From Ethics to Trust: Strategic Guardrails for Safe, Secure, Effective AI in Healthcare
Ethical AI and trustworthy AI frameworks should be table stakes for healthcare and life sciences organizations seeking to innovate with AI.
Ethical AI and trustworthy AI frameworks should be table stakes for healthcare and life sciences organizations seeking to innovate with AI.
Here’s what healthcare entities should know about the maturing landscape, what to actually be concerned about, and the next stage of healthcare data evolution as we know it.
By adopting interpretation-driven, clinically intelligent technologies, revenue cycle teams can ensure that every nuance of care is accurately represented. This safeguards revenue integrity while maintaining the highest standards of compliance.
Will doctors or patients who are burned by one AI solution trust the next one they’re given? Probably not. That’s why every provider rolling out AI tools has to understand this risk and build governance into its development process.
The challenge is determining whether wearable data is reliable enough to relieve the review burden, guide care, support reimbursement, or reassure a patient who is worried about their heart rhythm at two o’clock in the morning.
The core challenge for leadership is no longer merely managing direct vendors; it’s the cascading risk of the "nth-party" supplier.
We are taking a look at how health insurers are using AI, defining success, and managing cybersecurity risks. Give us your opinions by completing our brief, anonymous survey.
Those who recognize that AI governance is an innovation enabler will be able to align business priorities and AI investments — and make smarter decisions about which models to accelerate, and which to retire — ultimately leaving the enterprises better positioned to achieve ROI on their AI.
According to a recent panel of employer leaders, it is important for health vendors to be honest with employers and share their incentives.
Trust in the U.S. healthcare system is eroding, but experts say rebuilding it is possible. They say this will require both payers and providers to prioritize empathy, transparency and personalized communication.
For all the talk about this problem, the industry has struggled to make big headway. But there are solutions — here are a few steps that make a substantial difference.
How do you know a vendor handles data properly? Fortunately, independently-audited certifications can ease your concerns about a developer’s trustworthiness.
The more the use of AI in healthcare and healthcare research becomes mainstream, the more the risks associated with AI-powered analysis evolve — and the greater the potential for breakdowns in consumer trust.
The public health community can circle the wagons and complain about the attacking forces outside. Alternatively, public health advocates can take a hard look in the mirror, get outside of their bubble, frankly identify failures and missteps, and determine how to regain the trust of an increasingly skeptical public.
Access to care isn't enough. Healthcare organizations need to build trust in order to reach underserved communities, experts said on a recent panel.
Building trust while simultaneously building products, selling, recruiting, and fundraising can feel impossible. But it's required whether you have the time or not, and it doesn’t stop no matter how big you grow.