Estimated annual users
Residents expected to have at least one included specialty visit during a year.
Understanding the Data
Using specialty demand data in healthcare location decisions.
Connect the population in a market to the specialty care it is expected to use.
Bring population-based utilization estimates into your location models, market assessments, and client conversations. Your analytics provider combines this demand input with other evidence to assess a particular location.
What do the estimates measure?
CurisData applies survey-based healthcare utilization relationships to the demographic profile of a defined population. The models account for age, recorded sex, race and ethnicity, region, and income.
Estimated annual visits
Total included specialty visits those residents are expected to generate, including repeat visits.
The current specialty dataset covers 25 categories of office-based and hospital outpatient physician visits. Its survey foundation represents the U.S. civilian, noninstitutionalized population, including insured and uninsured people.
Utilization and disease burden answer different questions
- Incidence: New cases of a condition during a defined period.
- Prevalence: People living with a condition at a point in time or during a period.
- Utilization: People receiving care and the visits they generate.
A person can have a condition without receiving specialty care. A visit may involve screening, evaluation, or follow-up. Specialty users and visits should therefore not be interpreted as disease cases.
A demand input for your analytical workflow.
Two equally populated markets may generate different specialty use because their demographic profiles differ. Reviewing both volume and compatible population-based rates helps separate market size from utilization intensity.
Models and market screening
Join block-group estimates to your geographic variables. Evaluate specialty users and visits alongside competition, accessibility, patient profiles, and observed operating performance. Test the contribution against your own outcomes and validation process.
Client reporting
Illustrate resident-generated specialty use in maps and market assessments. Give clients a clear demand baseline and explain how it contributes to the broader location recommendation.
Incorporating the data into a model does not by itself establish improved predictive accuracy. Its contribution should be evaluated for the specific application.
From resident demand to clinic opportunity.
The estimates follow where residents live, wherever they receive care. Residents may travel outside the area, and clinics may attract patients from elsewhere. Analytics partners combine this baseline with patient travel, referrals, competition, access, capacity, and achievable share.
Utilization reflects access and care-seeking patterns as well as health needs. Lower modeled use does not automatically mean lower clinical need. Establishing unmet need, provider shortages, or financial viability requires other evidence.
How the estimates complement claims.
CurisData supplies a modeled population baseline. Claims can supply observed activity, service detail, provider relationships, and patient flows within the coverage of a particular dataset.
| Dimension | CurisData specialty estimates | Claims analysis |
|---|---|---|
| Starting point | Utilization relationships applied to local demographics. | Claims or encounters and, where available, enrollment. |
| Contribution | Expected resident-generated users and visits. | Observed services and patient movement where supported. |
| Review carefully | Survey support, model assumptions, demographic compatibility, and local applicability. | Payer and population coverage, record maturity, coding, and visit definitions. |
Both approaches rely on historical evidence. Align population, geography, period, specialty, included settings, and visit definitions before comparing them. A gap between claims and modeled utilization prompts investigation; it does not establish an underserved market by itself.
Keep the context with the numbers.
- Source periods: The current specialty release uses ACS 2020–2024 demographics and a 2024 utilization baseline informed by 2022–2024 survey records. These are not point-in-time 2026 observations.
- Population scenarios: Compatible current or projected demographics can change the population evaluated. Future treatment, access, insurance, and behavioral changes require separate assumptions.
- Aggregation: Sum counts across nonoverlapping areas before calculating rates. Overlapping radii cannot be added; users across specialties are not unique patients.
- Report geography: Current radius reports include whole intersecting block groups. The included population can extend beyond the drawn circle.
- Quality: Retain warnings about sparse survey support and reconstructed demographic inputs. Fine geographic detail does not establish equally fine local accuracy.
- Sampling ranges: Conditional ranges hold demographic inputs fixed and exclude other uncertainties. Do not add published interval endpoints across areas; aggregate ranges require shared model replicates.
A practical evaluation process.
- Define the specialty, geography, included services, and decision.
- Review users, visits, compatible rates, source periods, and quality indicators.
- Combine the baseline with competition, claims, patient flows, and operating knowledge.
- Evaluate model contribution or state explicit capture and capacity assumptions.
- Test sensitivities and document what could change the recommendation.
For Analytics Partners
Explore the application. Evaluate the data.
Review a sample assessment, then discuss coverage, delivery, and licensing for your modeling and reporting workflows.
Further reading
AHRQ: Medical Expenditure Panel Survey