Every health-system executive is being asked to make bigger bets with less certainty: which service lines to grow, which sites to fund, which markets to hold, and which to leave. The plan behind each bet rests on assumptions about patient volume, payer mix, and demand. Many of those assumptions come from claims data that was built to pay bills, not to describe how care actually happens.
But how reliable is that plan?
A market may appear to have enough demand to support a new ambulatory surgery center, but the patient population may be overstated. A physician may appear to be a high-volume referral source when the underlying claims really show a different provider's activity. A service line may look healthy in an internal report while patients are increasingly completing their care elsewhere.
These aren't necessarily failures of strategy. And understanding these limitations is an important part of understanding your market.
Four ways market data can give an incomplete picture
Are you counting unique patients?
Your market may be smaller than your claims data suggests. Raw claims count the same patient many times over. Skewing the size of your market and misstating the size of opportunities. If you're using raw claims to estimate market demand, forecast revenue, or justify a service line expansion, you may be making decisions based on inflated patient counts and incomplete patient histories.
When Kythera unifies individual patient records across raw data sources, 51% of apparent patient IDs are duplicates or unreliable fragments and are resolved into unified patient records.
At the same time, unifying patient records reveals a more complete picture of individuals’ care: approximately 61% more claims per patient and 32% longer longitudinal coverage.
Can you reliably identify the operating surgeon?
You may be targeting the wrong surgeons for recruitment and growth. About one in three surgery claims names the wrong clinician as the surgeon, such as an assistant, anesthesiologist or facility, affecting provider-level volume analyses and recruitment.
Our analysis shows 32% of surgery claims lack a directly validated operating surgeon. Kythera uses related claims to identify the real surgeon and indicates the confidence of each match. More reliable surgeon-level volumes and referral intelligence helps health systems identify the right physicians to recruit, build relationships with, and align around surgical service line growth.
Do you know where surgical care actually happens?
Your claims may point to the wrong surgical facility. Claims data doesn't always identify where a surgical procedure took place. The facility associated with a claim may be a billing location rather than the hospital or ambulatory surgery center where care was delivered.
Kythera verifies the actual hospital or ambulatory surgery center for 82% of relevant surgical events. Distinguishing where care occurred from where it was billed and flagging unverified events rather than guessing.
When deciding where to expand surgical capacity or develop an ambulatory surgery center, you need to know where procedures actually occur. Otherwise, you risk directing capital toward the wrong facilities or markets.
Are the locations in your market analysis consistent?
Your next expansion decision could be based on the wrong market map. A more reliable picture of where care is delivered and where competitors operate helps health systems identify underserved markets, evaluate acquisition targets, and make better decisions about facility expansion or consolidation. But physical locations often appear under different names and addresses.
Kythera resolves 10.2 million inconsistent addresses into 3.8 million mappable locations, reducing duplication and improving the geographic accuracy of healthcare claims data.
These figures come from Kythera's national claims data, covering all available history through mid-2026: about 560 million raw patient IDs. Unified patient records have an average of about 5.5 to 6.5 years of history each.
In Kythera's analysis of raw claims data, the distortions are large enough to change the assumptions behind a financial plan. None of these is simply a data-quality statistic. Each exposes a potential weakness in a financial or strategic assumption.
The Impact of Data Reliability on Growth Decisions and Capital Allocations
Consider a health system evaluating whether to expand its cardiac service line. Leadership needs visibility into the number of patients that need care, where they receive it, which physicians influence their decisions, and how much of that volume the system could realistically capture. For example, one health system identified 39% more cardiac events than were visible and a nearly 5x increase in visibility into cardiac procedure leakage by using more complete data.
If patient counts are inflated, the addressable market may look larger than it is. If surgical procedures are assigned to the wrong facilities, the competitive picture changes. If the operating surgeon cannot be reliably identified, physician recruitment and referral development may target the wrong opportunities.
The same problems affect decisions about where to build facilities, which practices to acquire, and whether a struggling service line reflects declining demand or simply a loss of market share.
The cost isn't limited to making the wrong investment. It also includes opportunities that never make it into the strategic plan because the underlying data failed to reveal them.
Why Data Confidence Matters at Every Scale
The underlying need is confidence in decisions made under uncertainty. But the challenges differ considerably depending on the size and complexity of the health system.
For smaller and regional health systems, the challenge is turning data into insights.
Leadership teams face the challenge of making consequential decisions with limited strategy and analytics resources. They need to understand where they can compete effectively, which service lines warrant additional investment, and where partnerships may make more sense than building capabilities independently.
A regional system considering a new outpatient facility, for example, needs more than a population growth forecast. It needs to know whether patients in the surrounding market are already receiving those services elsewhere, which physicians influence that utilization, how payer mix affects the opportunity, and whether existing competitors have the capacity to absorb additional demand.
The objective is not to build a larger analytics department. It is to give a lean leadership team reliable evidence to challenge the assumptions in its plan and make a decision with greater confidence.
For larger health systems, the challenge is informed decisions across the portfolio.
Large systems typically have substantial analytics capabilities, but are challenged to maintain consistency across teams, data sources, market definitions, patient counts, and methodologies. When these don’t align, the result can be competing versions of the same market.
One region may calculate market share differently from another. Service-line leaders may use different definitions of addressable demand. Corporate strategy may have difficulty comparing investment opportunities across markets because the underlying assumptions are inconsistent.
For these organizations, a consistent, governed view of the market helps finance, strategy, and operational leaders spend less time reconciling competing reports and more time evaluating what the findings mean.
The value comes not only from faster analysis, but from reducing the time executives spend reconciling conflicting answers before they can act.
Questions to ask before your next major investment
Better market intelligence helps leadership determine where capital, people, and management attention goes. Before making those decisions, health systems should consider whether the information they’re relying on provides an accurate and consistent view of their market.
Are your growth projections based on an accurate picture of patient demand?
When an individual patient appears multiple times in the data, patient histories can be misstated, and patient counts can be inflated. Understanding actual patient populations, utilization patterns, and payer mix helps distinguish sustainable demand from growth projections built on incomplete or duplicated data.
Do you know where patients are receiving care across your market?
Internal data show what happens within the health system, but not where else patients go for care. External market data can reveal where care is delivered, the providers and facilities involved, and whether the evidence is strong enough to justify an intervention.
Are you evaluating investment opportunities using the same market assumptions?
A new facility, physician recruitment initiative, or practice acquisition should be evaluated against a consistent view of demand, competitive capacity, patient movement, and financial opportunity. This supports improved comparisons of competing investments rather than evaluating each in isolation.
What information would increase confidence in your decision?
Before committing capital, understand where data gaps or inaccuracies in your market data could undermine the business case. A more complete picture may validate your strategy, uncover an opportunity, or reveal a better place to invest.
Business decisions do not require every available market metric.They need reliable answers to a number of consequential questions, with enough transparency to understand the assumptions behind them.
