By Rajiv Mahale

AI has revolutionized the healthcare industry in numerous ways. From models that identify patient risk for early interventions to machine learning algorithms that detect patterns humans miss, AI's capabilities often seem limitless. Newer generative AI (GenAI) tools hold immense potential to improve healthcare. GenAI supports clinical decision-making at the point of care and aids in creating personalized treatments. It also works behind the scenes, enhancing administrative efficiencies in data management, billing, payment processing, and workflow automation.

The innovation and potential AI holds has prompted healthcare leaders to significantly increase their budgets for adopting the technology, with a reported 300% rise. As the pressure increases to implement AI, so do the risks – and in an industry like healthcare, the stakes are too high to get it wrong. Organizations that rush into AI adoption may wind up with technology that is untested, inadequate, biased, or even harmful to patients.

Poor or inadequate AI tools can lead to several negative consequences, including restricting access to care, financial repercussions and liability, damage to organizational reputation, and depletion of patient trust. When irresponsible AI use rises to the level of patient harm (as we saw recently with claims that AI technology is adversely affecting Medicare Advantage patients), we may quickly see a shift away from excitement about the potential to reactionary rules and laws that restrict the use and innovation of AI. Ensuring the safety and trustworthiness of healthcare AI solutions is of utmost importance to mitigate the potential fallout of unethical or irresponsible use.

AI is most beneficial when used as a support system for clinical teams, not as a replacement for human capabilities. Payers and providers can only realize AI's positive outcomes and change the trajectory of the healthcare system by embracing responsible AI.

How to ensure ethical and responsible AI in healthcare
How can healthcare organizations integrate AI seamlessly and responsibly while prioritizing patient safety and well-being alongside data security? It requires a focused commitment to ethical use at every step of the AI development and deployment journey. Organizations that use or develop AI should define guiding principles that every team member fully understands and agrees to abide by. Broad themes for these principles should include:

• Balancing AI advancements with patient safety and trust
Always remain focused on what is best for the patient when developing or implementing AI tools. Be transparent in the use of AI tools so software end users understand how they are incorporated, and they can share that information with patients when necessary to build trust.
• Eliminating model bias
Training and improving AI models requires large datasets with diversity in patient populations. The larger the dataset, the more likely developers can mitigate bias. Additionally, more data leads to better insights that providers and payers can glean from analytics tools, and more accuracy in predictive AI models.
• Ensuring accuracy and efficacy
AI developers should work closely with healthcare experts from clinical and administrative backgrounds to validate and improve models over time. This expertise helps developers create models that promote health equity, improve care, and address the most critical financial and clinical performance outcomes.
• Making AI data interpretable and accessible
A key benefit of AI is its ability to review massive volumes of data and provide a prediction or recommended action. However, it can be difficult for healthcare stakeholders – particularly clinicians – to trust what can seem like a “black box” of information. When AI technology includes the ability to drill down into the data, it improves trust in the underlying AI tools, and the resulting outputs.

AI presents the best chance to achieve our collective value-based care goals
When used responsibly, AI can be a powerful tool for implementing value-based care (VBC), which prioritizes care quality over quantity and outcomes over volume. AI's ability to analyze vast amounts of healthcare data and make connections that humans might miss allows for proactive interventions. Clinicians have insights to predict a person’s risk of developing chronic diseases like heart disease and diabetes before it occurs. Combined with care tools, these insights can lead to earlier intervention and more personalized care plans that prevent disease progression, reduce healthcare costs, and ensure appropriate care.

Additionally, AI can uncover broad trends in patient or member populations. This allows healthcare providers to allocate resources efficiently, identify gaps in care such as missed screenings, and develop targeted interventions to improve patient outcomes. AI-powered insights can present this data clearly and concisely so clinicians can quickly determine which patients need care, and move them into appropriate care workflows to address those needs.

Shaping the ethical future of AI
AI will play an essential role in the future of value-based care and the entire healthcare industry, but its development must be accompanied by a strong focus on responsible and ethical use. It's crucial to balance innovation with patient safety and trust. With these guardrails in place, healthcare leaders can create a future where AI is a critical tool for improving our health and our lives.

About the author: Rajiv Mahale is the chief product and business development officer at Cedar Gate Technologies.