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 Welcome back to Today in Health It where we delve into the latest advancements in healthcare information technology.

I'm Kate Campbell, joined by my co-host, Sarah Richardson.

Sarah, welcome to the show. Thank you, Kate, and happy Friday. Happy Friday to you. So we're gonna explore an intriguing article titled Google Health Unveils AI Innovations to Transform Global Health Outcomes.

I am excited to discuss the developments and their potential to transform healthcare delivery and healthcare operations.

So let's start by providing some context. At the checkup, Google shared six key AI driven health initiatives aimed at improving health outcomes globally.

Exactly. These initiatives are designed to enhance the accessibility and quality of health information, streamlined data management, and support healthcare professionals through advanced AI applications.

So let's dive right into these six key highlights. The first is enhanced health information. In Search. Google is leveraging AI to provide more accurate and comprehensive health information in search results. So this includes expanding AI overviews to cover thousands more health topics and introducing features like what people suggest,

which organizes diverse perspectives from online discussions. So if you're a CIO or CMIO, this underscores the importance of ensuring that the organization's online health info is accurate and optimized for search engines. It also highlights the growing role of AI in patient education and engagement,

the introduction of medical records, APIs in Health Connect allows apps to read and write medical record information in standard fire format. This aims to consolidate health data access across different platforms, making it more accessible to users. The implication here, this is your C-T-O-C-I-O hat, is considering how your systems integrate with your APIs to provide patients with a unified view of their health data, which enhances patient engagement and also creates a better adherence to treatment plans.

So then next we have advancements in AI for radiology. Google introduced Med LM for chest X-ray, a model that assists in classifying chest x-rays for various use cases, aiming to transform radiology workflows. So for the CMIOs and radiology departments that can explore integrating such AI tools to improve diagnostic accuracy and efficiency, potentially reducing workload and enhancing outcomes.

Then the use of generative AI and clinical documentation. Google's Med Palm two. A large language model fine tuned for healthcare is being utilized to streamline clinical documentation processes such as nurse handoffs and medical record summarization. If you are the clinical leader in the organization, assess how generative AI can be incorporated to reduce administrative burdens, allowing the clinicians to focus on more patient care.

Please include your CIO and your CISO in the conversation to make sure you have all your bases

Then the next one, this is what I think a lot of people are excited about, is AI and early disease detection. So Google is collaborating with organizations to implement AI powered screenings for diseases like tuberculosis, aiming for early detection and treatment.

so consider partnerships that leverage AI for preventative care, which can lead to better population health management and reduced healthcare costs.

And then finally the AI driven health information accessibility. So Google's using AI to make health information more accessible and more personalized, which includes features that allow users to search for skin condition, using images, and providing high quality health content on platforms like YouTube.

Putting my CISO hat on here. Ensure that the organizations that adopt these AI tools and using them for patient data privacy and security, that those spaces are maintained adhering to regulations like HIPAA and

Yeah. It's important to keep that hat on because these are such important considerations.

So these initiatives highlight the rapid integration of AI into healthcare. So Sarah, what are some considerations for healthcare leaders? To keep in mind when adopting these technologies,

I love this question because first and foremost is interoperability. Ensuring that new AI tools can seamlessly integrate within your EHR systems and beyond is crucial for data consistency and for data accuracy.

Absolutely, and with the integration of AI, data security becomes even more critical. As we alluded to. CISOs need to address the security protocols of these AI solutions to prevent potential breaches and ensure compliance with healthcare regulations.

Another consideration, and we talk about this often as well, is the ethical use of ai.

It's essential to have transparency in how models make decisions, especially in clinical settings that maintain trust among healthcare professionals and patients. Put those essentially AI auditing frameworks in place in your organization so that you know how decisions are being made with that

Yeah, that's a good point. And then additionally, training and change management are necessary to ensure that staff are comfortable and proficient in using these new AI tools. I.

And from your strategy standpoint, aligning AI initiatives with the organization's overall goals and care objectives for patients is gonna facilitate smoother adoption and demonstrate the value to stakeholders.

And we've talked about that. How do you measure the value of ai?

Let's consider also the broader industry implications. Adoption of AI in healthcare can lead to more personalized patient care, improved operational efficiency, and potentially lower costs. But it does raise questions about data ownership, patient consent, and the potential for algorithmic bias.

And

healthcare organizations have to establish clear policies on data governance and work towards mitigating biases in their AI models to ensure equitable care for all patient

So for our listeners, especially those in leadership roles, it's important to stay informed about these advancements.

Engaging in industry forums, collaborating with technology partners and investing in continuous education will be key to navigating this involving landscape. And we have a community right here and we encourage you to reach out to each other, attend our summits, but we're always talking about these types of topics.

We have 40 city tour dinners this year, and you can travel to another city to go to a dinner if it's not local for you. Actually had someone reach out to me last night and say, Hey, can I go to your New Orleans dinner? I was like, of course you can. And they live in New Jersey. Go figure. So as these technologies developed, being proactive in pilot testing and providing feedback is gonna help shape solutions that truly meet the needs of healthcare providers and

So now that we've unpacked these six AI initiatives from Google, I think a lot of our listeners are wondering how do we take this back to our organizations and what conversations should you be having with your teams and peers?

That brings us to something really tangible, which is the pilot programs.

So if you're the CMIO and you're interested in testing generative AI for discharge summaries, you don't need to roll out the system wide on day one. You could do a 90 day pilot in one department, choose cardiology as an example, measure the impact on provider time burnout and accuracy, and then scale from there.

And if that's a space, so you're not as comfortable. We already have seen that our friends at Stanford Health are doing a really good job in this

And those pilot results can become your board level narrative. And this is exactly what CFOs and CEOs wanna see. Quantifiable outcomes tied to strategic goals like reducing readmissions, improving HCAP scores, or optimizing operational costs.

I

wanna go a little bit deeper

though on interoperability like Health Connect and Fire API updates because they're really powerful. This is where the CTO and the CISO rules become central to the conversation because suddenly patient data is moving between apps, devices, and systems that were not necessarily built to speak the same language or protect the information in the same way.

Exactly, and the upside is huge. Imagine a patient seeing all their labs, vitals, imaging, and visit summaries in one place from multiple providers, but the risk is real too. If you're a ciso, your focus should be on data governance, consent management, and third party risk assessments.

We have to mention equity and bias because when you're training AI and massive data sets, even from diverse sources, you're still gonna run the risk of reinforcing existing biases.

For example, if AI skin condition analysis performs better on lighter skin tones, that's a problem.

Yeah, that's a big issue, and it's one that's increasingly being flagged by clinical leaders and researchers. So healthcare organizations should be demanding transparency in model training data and asking vendors.

Who is represented, what geographies and what demographics

and the regulatory requirement is catching up Slowly but surely in the us the FDA is continuing to refine its approach to AI and ML based software as medical devices. So for compliance teams, this is not just about IT or clinical discussions, it's legal, its ethical, and it's strategic.

And here's a good way to think about it. Start with three buckets, clinical need. Technical feasibility and governance requirements, and for each AI use case, ask your teams, is this solving a real clinical or operational pain point? Can we integrate it securely into our infrastructure and do we have the policy training and oversight in place to deploy it responsibly?.

Exactly. Kate, these are not just tech questions. They're business, clinical, and ethical conversations that leaders need to be having

today. Just as a final thought, there's a real opportunity here for health systems to lead.

If you're a regional hospital or an dn, you have the chance to help shape what responsible human-centered AI and healthcare looks like, but it takes leadership, intentionality, and collaboration.

I couldn't agree with you more, and for our listeners, this is the time to lean in, not just with curiosity, but also with purpose.

So that's it for today's episode. Join us next week as we continue to cover the most pressing issues for healthcare IT leaders. Sarah, thank you as always for joining me. Happy Friday and thank you. Don't forget to 📍 share this podcast with a friend or colleague. Use it as a foundation for daily or weekly discussions on topics that are relevant to you in the industry.

They can subscribe wherever you listen to podcasts. Thank you for listening, and that's a wrap.