Healthcare Site Selection Using Drive-Time Analysis: A Practical Framework

Most healthcare facility siting decisions are made using radius buffers or ZIP code aggregates — both of which systematically misrepresent where patients actually come from and how underserved a community actually is. Drive-time analysis fixes this, but the methodology has to account for healthcare-specific access patterns that don't apply to retail.

How healthcare siting differs from retail

Retail site selection is fundamentally about capturing demand in concentrations of disposable income. You want to be where customers are and where competitors aren't. Healthcare facility siting has a different objective: placing a facility where patient access is limited, need is high, and capacity does not yet meet demand.

This changes the analysis in three important ways. First, you're optimizing for coverage gaps, not competitive avoidance — areas with no existing facilities of the same type are often your best locations, not your worst. Second, the patient population is defined by health need and payor mix, not just income or demographics. Third, drive time thresholds differ significantly by specialty: a patient will drive 45 minutes for a specialist they were specifically referred to; the same patient will not drive 45 minutes for a routine primary care visit.

Patient access zones by specialty type

The starting point for any healthcare site analysis is establishing the appropriate drive-time threshold for the facility type. This threshold — called the patient access zone or catchment distance — determines the population you are actually serving.

• Primary care clinics: 10–20 minute primary catchment. Patients will not routinely drive more than 20 minutes for a primary care appointment. HRSA designates areas where primary care is not available within 30 minutes as Health Professional Shortage Areas (HPSAs).
• Urgent care centers: 10–15 minute primary catchment. Urgency is time-sensitive; patients choose the nearest appropriate facility. Catchment zones above 20 minutes see sharply declining demand capture.
• Specialty outpatient clinics: 20–45 minute primary catchment. Patients accept longer travel for specialty care because referral, not proximity, drives the visit decision. Cardiology, oncology, and orthopedics typically see 30–45 minute willingness-to-travel.
• Hospitals and medical centers: 30–60 minute primary catchment for elective and planned admissions. Emergency admissions follow different logic — speed of access is paramount, and 15-minute ambulance access zones apply.
• Behavioral health and addiction services: 15–30 minute primary catchment. Transportation barriers disproportionately affect patients seeking behavioral health care; walkability and transit access are more relevant here than for other specialties.

Coverage gap analysis

A coverage gap is a populated area that falls outside the drive-time catchment of any existing facility of the same type. Identifying coverage gaps is the highest-value analytical output in healthcare site selection: it tells you which communities are underserved and where a new facility would create access rather than just redistribute it.

The methodology is straightforward. Generate drive-time isochrones from every existing facility of your type within the target market. Take the union of all these polygons — the combined area reachable within your access threshold from any existing facility. The population outside this union is your uncovered population.

In practice, markets are not binary covered/uncovered. The more useful output is a coverage gradient: areas within 10 minutes of a facility, areas 10–20 minutes away, areas 20–30 minutes away, and areas beyond 30 minutes. This gradient identifies where new facilities would have the most incremental impact on access.

Competitive density analysis

Coverage gap analysis tells you where access is absent. Competitive density analysis tells you where existing access is undersized relative to demand. A neighborhood with three primary care clinics serving 120,000 residents may still be underserved if those clinics have combined capacity for 60,000 patients. The metric to track is population-to-provider ratio within the drive-time catchment.

For each candidate site, count the competing facilities within the access zone polygon. Then calculate the approximate capacity of each competitor — providers per facility is a reasonable proxy when claims data is unavailable. Divide the catchment population by total capacity. Markets where this ratio significantly exceeds national benchmarks for the specialty indicate opportunity.

Healthcare competitive analysis differs from retail in one important respect: a certain level of competitive co-location is beneficial, not harmful. Specialty care clusters (cancer centers, orthopedic campuses) generate referral volume that benefits all facilities. Proximity to hospitals creates referral pathways for outpatient facilities. Competitive density analysis for healthcare should model referral proximity, not just competitive proximity.

Payor mix and demographic analysis

The population within a drive-time catchment is not homogeneous in terms of financial impact on the facility. Payor mix — the distribution of Medicare, Medicaid, commercial insurance, and uninsured patients — determines the revenue per patient encounter and thus the financial viability of the location.

Drive-time catchment demographics feed payor mix estimation. Census block group data provides age distribution (a strong proxy for Medicare eligibility), income distribution (a proxy for Medicaid eligibility and uninsured rate), and insurance coverage rates from the American Community Survey. For facilities with tight margin requirements, a catchment with a high Medicaid and uninsured proportion may require charitable mission framing or cross-subsidization from other service lines.

A four-step healthcare site selection framework

1. Define the access zone. Set the drive-time threshold appropriate to your facility type. Map your candidate sites' catchment polygons using a road network routing engine — not radius buffers.
2. Identify coverage gaps. Map the union of all existing competing facility catchments. Find the populated areas outside this union. These are your highest-priority candidates.
3. Assess competitive density. For each candidate site, count competing facilities within the catchment polygon and estimate total capacity. Calculate the population-to-capacity ratio. Identify sites where this ratio indicates unmet demand.
4. Analyze payor mix. Pull Census ACS data for the catchment polygon: age distribution, income quintiles, health insurance coverage rates. Model estimated payor mix. Filter candidate sites for financial viability before committing to detailed real estate due diligence.

FAQ

What drive time is acceptable for patient access to a primary care clinic?

For primary care and urgent care, HRSA guidelines suggest that drive times exceeding 30 minutes create a meaningful access barrier. Most health systems target a 10- to 20-minute primary catchment zone for primary care clinics. Specialty care operates on longer thresholds — 30 to 60 minutes — because patients accept longer travel for less frequent, higher-acuity services.

What is a healthcare coverage gap?

A coverage gap is a populated area that falls outside the drive-time catchment zone of any existing facility of a given type. Drive-time isochrone mapping identifies these gaps by subtracting the union of all existing facility catchments from the total population map.

How do you measure competitive density for a healthcare facility?

Count facilities of the same type within the proposed location's drive-time catchment zone. Weight competitors by capacity and calculate the ratio of catchment population to total existing capacity. Areas with high population-to-capacity ratios indicate underserved markets.

What is the difference between healthcare and retail site selection?

Healthcare siting prioritizes access equity and coverage gaps over competitive positioning. It optimizes for patient access in areas of demonstrated health need, and accounts for payor mix, referral network proximity, and regulatory factors like certificate of need (CON) laws — none of which apply to retail.