Viewing cybersecurity as a component of healthcare’s critical infrastructure is essential for improving patient care, where there's no room for error.
Many organizations meet the HIPAA requirements by manually sifting through audit logs to identify inappropriate access. Considering that millions of EHR accesses occur daily at a hospital, it’s inevitable that incidents will fall through the cracks with manual monitoring practices. In addition, as usage expands across more applications and digital services, many are realizing that manual processes can't scale in reach, timeliness, consistency or accuracy.
Those doing manual audits of EHR access often review and investigate a random sample of PHI accesses. These manual audits happen weekly or monthly, meaning that IT teams may only look at a handful of accesses out of the thousands that occur daily across the network. This isn't a comprehensive audit. It doesn’t give insight into user access trends over time.
As healthcare’s network expands beyond the traditional perimeter and HDOs take an identity-centered approach to cybersecurity, IT teams should consider the role of advanced ML technologies in monitoring PHI access. By integrating with existing IAM solutions, HDOs can adopt a more comprehensive strategy for monitoring and protecting patient privacy.
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