One goal of these new functions is to streamline physician workflows. However, these new EHRs may interface differently with clinical laboratory information systems
Artificial intelligence (AI) developers are making great contributions in clinical laboratory, pathology, radiology, and other areas of healthcare. Now, Electronic Health Record (EHR) developers are looking into ways to incorporate a new type of AI—called “Generative AI”—into their EHR products to assist physicians with time-consuming and repetitive administrative tasks and help them focus on patient-centered care.
According to the federal Government Accountability Office (GAO), generative AI “has potential applications across a wide range of fields, including education, government, medicine, and law” and that “a research hospital is piloting a generative AI program to create responses to patient questions and reduce the administrative workload of healthcare providers.”
Reducing the workload on doctors and other medical personnel is a key goal of the EHR developers.
Generative AI uses deep learning neural networks modeled after the human brain comprised of layers of connected nodes that process data. It employs two neural networks: a generator [generative network] which creates new content, and a discriminator [discriminative network] which evaluates the quality of that content.
The collected information is entered into the network where each individual node processes the data and passes it on to the next layer. The last layer in the process produces the final output.
Many EHR companies are working toward adding generative AI into their platforms, including:
As our sister publication The Dark Report points out in its December 26 “Top 10 Biggest Lab Stories for 2023,” almost every product or service presented to a clinical laboratory or pathology group will soon include an AI-powered solution.
“We believe that generative AI has the potential of being a personal assistant for every doctor, and that’s what we’re working on,” Girish Navani (above), co-founder and CEO of eClinicalWorks, told EHRIntelligence. “It could save hours. You capture the essence of the entire conversation without touching a keyboard. It is transformational in how it works and how well it presents the information back to the provider.” Clinical laboratory information systems may also benefit from connecting with generative AI-based EHRs. (Photo copyright: eClinicalWorks.)
Generative AI Can Help with Physician Burnout
One of the beneficial features of generative AI is that it has the ability to “listen” to a doctor’s conversation with a patient while recording it and then produce clinical notes. The physician can then review, edit, and approve those notes to enter into the patient’s EHR record, thus streamlining administrative workflows.
“The clinician or support team essentially has to take all of the data points that they’ve got in their head and turn that into a narrative human response,” Phil Lindemann, Vice President of Data and Analytics at Epic, told EHRIntelligence. “Generative AI can draft a response that the clinician can then review, make changes as necessary, and then send to the patient.”
By streamlining and reducing workloads, EHRs that incorporate generative AI may help reduce physician burnout, which has been increasing since the COVID-19 pandemic.
A recent study published in the Journal of the American Informatics Association (JAMIA) titled, “Association of Physician Burnout with Perceived EHR Work Stress and Potentially Actionable Factors,” examined physician burnout associated with EHR workload factors at UC San Diego Health System. The researchers found that nearly half of surveyed doctors reported “burnout symptoms” and an increase in stress levels due to EHR processes.
“Language models have a huge potential in impacting almost every workflow,” Girish Navani, co-founder and CEO of eClinicalWorks, told EHRIntelligence. “Whether it’s reading information and summarizing it or creating the right type of contextual response, language models can help reduce cognitive load.”
Generative AI can also translate information into many different languages.
“Health systems spend a lot of time trying to make patient education and different things available in certain languages, but they’ll never have every language possible,” Lindemann said. “This technology can take human language, translate it at any reading level in any language, and have it understandable.”
MEDITECH is working on a generative AI project to simplify clinical documentation with an emphasis on hospital discharge summaries that can be very laborious and time-consuming for clinicians.
“Providers are asked to go in and review previous notes and results and try to bring that all together,” Helen Waters, Executive Vice President and COO of MEDITECH, told EHRIntelligence. “Generative AI can help auto-populate the discharge note by bringing in the discrete information that would be most relevant to substantiate that narrative and enable time savings for those clinicians.”
Many Applications for Generative AI in Healthcare
According to technology consulting and solutions firm XenonStack, generative AI has many potential applications in healthcare including:
- Medical simulation
- Drug discovery
- Medical chatbots
- Medical imaging
- Medical research
- Patient care
- Disease diagnosis
- Personalized treatment plans
The technology is currently in its early stages and does present challenges, such as lack of interpretability, the need for large datasets and more transparency, and ethical concerns, all of which will need to be addressed.
“We see it as a translation tool,” Lindemann told EHRIntelligence. “It’s not a panacea, but there’s going to be really valuable use cases, and the sooner the community can agree on that, the more useful the technology’s going to be.”
Since generative AI can be used to automate manual work processes, clinical laboratories and anatomic pathology groups should be alert to opportunities to interface their LISs with referring physicians’ EHRs. Such interfaces may enable the use of the generative AI functions to automate manual processes in both the doctors’ offices and the labs.