks by automating processes across various stages of patient care.

One key application is querying patient history for essential details like allergies or genetic predispositions. Instead of sifting through years of medical records, clinicians can rely on GenAI to quickly surface relevant information, thus saving time and improving decision-making.

Similarly, summarizing clinical notes using GenAI helps clinicians focus on the most critical information in patient records. By condensing subjective symptoms, objective assessments, and treatment plans, GenAI accelerates care delivery and ensures that no vital details are overlooked.

GenAI also assists in summarizing radiology reports and bloodwork, helping clinicians interpret diagnostic data faster and more accurately. It highlights key trends and offers insights into potential diagnoses, even suggesting treatment plans by referencing other patient cases.

Furthermore, automating discharge summaries and patient handover documentation allows doctors to generate standardized reports using data from EHRs and lab results. These summaries maintain consistent terminology and structure, which clinicians can quickly review and sign off, saving considerable time while maintaining accuracy.

Together, these applications streamline workflow and enhance both efficiency and accuracy in medical settings.

#2. Streamlining patient interactions and ongoing care with GenAI

Doctors spend a major fraction of their time looking at patient monitoring systems, interacting with patients, and revising their medication based on symptoms and new research.

In each of these areas, GenAI can help clinicians save a significant amount of time. GenAI integrations can flag important findings from patient monitoring and alerting systems to ensure that doctors spend time only on the most relevant details.

Similarly, GenAI can draft responses to patient queries in post-care scenarios. Clinicians can then simply verify and edit these responses to deliver a more empathetic experience to their patients without spending too much time. Likewise, patient symptoms can be analyzed by GenAI models to find suitable alternatives to drugs based on the latest research.

Next steps

GenAI is an important tool for mitigating clinician burnout. However, given the critical and sensitive nature of the above applications, it is important to build GenAI use cases in a fail-proof fashion. The most important consideration is to offload the task of prompt engineering from clinicians, by offering well-tested prompts built into the solution.

Lastly – before these applications are rolled out, it may be prudent to hold working sessions to educate doctors about the right way to leverage the technology. Working with an experienced technology partner can help hospitals effectively navigate these issues and build resilient GenAI solutions.

<Contact Us> for more details about our GenAI solutions to mitigate clinician burnout.


[1] https://www.passmed.uk/doctor-shortage/
[2] https://www.advisory.com/daily-briefing/2024/01/31/physician-burnout
[3] https://www.advisory.com/daily-briefing/2024/01/31/physician-burnout
[4] https://www.advisory.com/daily-briefing/2024/01/31/physician-burnout


Saurabh Jain
Author:
Saurabh Jain
GM Business Development

Knowledge Center

Blog

Is Hadoop Holding Your Business Back? Have you tried Databricks?

Hadoop, initially developed to address the challenges of big data processing, has become a cornerstone ...

Read More
Blog

Enhancing clinical decision-making quality with Generative AI

With the ubiquity of patient data and clinical knowledge, GenAI can play a significant role ...

Read More
Blog

Mitigating clinician burnout in healthcare with GenAI

Amidst the rising shortage of healthcare workers, GenAI shows significant promise in reducing the burden ...

Read More
Blog

Reducing the administrative burden of care delivery with Generative AI

With corporate functions accounting for a major chunk of the healthcare spend, hospitals must identify ...

Read More
Blog

How Databricks Simplifies ML Model Development

Simplifying Machine Learning: Databricks’ Scalable and Collaborative Approach Machine learning (ML) model development is widely ...

Read More
Blog

Enhancing patient outcomes in healthcare with modern data lakes

With the rising volume of patient data and growing AI applications, healthcare organizations need robust ...

Read More