4 Keys to Executing Generative AI

4 Keys to Executing Generative AI. A biomechanical hand lifts up a brain surrounded by a cloud of letters of the alphabet.

Generative artificial intelligence (AI) has captured the attention of health care providers and payers focused on reimagining care delivery. That’s not too surprising given the flurry of recent application launches from big tech companies like Microsoft and Google as well as startups touting the significant time savings the technology can generate for clinicians.

Kicking the Tires of Generative AI

A recent Accenture survey report and analysis explores the potential of generative AI across various fields. The significance of the report to health care was underscored by Rich Birhanzel, Accenture’s global health industry lead, in a recent interview. The research found that:

  • 98% of health care providers and 89% of payer executives believe that advancements in generative AI are ushering in a new era of enterprise intelligence.
  • 40% of all working hours in health care could be supported or augmented by language-based AI.
  • Half of all health care organizations plan to use ChatGPT, an AI chatbot, for learning purposes, and more than half are planning pilot cases this year.

Generative AI uses large language models to generate responses to natural language questions. It can perform tasks like text classification, translation, summarization and question answering.

Yet even with all its potential benefits for health care, the pharmaceutical industry and other fields, questions remain over the technology’s accuracy and what resources provider organizations will need to properly oversee and manage generative AI applications.

Applying the Technology

The University of Kansas Health System is rolling out generative AI throughout its Kansas City-area medical centers. In one of the earliest large-scale uses of generative AI, the system is making Abridge AI Inc.’s application available to its 1,500 physicians and other clinicians.

Abridge, founded in 2018, developed its platform to create summaries of medical conversations from recorded audio during patient visits. This reduces the time physicians spend on notes by as much as two hours per day, Gregory Ator, M.D., the health system’s chief medical information officer, recently told The Wall Street Journal.

At UPMC, a minority investor in Abridge, a small cohort of clinicians is using technology from Abridge to automatically document interactions with patients. Abridge “listens” to conversations between a patient and their health care provider and extracts the important points, like a change in medication or behavior, and creates notes for the patient and the electronic record (EHR). So far, both patients’ and clinicians’ reactions have been positive.

UNC Health, meanwhile, is another early adopter. It has agreed to take part in a much smaller generative AI pilot with EHR giant Epic. The initial rollout will begin with five to 10 physicians at UNC Health using the technology to auto-draft responses to common patient questions that are time-consuming to answer. UC San Diego Health, UW Health and Stanford Health Care also are 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 roll out, Accenture suggests that providers take the following steps to evaluate, monitor and implement the technology.

4 Ways to Capitalize On Generative AI Capabilities

1 | Take a people-first approach.

Focus on people as much as technology. Ramp up talent investments to address both creating and using AI. Develop technical competencies like AI engineering and enterprise architecture and train people across the organization to work effectively with AI-infused processes.

2 | Get your proprietary data ready.

Foundation models require vast amounts of curated data to learn. This makes solving the data challenge an urgent priority. Take a strategic, disciplined approach to acquiring, refining, safeguarding and deploying data. Ensure that the organization has a modern enterprise data platform built in the cloud with a trusted, reusable set of data products.

3 | Invest in a sustainable tech foundation.

Consider requirements for infrastructure, architecture, operating model and governance structure to leverage generative AI and foundation models — keeping a close eye on cost and sustainable energy consumption.

4 | Deliver responsible AI.

Urgently assess whether a company’s responsible AI governance regime is sufficiently robust before scaling generative AI applications. Build controls for assessing risks at the design stage and embed responsible AI principles and approaches throughout your organization.

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