Combating clinician burnout: The role of AI in value-based healthcare
While ChatGPT grabs the headlines, there are other uses of artificial intelligence that can support clinicians and aid value-based care efforts.
Clinician burnout has significant implications for value-based healthcare. Value-based care models aim to improve patient outcomes while reducing costs overall by providing high-quality care.
However, when healthcare providers are burned out from being required to perform an overwhelming amount of administrative tasks, the quality of care they provide can suffer, leading to increased costs.
Burnout has been an issue for a while in healthcare, but it was especially exacerbated during the pandemic. A recent report from the AMA found that about 63 percent of clinicians are experiencing burnout and that the largest reported cause of this burnout is because of the significant amount of time spent on non-patient facing administrative tasks in the clinician’s workday. Another study found that clinicians spend anywhere from 2 to 4.5 hours every day logging information into electronic health records systems (EHR). That takes time away from patients, adds to clinician stress and creates an environment in which it is easy for mistakes to be made.
Administrative tasks such as appointment scheduling, data entry, medical coding, prior authorization and patient communication are necessary components of healthcare. Because they are also a leading cause of burnout for clinicians, finding and implementing a manageable solution is necessary to fix the system causing the burnout. Having more satisfied clinicians who believe they are doing good work will help improve the quality of care overall, which is a cornerstone of the value-based care model.
Value-based care, according to the New England Journal of Medicine, is best achieved when providers are rewarded when they improve patients' health, reduce the effects and incidence of chronic disease, and live healthier lives in an evidence-based way. For this system to work, clinicians need to focus on each of their patients’ needs, and have the time to provide thoughtful interventions and advice. In a value-based healthcare system where quality is paramount, clinician burnout can have significant negative effects on both patient outcomes and financial sustainability.
How AI can help
Addressing the administrative burden on healthcare providers through the use of AI-powered tools can help mitigate the risk of clinician burnout and ensure the success of value-based healthcare models.
Talking about AI and machine learning can feel intimidating for clinicians and healthcare leaders. These are relatively new topics, and the speed with which these technologies are advancing is amazing yet overwhelming.
The good news is that AI will never be able to replace a clinician. This type of technology is great for processing large amounts of data, but it does not have human decision making capabilities and lacks bedside manner. While ChatGPT has been at the forefront of conversations about AI in healthcare, there are actually several different AI and machine learning technologies available that help streamline administrative processes and help clinicians focus on what they care about most – the patient.
ChatGPT is an example of a large language model, which encompasses AI tools that tap into large datasets to execute advanced language-related tasks, including reading, summarizing and translating texts, as well as constructing sentences. However, this is just one facet of using AI as a healthcare solution for administrative tasks such as medical documentation. Other components include automatic speech recognition and natural language processing, both of which can generate data from the conversations between clinicians and their patients, effectively delivering an accurate medical note.
Natural language processing can transcribe the interactions between a clinician and a patient and extract relevant information, such as diagnoses, medications, new health issues and treatment plans from a conversation. This can save healthcare providers time and reduce errors in the patient’s EHR. This technology works for the clinicians and health systems by streamlining the administrative tasks, giving clinicians the time to provide quality care to each patient.
These tools are also more efficient, which can drastically improve the process of performing administrative tasks. AI and machine learning technologies are capable of efficiently sorting, categorizing and analyzing enormous quantities of data in a significantly shorter timeframe. AI systems are often more precise than humans because they eliminate the risk of human error.
Examples of support
An example of how this can be used is with medical billing and coding. AI can automate the billing and coding process, such as assigning diagnostic codes and logging patient data, making it faster and more accurate. This eliminates the significant administrative burden often associated with a value-based care model.
By better understanding the uses of AI technologies and how they can improve value-based care, clinicians can refocus their time on caring for their patients, which will lead to better outcomes and reduced costs. This promotes value-based care, benefiting the clinician, patient, payer and society.
Davin Lundquist, MD, is chief medical officer for Augmedix, which develops real-time support systems to help doctors close care gaps and improve quality.