Healthcare’s Guide to Generative AI
Healthcare’s Guide to Generative AI
Your tool is only as good as the data it’s trained on — better data means a better output, which means better results.
Look for AI platforms that offer algorithmic and data transparency — you need to know if the data set the tool was trained on is appropriate for your organization. This is particularly important if you are using a third-party platform or off-the-shelf solution. For example, if you’re using a tool trained on urban population data, it may not be appropriate to use in a rural hospital setting. You’ll likely need to customize off-the-shelf solutions if they don’t fully address your organization’s needs.
If you’re using an in-house platform, you should create a system that makes it easy for people to input data and verify that it is correct. Your organization should also regularly validate the data to make sure it is accurate and being used correctly.
Looking for help deploying AI responsibly? BDO’s Responsible AI service can help you implement policies and deploy specific tools for Responsible AI.
As understanding of the impact of social determinants of health (SDoH) grows, healthcare organizations are increasingly focused on increasing health equity in their communities.
While generative AI offers opportunities to improve health equity — by freeing up doctors to see more patients and analyzing patterns to identify existing healthcare disparities within a population, for example — it can also inadvertently contribute to health inequities. Generative AI is vulnerable to biases gathered from the data it analyzes, which can perpetuate the issues that cause and exacerbate health inequity.
Work closely with your health equity team to understand how generative AI can help their work and what steps to take to mitigate the potential risks. Consider also speaking with clinicians, community leaders, and patients to gain a better understanding of the practical use cases for generative AI that will make a real difference in your community.
Want to learn more about health equity? Read our 2021 Health Equity Survey to discover how healthcare organizations across the U.S. are advancing health equity in their communities.
You’re likely feeling the pressure to adopt generative AI. But before you do, you need to have proper oversight and AI governance. You need to be able to move quickly, but carefully, to adopt generative AI in your organization in a way that mitigates risk.
Consider who in your organization you want to oversee your use of generative AI. These entities may include your ethics boards, quality officers, and informatics officers. Their guidance can help you protect your people and organization while still moving forward on your generative AI goals.
It’s crucial to include the right people in the oversight role without making your governance structure too unwieldy. Remember that being nimble but safe is the key to success with generative AI.
BDO can help you select the right governance structure for your AI strategy. Our Adoption and Change Management services help support organizational alignment, user acceptance, and a smooth transition to AI adoption.
It’s important that you aren’t using just any generative AI for your organization. Make sure you select a HIPAA-compliant platform that is appropriate to your organization’s needs.
Keep in mind that, in any cloud platform, data security and compliance is a shared responsibility between the vendor and the user. That means it’s your responsibility to take appropriate risk mitigation measures — the responsibility does not solely fall on the vendor.
Be sure that what goes into the generative AI platform is HIPAA-compliant and encrypted. Feeding unencrypted patient data into the platform can be a huge security risk in the event of a hack or leak.
You should also look for any potential vulnerabilities that could compromise the security of the platform. For example, consider a patient who falls for a phishing scam. If the phishing scam is delivered to the email address they use for their patient portal, could this compromise the generative AI platform connected to the portal?
To assess potential vulnerabilities in your use of AI, reach out to our AI-Enabled Cybersecurity team. Our AI Security Evaluation can help enhance your control posture as AI capabilities are introduced.
It can be difficult to decide where to begin when adopting generative AI. Look for a task or function that is low risk, highly manual, and creates unnecessary process overhead — these types of functions are great candidates for testing generative AI.
It may be tempting to use generative AI for complicated processes from the very beginning, such as AR/AP processes. However, it’s critical to test the tool on low-risk functions first — you don’t want to make a mistake where risk is high and the margin for error is slim. Once you’ve tested generative AI on an appropriate use case and have learned how best to use it, you can start scaling up your usage to tackle more complex processes and functions.
Need help selecting the right use case? Our AI Consulting and Strategy team can help you identify and prioritize AI use cases that align with your business objectives, assess the feasibility and potential impact of each use case, and develop a detailed implementation plan.
There are certain capabilities you’ll need to have in place to make generative AI work for your organization.
Start by assessing what technology you have now, where you’re already using AI, and your organization’s data maturity level. You may need to make investments to upgrade technology or to improve your data management. You also want to make sure you’re making the most of your available tools before adopting and deploying a potentially expensive new platform.
Determine what changes need to be made to adopt generative AI and how much capital and time it will take to make those changes. From there, you can construct a roadmap to help guide you through the process of deploying generative AI. You can also use this roadmap to track your progress as you move forward on your generative AI journey.
Not sure you’re ready for generative AI? Take the first step with our Analytics Adoption Readiness Assessment to determine what data maturity investments you need to make to enable generative AI.