3 Generative AI Use Cases Transforming Healthcare

By / Healthcare Technology

Key Takeaways

  • Generative AI can contribute to administrative tasks and aid in reducing burnout rates among clinicians. Oracle Health and Epic are working towards innovative generative AI models to reduce administrative burden in healthcare.
  • Researchers at Northwestern Medicine have created a generative AI tool that is capable of interpreting chest radiographs. It may facilitate the timely interpretation of chest radiography during emergency healthcare conditions.
  • Generative AI can automate the summarization of lengthy clinical notes, extracting key information and presenting a concise overview for efficient clinical documentation.

Generative AI, one of the fast evolving technologies of 2023 is taking center stage in the healthcare domain. Its many benefits such as clinical diagnosis, drug development, and aiding in admin tasks are capturing the attention of many health tech companies.    

The Generative AI Tracker stated that the healthcare generative AI market was valued at more than $1 billion in 2022. The numbers will continue to grow due to its varied usage in the healthcare industry. 

Without further ado, let’s look at some of the essential generative AI use cases in healthcare. 

Generative AI Use Cases in Healthcare

1. Contribution to Administrative Tasks

Administrative tasks are time-consuming and lead to burnout in clinicians. Doximity polls predict that 46 percent of physicians believe that reducing administrative burden would eventually decrease burnout, and improve work-life balance.

Generative AI can contribute to administrative tasks and aid in reducing burnout rates among clinicians. Here’s how new advances in generative AI can help: 

  1. Epic EHR is testing new features that will speed up message response time and enable physicians to spend more time with patients. The company is also working on a tool to summarize patient information, so instead of searching for patient’s records, the tool offers a gist of data to the physicians. 
  2. Oracle Health is testing a generative AI-based chatbot feature that will help healthcare providers automate administrative tasks. The company will build the tool on Oracle Digital Assistant, a platform for conversational interfaces/chatbots.

2. Image Analysis in Radiology

The shortage of radiologists and burnout emphasize the need for practical solutions to relieve the burden on healthcare staff. As per the 2023 American Society of Radiologic Technologists Staffing and Workplace Survey, vacancy tops at 18 percent and is the highest in the past 20 years. Solutions such as generative AI are being tested and tried to enhance the radiology field. 

Generative AI is known to create images, but can it also contribute to image analysis in radiology? As we speak, significant steps are being taken to inculcate generative AI in image analysis. 

Let’s look at real-world examples:

  1. Researchers at Northwestern Medicine have created a generative AI tool that is capable of interpreting chest radiographs. The JAMA Network stated that over 500 chest radiograph samples were used for the study, and generative AI may facilitate timely interpretation of chest radiography during emergency healthcare conditions.
  2. Nuance Communications, Inc., a Microsoft Company is set to empower radiologists through its Nuance PowerScribe platform (powered by generative AI) that allows radiologists to create accurate reports in less time.
  3. A study published in the Medical Image Analysis reported using the deep generative model to identify lung nodules in CT images. The generative model achieved higher sensitivity than other methods.

3. Better Clinical Documentation

Physicians spend around 35 percent of their time documenting patient data. They are giving a major chunk of their day to EHR documentation. Thereby leaving very little time for their patients and themselves. 

How can generative AI contribute to clinical documentation to ease their burden and save time?

Here are a few ways:

  1. By automating routine data entry tasks, generative AI reduces the manual burden on healthcare providers, allowing them to focus more on patient care and less on administrative tasks.
  2. Generative AI can automate the summarization of lengthy clinical notes, extracting key information and presenting a concise overview for efficient clinical documentation.
  3. This AI technology can convert spoken words into text, allowing healthcare professionals to dictate notes during patient encounters, improving the speed and accuracy of documentation.
  4. Implementing NLP through generative AI enables the extraction of relevant information from unstructured clinical text, facilitating the creation of structured and standardized documentation.

Real-world examples include:

  1. In September 2023, Oracle announced a generative AI EHR integration that focuses on enhancing clinical documentation. It will be available in the next 12 months.
  2. Amazon is the next tech giant to introduce a clinical documentation service that will enable healthcare providers to create notes using generative AI technology automatically. HealthScribe by AWS leverages speech recognition and generative AI to make transcripts of patient visits, create summaries, and identify key details that can be entered into the EHR.
  3. Baptist Health based in South Florida used generative AI technology to reduce clinical documentation. The benefit of AI integration was access to comprehensive clinical notes after patient visits, cutting down lag time.
  4. Recently, Google announced the launch of MedLM, a part of a generative AI model that is built for medical applications. It offers services such as medical documentation, patient care experience, and drug research.

Wrapping Up

In conclusion, the transformative impact of generative AI in healthcare is evident across various crucial domains, from clinical diagnosis to documentation. This innovative technology holds the promise of enhancing medical practices, fostering precision, and ultimately improving patient outcomes.

As we witness the evolution of healthcare through generative AI, it is imperative to embrace its potential responsibly, ensuring ethical use and patient privacy. The journey towards a more efficient, personalized, and accessible healthcare future is underway. 

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