⌚ Apple's Health Battle

Good morning! 

Welcome to Healthcare AI News, your weekly dose of the latest developments and headlines in the world of Healthcare AI.

In this issue, we explore:

✅ Headlines: How Biden's AI EO could impact pharma, biotech

✅ Industry: AI could help develop a gonorrhea vaccine

✅ Interesting Reads: How scientists are solving the mystery of aging

✅ Tech: Over-automation is breaking the web

✅ Feature: AI, Air Quality, and Health

HEADLINE ROUNDUP

• AI revolution in diabetes care: How technology is beating this silent killer (Read More)

• How Biden's AI executive order could impact pharma, biotech (Read More)

• VA launches $1 million AI tech competition to reduce healthcare worker burnout (Read More)

• The health battle within Apple (Read More)

• How Medicaid directors are thinking about AI (Read More)

TOGETHER WITH COURSERA

Take your Excel productivity to the next level by learning how to leverage the capabilities of ChatGPT, regardless of whether you are an Excel amateur or seasoned power-user.

Over the span of this course, you will learn how to effectively standardize, extract, classify, and generate data through the combined power of Excel and ChatGPT.

Through hands-on projects and real-world scenarios, you will gain practical experience in creating standardized datasets, writing formulas for data cleaning and classification, extracting text from URLs, conducting sentiment analysis, and generating dummy data for surveys.

This course offers a unique opportunity to explore the potential of combining AI capabilities with common business challenges faced within Excel. Basic knowledge of Excel is recommended to make the most of this course.

INDUSTRY NEWS

• Fake Ozempic pens sending people to hospitals (Read More)

• AI could help develop a gonorrhea vaccine (Read More)

• Second person to receive experimental pig heart transplant dies nearly six weeks after procedure (Read More)

• AI-Driven digital twin initiative at Thomas Jefferson hospital (Read More)

• Addressing SDOH using cell phones and AI (Read More)

INTERESTING READS

• How scientists are solving the mystery of aging (Read More)

• Traditional Chinese medicine reduces risk after heart attack (Read More)

TECH NEWS

• Over-automation is breaking the web (Read More)

• 6 software engineering templates I wish I had sooner (Read More)

• Putting everything in its right place with ML-powered file organization (Read More)

HEALTHCARE AI JOBS

• Area Innovation Manager AI - Medtronic

• Sr. Data Engineer - AI/ML - Stanford Health Care

• Full Stack Engineer / AI Lead at voice - Knowtex

THE FEATURE

Healthcare Boards and Gen AI

4 questions healthcare corporate boards must face about generative AI

“Please regulate us”

This is the plea coming from all sides of the generative AI industry at the moment. Innovators are asking U.S. regulators to step it up and institute formal guardrails in this space.

But for now, it’s largely up to companies themselves to self-regulate. 

So, who makes judgment calls on the use of generative AI in healthcare? 

A lot of that responsibility falls on executive boards.

And how do these boards make these decisions? Ask McKinsey.

The firm recently published a 4-topic guide for corporate boards overseeing their companies’ use of generative AI. We loved the questions they posed, but we’d like to make them specific to healthcare.

Take a look at what we came up with—and weigh in with your thoughts!

4 topics healthcare executive boards must consider about generative AI

1. Balancing value-creation with risk management in healthcare

With generative AI helping manage triage and patient follow-up, doctors can focus more on doctoring.

But before rolling out these solutions across health systems, boards need to balance these benefits with the many inherent risks:

• Generative AI’s hallucinations resulting in inaccurate or even inappropriate patient communication

• The perpetuation of bias from training data

• Chatbots revealing  legally protected (e.g., copyrighted, patented) information—or even PHI

And this all brings us to reputation management. 

If you’re looking for a worst-case scenario here, look no further than our feature on crisis hotlines. The story of the backlash against one helpline’s chatbot hallucinations is sure to make you cringe. 

2. The resources healthcare companies need to deploy gen AI

Generative AI can only work for a healthcare company with the right tech stack. And the data architecture and security standards to boot.

After all, generative AI is more useful in healthcare if it’s trained on a healthcare organization’s own data—going beyond a standard foundation model. 

Plus, deploying generative AI is not a one-and-done deal, especially in healthcare. Companies need a plan for acquiring and training the appropriate talent to maintain, work with, and communicate about generative AI.

3. Amending organizational structure to accommodate gen AI

Speaking of talent...

If you’ve read our Chief AI Office (CAIO) feature, you know what we’re about to say.

Healthcare companies especially would be well-served by appointing AI specialists to their executive suite. The high level of risk management with generative AI solutions requires an experienced AI leader’s help calling the shots. 

4. The short- and long-term impacts of gen AI in healthcare

When weighing all these questions, corporate boards can’t afford to be short-sighted. 

Yes, companies must consider some of these short-term impacts of generative AI on healthcare:

• The expectation for more personalization and faster response time to patient queries

• The need to educate providers on the effective use and oversight of these tools

• Development of patient communication about generative AI implementation

But they must also keep in mind more long-term, robust workforce impacts, such as:

• Applications of patient-facing generative AI beyond triage chatbots

Preparing the business for future workforce unpredictability (e.g., increases in productivity, the reallocation of staff resources to AI oversight)

How are actual healthcare companies evaluating their use of generative AI?

So, how are these self-governance efforts looking in the real world of healthcare generative AI?

Some hospitals, like NYU Langone, are taking the management and improvement of generative AI tools into their own hands. 

At the same time, many hospitals are waffling on how—and whether—to talk to patients about their use of generative AI at all, waiting for others to take the lead. 

And forget having a comprehensive plan: In a hospital executive survey conducted by Bain, only six percent of respondents reported having a generative AI strategy ready.

Final thoughts from HAN

If you ask us, those survey results should be a reality check. Both for proponents of healthcare AI and for the broader healthcare industry.

Companies that aren’t prepping to handle generative AI are already late. The time to start working on a plan was yesterday.

Granted, there’s only so much healthcare companies can do to self-regulate. Especially as stiff competition pressures more companies to jump into generative AI as soon as they can, guns blazing. 

We must still call for formal industry-wide regulation.

On this front, we’ll be watching proposals from academic institutions and official bodies like the WHO. 

But first: Take a moment to imagine you’re on a corporate board (if you’re not already). Before you get too excited about your newfound power, take a moment to think: 

How would you go about evaluating your healthcare company’s use of generative AI? Is there a question we missed in this guide? Let us know!

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