Quality keeps evolving; it's time to rethink how we do the work. From performance intelligence to AI-powered automation, see how Medisolv is helping healthcare quality teams work differently.
Healthcare quality teams are being asked to manage more data, support more reporting programs, and take on greater responsibility for organizational performance. Yet many of the processes supporting this work haven't changed nearly as much as the expectations placed on the people doing it.
Teams still spend significant time gathering data, abstracting charts, validating results, tracking down missing information, and preparing submissions. All of that work matters. But when so much time goes toward getting the data ready, it leaves less capacity to understand what the data is telling us and use it to improve care.
That's a challenge worth reconsidering.
What would your quality team be able to accomplish if less of its time went toward preparing data and more went toward using it?
At Medisolv, we've been developing new solutions around that question. Our work continues to support accurate, reliable quality reporting, while expanding into performance intelligence, financial accountability, and AI-powered automation.
Here's what that looks like in practice.
Most quality leaders have access to plenty of performance data. The difficulty is often getting a consistent view of that information across hospitals, practices, measures, and reporting programs.
When information lives in separate systems or reports, teams spend time bringing it together before they can evaluate performance. By the time a trend becomes clear, opportunities to address it may already be limited.
This creates a practical question for quality leaders: Are your reporting processes helping you understand performance while you can still influence it?
Medisolv's QualityIQ brings quality performance across hospitals, practices, measures, and programs into a near real-time view.
Teams can monitor changes, compare performance against benchmarks, track organizational goals, and investigate results down to individual case detail.
The value is in giving quality professionals a clearer view of where attention is needed, without requiring them to piece together information from multiple reports.
The same approach applies to Hospital Star Ratings. Medisolv's Hospital Star Ratings Analyzer helps hospitals predict future ratings, identify measures with the greatest opportunity for improvement, and model how changes in performance could affect their overall rating.
Instead of waiting for a published rating to understand the outcome, teams can evaluate potential performance changes earlier and prioritize their improvement efforts.
Quality leaders are also being asked to understand how clinical performance relates to financial outcomes.
CMS's Transforming Episode Accountability Model (TEAM) is one example. Under TEAM, episode spending and quality performance contribute to financial reconciliation. Participating hospitals need to understand their performance throughout the model year, particularly when spending or quality results may affect their financial exposure.
Traditional quality reporting processes were not necessarily designed to provide that kind of ongoing financial visibility.
Medisolv developed ModelIQ for TEAM to help hospitals understand that relationship.
Using CMS data, ModelIQ provides a monthly view of episode spending, quality performance, financial exposure, and projected reconciliation. Teams can identify areas of risk, investigate what's contributing to performance, and evaluate where intervention may be needed.
For participating hospitals, the practical benefit is having more information available before final reconciliation, when there may still be opportunities to address performance.
As quality and financial accountability become more closely connected, the ability to understand both will be increasingly valuable.
There's another part of the quality reporting process that deserves attention: the amount of skilled staff time spent on repetitive tasks.
Consider chart abstraction.
An experienced abstractor knows how to interpret clinical documentation, apply measure specifications, identify discrepancies, and determine whether the record supports a particular response.
Those skills are essential to producing reliable quality data.
But much of the abstraction process also involves locating information, transferring it into required fields, and documenting supporting evidence.
That raises an important distinction. The expertise needed to validate clinical information is not the same as the manual effort required to collect and enter it.
And as reporting requirements and case volumes increase, asking skilled professionals to handle every step manually can limit how much work a team can manage.
Medisolv is embedding AI into quality workflows to help reduce repetitive tasks while keeping quality professionals responsible for reviewing and validating the results.
With AI-Assisted Chart Abstraction, AI populates fields and surfaces supporting evidence within Medisolv's certified measure engine. Abstractors can then focus on reviewing the information, validating results, and resolving exceptions.
The goal is to help abstractors start further along in the process, rather than having to complete every step manually.
Medisolv is also introducing AI-Assisted Registry Abstraction, beginning with cardiovascular registries and expanding to additional specialties over time.
The technology extracts and populates registry data for expert review, helping teams manage growing case volumes without relying entirely on additional manual abstraction.
In both workflows, the quality professional remains at the center, and responsible for the final record.
That's an important part of how we think about AI at Medisolv. The technology should support the judgment and experience of the people who understand the data, the measures, and the clinical context.
AI can help prepare the information. Your experts determine whether it's right.
Patient-reported outcome performance measures (PRO-PMs) create a different operational challenge.
Collecting patient responses often requires repeated outreach, follow-up, documentation, and coordination. As patient volumes grow, these activities can take considerable staff time, particularly when teams are managing multiple reporting requirements.
Medisolv's PRO-PM Plus uses AI-powered capabilities to support patient outreach and follow-up, helping teams reduce manual effort and work toward complete, submission-ready PRO-PM data.
For staff, the opportunity is to spend less time managing routine outreach and more time addressing patients who need additional assistance or follow-up.
Across abstraction, registries, and patient outreach, the underlying approach is consistent: identify the repetitive work that technology can support and give quality professionals more capacity for the responsibilities that depend on their expertise.
It's easy to think about automation in terms of how quickly a task can be completed. But for healthcare quality teams, the more useful question may be what that additional capacity makes possible.
A quality professional who spends less time manually entering data may have more time to investigate performance variation.
An abstractor who can focus on reviewing evidence and resolving exceptions may be better positioned to support accurate, timely reporting.
A quality leader with a more current view of organizational performance may be able to identify improvement opportunities sooner.
These are the kinds of changes that can make a meaningful difference in the day-to-day work of quality teams.
Of course, automation still requires oversight. Organizations need to understand how information is collected, how results are validated, and where human review remains necessary. Introducing AI does not eliminate the need for quality expertise. It makes that expertise central to how the technology is used.
As we celebrate Healthcare Quality Week, we're reminded that behind every measure, every report, and every improvement in patient care is a person who cares deeply about getting it right.
Technology is changing how quality teams work. AI can reduce manual tasks, surface meaningful insights, and give teams more time to focus on what matters. But it can't replace the experience, judgment, and dedication of the people who turn that information into action.
That's why we're so excited about what's next at Medisolv. Not because technology can do more on its own, but because of what it can help you accomplish.
To every quality professional working behind the scenes to make healthcare better, thank you. Your work matters, even when it isn't always visible.
AI can help you see what's possible. But YOU are the one who makes it happen.