A focus on quality isn't seasonal anymore: How high-performing organizations are preparing for what's next

For years, quality improvement efforts often followed a familiar rhythm. Organizations would ramp up chart reviews, outreach campaigns and quality reporting activities as HEDIS® deadlines approached, then reset once reporting season ended.

That model is rapidly changing.

As the healthcare industry moves toward digital quality measurement, interoperability standards and total-population reporting, quality can no longer be viewed as a seasonal exercise. Instead, it’s becoming a year-round operational strategy that influences care delivery, member engagement, value-based care performance and organizational outcomes.

At the same time, payer and provider organizations are facing growing pressure to do more with less. That translates into more pressure navigating fragmented data sources and workforce challenges while also balancing increasing expectations for quality performance.

But advanced technologies, particularly AI-enabled analytics and workflow tools, are helping organizations identify opportunities earlier and act more efficiently.

We sat down with Shariff Baseer, Vice President of Quality Optimization at Optum, to discuss the trends reshaping quality measurement, the challenges organizations continue to face and the capabilities that may help define success in the years ahead.

One of the biggest factors driving this shift is the industry's move away from hybrid retrospective reporting toward NCQA’s Interactive Data Submission System (IDSS)-based total population reporting. This requires organizations to measure quality across all eligible members.

The evolution has now shifted more towards a year-round strategy because we're going after everyone within the population rather than relying on hybrid sample-based reviews.

As organizations prepare for digital quality measurement and broader reporting requirements, waiting until the second half of the year to identify and address care gaps is becoming increasingly difficult.

Instead, many organizations are developing year-round strategies that allow them to help continuously monitor performance, engage members and intervene earlier when opportunities are identified.

The result is a more proactive approach to quality: one that aims to emphasize ongoing visibility, earlier interventions and continuous improvement rather than end-of-year recovery efforts.

Organizations that consistently perform well on quality measures tend to view quality as an operational discipline rather than a reporting project.

Rather than treating prospective and retrospective activities as separate efforts, they have an interconnected workflow that pulls together provider engagement, care management and member outreach, informed by advanced analytics. They prioritize quality improvement opportunities early in the year, continuously evaluate performance and use data to focus resources where they may have the greatest impact.

Many are also adopting more coordinated intervention models that bring together multiple channels of engagement (in other words, a multimodal strategy), aiming to connect members with the right outreach at the right time through a channel that’s highly effective for them.

Identifying quality improvement opportunities early in the year and connecting workflows, informed by analytics over the entirety of the year, is the hallmark of a consistently strong-performing organization.

Think about leveraging point of care workflows, provider portals, care management, member outreach campaigns or helping schedule appointments. A multimodal strategy acknowledges that every member's needs, and preferred methods of engagement, are different.

Some may respond well to digital communications or provider outreach delivered through electronic health record workflows. Others may benefit from care management support, home-based services, remote monitoring programs or other personalized engagement strategies.

Rather than relying on a single intervention model, high-performing organizations use data and analytics to determine which approach is most likely to drive action and help improve outcomes. Being able to identify and meet the member where they are is essentially the key.

Organizations that tailor interventions to individual needs are often better positioned to help improve engagement, address barriers to care and support better quality outcomes.

One of the key drivers of this is the NCQA digital quality transition roadmap, where we’re evolving all of our internal technology processes to be more digitally oriented.

As the NCQA, CMS and other stakeholders continue advancing digital quality initiatives, with digital quality reporting becoming mandatory by MY2030 (Measurement Year 2030), organizations will need stronger capabilities to access, exchange and validate clinical data across a growing number of systems. Interoperability, data quality and standardized digital reporting are becoming foundational requirements rather than future-state goals.

The shift will likely place increased pressure on both payers and providers to establish more reliable data-sharing processes and help reduce reliance on manual collection methods.

Organizations that invest now in digital infrastructure, interoperability and data governance may be better prepared to adapt as reporting requirements continue to evolve.

That’s no easy feat for an organization. But Optum is making strides in partnering with the NCQA on their digital data quality pilot. We’re also looking at how we can expand digital data access to our customers, whether they’re payers, providers or health plans. We’re trying to see how Optum can play that pivotal role in helping in data transformation and validation, leveraging the digital CQL engine to generate results early enough for customers to make an impact.

Across CMS programs, including the new LEAD initiative, as well as ACO REACH, the Medicare Shared Savings Program (MSSP) and evolving Medicare Star Ratings, greater emphasis is being placed on clinical outcomes, chronic condition management and patient experience. At the same time, NCQA’s transition to digital quality measures (eCQMs and dQMs) are helping organizations evaluate quality using more complete and timely clinical data.

It’s less about retrospective reporting and claims-based approaches in favor of interoperable clinical data, electronic quality measures and digital quality measures.

Despite significant advances in technology, many organizations continue to operate across fragmented systems and complex workflows.

Clinical data often resides in multiple electronic health records, claims platforms, provider portals and external data sources. Before insights can be generated, that information frequently must be aggregated, standardized, validated and transformed into a usable format.

That’s where Optum is spending quite a bit of time and energy to streamline the process and build a unified data intake through Quality Optimization, part of the Optum Value Connect platform, so we can articulate and identify the data gaps and coordinate a plan to fill those gaps.

We can only be successful if we know what processes need to be interconnected with each other. Every single aspect of what we’re trying to drive toward requires us to build interconnected workflows, whether it’s sharing gap information to the provider, receiving feedback or gap responses and gap evidence back from the provider.

According to a 2025 Optum customer survey, only 5 – 10% of Optum customers have FHIR resources at their disposal.¹ As a surprising 90% of customers struggle to identify or get access to digital data, Optum can help with that data access and validation effort, a critical step in supporting the transition toward value-based care.¹

We’ve kept our regulatory understanding of AI analytics and measures whole. While AI continues to dominate industry discussions, some of its highly practical applications emerge in quality measurement and quality improvement workflows.

Organizations increasingly use AI-enabled analytics to prioritize opportunities, segment populations and identify which interventions may have the greatest potential impact. AI is also helping teams process large volumes of clinical information more efficiently, supporting chart review activities and surfacing relevant insights faster.

We’ve introduced advanced analytics beyond traditional measure results and outputs to help identify prioritized, meaningful impact areas, including the likes of gap probability scores and member segmentation scores.

We’ve also built technology to scan through charts, helping reduce associated manual review costs by up to 80%, while also helping in identifying an additional 20% of gap closures from those charts sourced from other areas.²

However, technology alone is rarely enough.

While AI is important, it isn’t without oversight. Although we have an AI-enabled abstraction platform where AI suggestions appear, the final say resides with the clinician. We are still making sure that we utilize our clinicians' brainpower.

AI is proving highly valuable when used to augment decision-making, help reduce administrative burden and help teams focus their expertise where it matters most.

As value-based care models continue to expand, quality performance is becoming more directly tied to financial outcomes, contract performance and member experience.³ Organizations are using quality data not only to evaluate past performance but also to guide operational decisions, prioritize interventions and measure the effectiveness of improvement efforts throughout the year.

This evolution is encouraging health care leaders to view quality metrics as strategic business indicators rather than compliance measures alone.

Quality is becoming a key factor in terms of how value-based care contracts are being written and evaluated, keeping in mind that quality has now become whole as we move away from hybrid sampling and get into total population review.

In many organizations, quality performance is becoming a critical component of how success is defined and measured across value-based care programs.

Closing care gaps requires more than data sharing. It requires collaboration via interconnected, automated workflows.

This is the place where we, as in Optum, have been spending quite a bit of time, building interconnected automated workflows and expanding these workflows to multiple digital connections. We are also providing that process improvement to go along with it, where providers know exactly what the action is to be taken with easily visible gaps within the EMR itself. And from a payer standpoint, are you actually enabling that workflow?

Providers need timely, actionable information that fits naturally into existing workflows. Payers need efficient ways to share insights, support interventions and help reduce administrative burden for provider partners. Both organizations need visibility into opportunities and progress.

Highly successful partnerships are increasingly built around shared workflows, connected data and aligned goals. When information moves efficiently between organizations, teams may spend less time searching for answers and more time taking action.

As health care becomes more connected and value-based arrangements continue to mature, payer-provider collaboration will likely become even more important to quality improvement success.

Essentially, the time for digital and AI-forward quality improvement is now.

Health care organizations are facing a period of significant transformation. Digital quality measurement, interoperability requirements, AI-enabled workflows and expanding value-based care models are fundamentally changing how quality is measured, managed and improved. Quality is no longer just a compliance exercise, but a core component of long-term value-based care success.

Organizations don’t have to solve all challenges at once. That said, establishing a strong foundation for data access, workflow integration via payer provider collaboration, and year-round quality improvement can help position teams for future success.

Organizations that begin building these capabilities today may be better prepared to navigate the next generation of quality measurement while creating more efficient processes and better experiences for both providers and patients.