Value Analysis (VA) teams sit at the center of some of healthcare’s most important decisions—balancing clinical quality, financial stewardship, and operational consistency. Across health systems, VA committees are deeply engaged, highly collaborative, and committed to doing the right thing.
Yet many leaders still voice a familiar tension:
“Our Value Analysis committee is doing strong work—but it’s getting harder to keep up with the pace and complexity of decisions.”
This isn’t a reflection of effort or governance. It’s a reflection of how much healthcare data has grown—and how fragmented it remains.
As spend categories become more complex and decision velocity increases, Value Analysis is being asked to operate at a scale it was never originally designed for.
Traditional VA committees were built for a different era.
Most are designed to:
Review products and proposals episodically
Evaluate decisions using historical snapshots
Rely on consensus-building during scheduled meetings
This approach works well when:
Decisions are infrequent
Data is relatively static
Variation is easy to identify
Today’s reality looks very different—especially in high-cost, high-variation categories like implants and physician preference items.
VA becomes more reactive than leaders would like
Time is spent aligning on what the data says rather than what action to take
Insights arrive after utilization patterns have already shifted
This isn’t a failure of governance. It’s a scale challenge driven by fragmented data and lagging visibility.
Behind most VA meetings is a familiar process:
Pulling data from multiple systems
Reconciling usage, pricing, and contracts
Translating clinical activity into financial context
Building spreadsheets and slides just in time
This work is thoughtful, detailed, and necessary—but it creates unavoidable constraints.
Data is retrospective by nature
By the time information is assembled, it reflects what already happened, not what’s unfolding now.
Insights are episodic
Analysis occurs at meeting cadence, not at the pace of clinical activity.
Scaling becomes harder—not easier
As facilities, physicians, and products grow, effort increases faster than clarity.
Key insight
VA professionals bring deep judgment and experience. Fragmented data forces them to spend time assembling context instead of applying insight.
When insights arrive late:
Pricing variation is identified after spend occurs
Exceptions are reviewed individually instead of pattern-based
Standardization conversations start on the back foot
This can unintentionally shift VA into a defensive posture—responding to outcomes rather than shaping them.
Again, this isn’t about capability or commitment. It’s about timing and data flow.
As healthcare complexity increases, the question isn’t how to make VA work harder—it’s how to connect intelligence across systems, teams, and moments of decision.
Instead of asking:
“How do we tighten governance?”
“How do we add more review steps?”
Leading organizations are asking:
Is our data structured, timely, and connected enough to support how Value Analysis actually operates today?
Case-level insight, not just aggregated summaries
Continuous visibility instead of manual compilation
Signals that surface early—before patterns are entrenched
When intelligence is connected:
Governance becomes lighter, not heavier
Committees focus on judgment, alignment, and trade-offs
Decisions move faster with greater confidence
Organizations that successfully scale VA tend to share common traits:
Always-on visibility into utilization, pricing, and variation
Early signals that guide conversations proactively
Exception-based focus that respects clinical nuance
A shared language across clinical, supply chain, and finance teams
In these environments, Value Analysis becomes:
More strategic
More trusted
More impactful—without increasing workload
For supply chain and value analysis leaders, progress doesn’t come from adding pressure—it comes from improving connection.
Consider:
How much time is spent preparing data vs. discussing decisions?
Do insights arrive early enough to influence behavior?
Can variation be explained clearly across teams?
Is VA being asked to scale without a connected data foundation?
If those questions spark discussion, that’s not a concern—it’s an opportunity.
Value Analysis teams are already doing the hard work of alignment. As healthcare data continues to fragment across systems and workflows, the next evolution of VA is about Connected Intelligence—bringing the right insight, to the right people, at the right time.
When data catches up to the complexity of care, Value Analysis doesn’t just scale—it leads.