Navigating Security and Compliance in SaaS Environments
Flower says maintaining control over data flows, third-party services and responsibilities is essential when moving protected health information into any SaaS environment.
“The first step is understanding what data is involved in interactions with a cloud provider and ensuring that PHI is handled only by mature services that provide appropriate security and privacy assurances,” he explains.
It’s important to clarify which security controls the organization must manage and which are managed by the SaaS provider, Flower says.
“Staying informed about changes in SaaS offerings can help optimize usage and maintain compliance,” he says.
Transparency around security and privacy programs, clear communication about the types of data involved and customer responsibilities, and healthcare-specific configuration are key factors in managing these risks.
From Smith’s perspective, strong identity and access management is the starting point. That includes multifactor authentication, role-based access controls, regular audits and a timely and robust offboarding procedure.
“A zero-trust mindset, where nothing is trusted by default, helps reduce exposure from compromised accounts or devices,” he adds.
Future SaaS Trends Shaping Healthcare IT
Smith says one major trend is the convergence of SaaS and AI.
“More platforms are embedding AI for clinical decision support, patient triage, documentation and even scheduling,” he says. “Interoperability is improving too, with growing support for open standards like FHIR.”
Frank Attaie, general manager for public sector, healthcare and life sciences at IBM, says he agrees that AI and SaaS have a great future together.
“AI and machine learning technology help healthcare organizations sift through their data to find valuable and actionable business insights,” Attaie says.
With these AI-embedded SaaS solutions, healthcare organizations can better integrate, automate and secure their complex operations across multiple environments.
“Ultimately, they also help healthcare organizations build cost savings and efficiencies to further invest in growth, patient-oriented applications and new services,” he says.
Despite the promise, Smith cautions that AI in healthcare is far from perfect.
“Algorithms must be explainable, tested on diverse populations and used alongside, not in place of, clinical judgment,” he says.
As more decisions move into AI-enabled SaaS environments, accountability becomes murkier.
“Healthcare organizations need clear governance for how AI is deployed, reviewed and corrected when it fails,” Smith says.
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