Generative artificial intelligence (AI) is capturing the attention of healthcare providers and payers who are focused on reimagining healthcare delivery. This is not all that surprising given the spate of recent application announcements from major technology companies such as Microsoft and Google, as well as start-ups touting the significant time savings this technology will bring to clinicians. is not.
Kick the Tires of Generated AI
A recent Accenture research report and analysis explores the potential of generative AI across a variety of areas. The importance of the report to the healthcare sector was emphasized in a recent interview by Accenture’s global healthcare industry head, Rich Billhansel. As a result of our research, we found the following:
- 98% with healthcare providers 89% of payer executives believe advances in generative AI are ushering in a new era of enterprise intelligence.
- 40% Of all working hours in healthcare, it could be supported or enhanced by language-based AI.
- Half of all healthcare organizations plan to use chat GPT, AI chatbots for learning purposes, more than half of which are planning pilot cases this year.
Generative AI uses large language models to generate responses to natural language questions. You can perform tasks such as text classification, translation, summarization, and question answering.
But despite the potential benefits for healthcare, the pharmaceutical industry, and other sectors, the accuracy of the technology and what resources provider organizations require to properly oversee and manage generative AI applications. Doubts remain as to whether
Application of technology
The University of Kansas Health System is deploying generative AI across its medical centers in the Kansas City area. As one of the first large-scale uses of generative AI, the system is making Abridge AI Inc.’s applications available to his 1,500 physicians and other clinicians.
Founded in 2018, Abridge has developed a platform that creates medical conversation summaries from audio recorded during patient visits. This cuts doctors from spending as much as two hours a day taking notes, Gregory Atholl, M.D., the health system’s chief medical information officer, recently told The Wall Street Journal.
At UPMC, a minority investor in Abridge, a handful of clinicians are using Abbridge technology to automatically document patient interactions. Abridge “listens” to patient-provider conversations, extracts key takeaways such as medication and behavioral changes, and creates notes for patient and electronic records (EHR). So far, both patient and clinician response has been positive.
Meanwhile, UNC Health is also an early adopter. The company has agreed to join a much smaller generative AI pilot with EHR giant Epic. The initial rollout will begin with his 5-10 physicians at UNC Health using the technology to automatically generate answers to common patient questions that take time to answer. UC San Diego Health, UW Health and Stanford Health Care are also participating in the pilot.
Epic is working with Microsoft to integrate large language model tools and AI into its EHR software.
As these and other early uses of generative AI unfold, Accenture suggests that providers take the following steps to evaluate, monitor, and implement the technology.
4 Ways to Leverage AI Generation Capabilities
1 | Take a people-first approach.
Focus on people, not just technology. Increase investment in people to address both the creation and use of AI. Develop technical competencies such as AI engineering and enterprise architecture, and train people across your organization to effectively operate AI-infused processes.
2 | Prepare your own data.
Training the underlying model requires a huge amount of carefully selected data. This makes solving the data challenge an urgent priority. Take a strategic and disciplined approach to data acquisition, refinement, protection and deployment. Ensure your organization has a modern enterprise data platform built on the cloud with a trusted and reusable set of data products.
3 | Invest in a sustainable technology foundation.
Consider the infrastructure, architecture, operational model, and governance structure requirements for leveraging generative AI and underlying models while closely monitoring costs and sustainable energy consumption.
Four | Deliver responsible AI.
Urgently evaluate whether your company’s responsible AI governance regime is robust enough before scaling generative AI applications. Build controls to assess risk during the design phase and embed responsible AI principles and approaches throughout your organization.
