Healthcare organizations have spent the past decade digitizing workflows, expanding access to data, and introducing automation to reduce administrative burden. However, for many clinicians and operational teams, the complexity of day-to-day tasks remains. Regulatory requirements continue to evolve, reporting frameworks are becoming more detailed, and there is ongoing pressure to do more with fewer resources.
This complexity is represented in programs like Merit-based Incentive Payment System (MIPS), where Medicare reimbursements are tied directly to clinician performance. While the goal is to incentivize higher-quality, more efficient care, the operational demands require practices to keep up with changing measures, shifting thresholds, and detailed documentation requirements, all while ensuring accuracy in submission.
Automation has helped, but in many cases, it has only addressed parts of the problem. Teams still need to interpret requirements, identify gaps, and decide what to do next. That work has not gone away. What is changing is how that work gets done.
Moving Beyond Task-Based Automation
AI agents represent a shift away from tools that simply follow instructions toward systems that help interpret information and guide next steps. Rather than just processing data, they string together inputs from multiple sources, track changes, and support faster, more informed decision-making.
For programs like MIPS, where requirements are frequently updated; quality measures evolve, thresholds change, and documentation expectations shift, keeping up requires more than access to information. It requires the ability to apply it quickly and consistently.
By bringing data, regulatory context, and workflow priorities into one place, these tools can help teams stay aligned. Instead of moving between systems or relying on retrospective audits, clinicians and administrators can identify issues earlier and address them before they escalate.
Reducing Risk Through Alignment
One of the more persistent challenges in regulatory reporting is keeping documentation, submission, and requirements aligned. Small gaps can have outsized consequences affecting performance scores, reimbursement, and compliance.
With more continuous visibility into data and requirements, organizations can catch inconsistencies earlier. This allows teams to address issues ahead of submission, rather than spending time correcting them later.
This shift is not only about efficiency, but also about reducing uncertainty. As financial and regulatory pressures increase, the ability to anticipate and manage risk becomes just as important as improving productivity.
Integrating Insight into Daily Workflows
Much of the value of AI in healthcare is showing up in small, practical improvements to everyday workflows. Embedding insights into the tools clinicians and staff already use makes it easier to act on information in real time, rather than relying on separate analyses.
This reflects a broader trend across healthcare. Progress is happening where technology fits naturally into existing processes and reduces friction. Ease-of-use is just as important as technical capability when it comes to long-term adoption.
Taking a Practical Approach
At the same time, implementation needs to remain realistic. Not every workflow requires advanced automation, and not all data is ready to support it. Data quality, interoperability, and governance continue to shape what is possible in real-world settings.
As with real-world data in clinical research, success depends on applying these tools in the right context and for clearly defined needs. A focused, practical approach is more likely to deliver meaningful results.
The emergence of AI agents does not represent a wholesale replacement of existing systems, but rather an evolution in how those systems support users. By combining data integration, contextual awareness, and real-time guidance, these tools have the potential to reduce administrative burden while improving accuracy and performance.
For clinicians, this could mean less time navigating reporting requirements and more time focused on patient care. For organizations, it may translate into more consistent performance, reduce compliance risk, and greater operational resilience.
Healthcare is operating in a more constrained and complex environment than it was even a few years ago. In that context, the ability to move from fragmented automation to more adaptive systems is becoming less of an advantage and more of a requirement. Programs like MIPS are one example of where that shift is already underway.
About Elizabeth Acord
Elizabeth Acord is responsible for managing the medical societies’ clinical data registries at Verana Health. Prior to Verana Health, she served as COO of PYA Analytics, a technology startup, where she led operations. PYA Analytics was later acquired by Verana Health. Elizabeth built and leads a team of 20 practice experience managers who support clinicians and practices in preparing for their annual MIPS submissions. She recently launched Verana’s MIPS Advisory Services, a paid service offering designed to provide comprehensive, end-to-end support for practices navigating MIPS requirements. In developing this program, she transitioned key team members into MIPS Advisors.
Elizabeth is passionate about empowering healthcare practices to improve performance, achieve compliance, and maximize their MIPS outcomes through expert guidance and data-driven insights.
