Three steps healthcare organizations can take to use generative AI - Digitaldynamo Tech Three steps healthcare organizations can take to use generative AI - Digitaldynamo Tech
Three steps healthcare organizations can take to use generative AI responsibly

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A healthcare leader at research and advisory giant Accenture defines how to process proprietary data, create the right controls and align people with technology.

Many healthcare organizations are qualifying generative AI fast and furious. Geneative is a type of artificial intelligence behind the popular ChatGPT application.

Although it may seem like a miracle technology to many, it is by no means perfect. In fact, three steps health organization can take to use Ai responsibly

But generative AI can be used in healthcare today, and it just needs to be used responsibly.

Rich Berhanzel advises healthcare executives at health giant Accenture and knows a lot about artificial intelligence. We interviewed him to get his thoughts on the responsible use of generative AI, which he says involves three things: preparing your proprietary data, setting the right controls, and aligning people with the technology.

Q: You are advising your clients on three key things as they begin to consider implementing generative AI in a responsible way. First, prepare your property data. Please clarify this.

a.

Huge amounts of data can be processed by large language models that use generative AI, giving them the ability to “know” everything that an organization has ever known. You may utilize language to drive innovation, improvement, and reinvention to new heights with applications, systems, papers, emails, chats, video, and audio.

Nowadays, the majority of companies have begun experimenting with using “off-the-shelf” baseline models. Organizations that can satisfy their own demands by enhancing or customizing models with their own data will reap the most benefits.

However, access to domain-specific organizational data, semantics, expertise, and methodology will be necessary for changing the core models. Although the quality of underlying data has always been crucial for effective machine learning and AI deployments, the vast array of data that

Question: Your second tip is to set the right controls. What do you mean by this?

a. The rapid adoption of generative AI brings new urgency to the need for healthcare organizations to define, develop, and articulate the mission and principles of responsible AI. At the same time, it must create a transparent governance structure that builds trust in AI technologies.

It is essential to include responsible AI approaches throughout, starting with controls to assess potential risks of generative AI in the design phase.

Accountable AI principles should be led from the top and translated into an effective governance structure for risk management and compliance, whether with regulatory principles and policies or applicable laws and regulations.

This includes enhancing compliance with current laws and regulations while keeping an eye on the future, establishing policies to mitigate risks, and operationalizing those policies through a risk management framework with regular reporting and monitoring.

To be accountable by design, organizations need to move from a reactive compliance strategy to the proactive development of mature responsible AI capabilities, including principles and governance; risk, policy and control; technology and enablers; culture and training

Question: And your third piece of advice is to get people attuned to technology. How is that? And why is this important for generative AI?

a. Generative AI applications in healthcare will rely on people to direct them based on human experience, perception, and expertise. Operations will need to be improved to include generative AI capabilities and elevate the role of the human factor.

Healthcare organizations need training programs to help workers — from physicians to administrative staff keep pace with advances in artificial intelligence, which requires more complex, judgment-based tasks. For example, clinicians interpreting health data will need to have more technical knowledge about how AI models work so they can have confidence in using them as a “co-pilot”.

In areas of healthcare where generative AI is showing very promising results, organizations must begin by decomposing existing jobs into core sets of tasks. Then evaluate how generative AI affects each task, whether it is fully automated, augmented, or unaffected.

For example, we are already seeing how generative AI can reduce the burden of healthcare documentation on human workers. Fundamentally rethinking how work is done and helping people .

Q: Where do you see generative AI in healthcare in five years?

a. This is a pivotal moment. For several years, AI and basic models have been quietly revolutionizing the way we think about machine intelligence. We are at the beginning of a very exciting era that will fundamentally transform the way we access information, how we meet the needs of physicians and patients, and how healthcare organizations are run.

Accenture research shows that nearly all healthcare executives (98%) agree that advances in generative AI herald a new era of enterprise intelligence. They are right to be optimistic about the ability of generative AI to fundamentally change how health care is delivered.

Follow Bill’s HIT coverage on LinkedIn: Bill Siwicki
Email him: bsiwicki@himss.org
Healthcare IT News is a publication of HIMSS Media.

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