Healthcare industry site score models use Buxton selected data variables to score and compare potential location trade areas to industry existing location trade areas, allowing you to understand how a trade area ranks in terms of consumer demand, competition, and nearby draw factors. Buxton offers a growing library of industry models for various service lines and facility types.

Each healthcare industry site score model uses an industry-based patient profile. Healthcare industry patient profiles classify U.S. households by their likelihood to utilize each healthcare service or facility type. They are developed using the national Medical Expenditure Panel Survey (MEPS). Using the survey results, Buxton analyzes health conditions for thousands of survey participants of various age, race, ethnicity, education, and income groups and then translates these findings to each relevant industry model and Experian’s Mosaic 71 Segmentation system.

Experian has analyzed demographic and psychographic data for all households across the U.S. and grouped like households together. There are 19 groups, represented by letters A-S, that contain 71 distinct segments, represented by numbers 1-71. These groups provide a comprehensive view of consumer households’ behaviors, preferences, and habits.

For each industry model profile, segments are designated as a Primary, Secondary, or Tertiary core patient segment or as a Non-Core segment.

Primary Patient Segments – segments that are composed of households that are most likely to utilize the specified industry's services and denoted in Buxton documentation by the color red.

Secondary Patient Segments – segments with households that are next most likely to utilize the specified industry’s services and denoted by the color blue.

Tertiary Patient Segments – segments that are possible growth or opportunity segments for utilizing an industry’s services and denoted by the color yellow.

Non-core Patient Segments – segments that are composed of households that are not as likely as core segments to utilize the specified industry’s services. These segments are denoted by either the color grey in Buxton documentation or have no color at all.

Note: Only Primary segments are loaded to SCOUT given the breadth of some industry profiles, allowing clients to focus on the most likely patients.

Healthcare industry site score models are composed of model variables known to impact location performance based on research, theoretical relevancy, and Buxton’s extensive experience in developing site selection models in the healthcare space.

Each industry model includes variables for the following:

• Patient Value (50%) – these variables account for the density and presence of core patient households, service line demand, and key patient demographics

• Area Draw (25%) – these variables account for the presence of area businesses, institutions, and healthcare facilities that bring traffic and visibility to locations and have a positive effect on industry model scores

• Competition (25%) – these variables account for the presence and density of competition within the trade area where higher competition has a negative impact on the model score

You have the option to modify the models to focus on specified patient age cohorts and/or insurance coverage types. These options allow you to customize the location site selection model to only consider the patients that meet your business case.

Patient Age Cohorts

Insurance Coverage Types

These options adjust the core patient profile (if using Buxton’s industry standard profile) for the selected age and insurance coverage patient groups and narrow demand variables in the models to the relevant patient groups. Competition is also adjusted to emphasize pediatric competition and competition that accepts Medicare/Medicaid where data is available and is relevant based on user customization selections.

To ensure logical consistency, there are rules applied to age & payor customization selection options.

Buxton Healthcare Industry Hotspots provide a view of how market area real estate scores, giving the user a jumpstart in evaluating strong trade areas for potential locations or for adding additional service lines to existing facilities.

The thematic map layers display healthcare industry model scores for block groups that contain a commercial retail or healthcare real estate seed point. The top 80% of block groups (by locations score) for each industry model are represented in these thematic maps.

Note these hotspots are not meant to be used as a market optimization. They are meant to convey, at a glance, areas that the model identifies as the strongest opportunities in each market. The hotspots can be found in Results / Custom Thematics / Service Line Results.

In SCOUT, right click on the potential location and select the option to score that site with the industry models.

You can then select up to 20 benchmark locations (must select at least 5) for comparison. These may be your own facilities, other potential locations that you are considering, or competing facilities.

Alternatively, you can compare against Buxton’s default selection of locations. These are pre-defined sets of locations within the market of interest, similar density, and are specific to the industry model being used for scoring.

Buxton sample sets for each industry model are specific to each industry model (i.e. primary care locations are used as the sample set in primary care models) and are pre-determined for consistently comparable results.

The following describes the geographic order in which relevant locations are determined:

At each geography level, the locations are sampled at a BUDS distribution that matches the market level. For example, if 1.2K primary care locations in DFW for BUDS 4/5/6 are distributed as 60%/35%/5%, then the 50 sample set locations would have that same BUDS distribution of 60% BUDS 4, 35% BUDS 5, and 5% BUDS 6.

These variables account for the density and presence of core patient households, service line demand, and key patient demographics. Measured at half the drive-time trade area by model type unless otherwise noted.

Patient Value scores account for ALL households in the trade area, both core and non-core. Primary households are worth more than secondary ones which are worth more than tertiary, which are worth more than non-core. But every household gets some value. Proximity is also a factor, where closer households are worth more than those farther away.

Residential Core Patient Score

Takes into account the segmentation of households within the full trade area and their distance to a facility. Household values are determined by both segmentation with respect to the core patient profile and proximity to the facility, where closer potential patient households are more valuable.

Workplace Core Patient Score

Takes into account the segmentation of persons at their workplace within the trade area and their distance to a facility. Workplace employee values are determined by both segmentation with respect to the core patient profile and proximity to the facility, where closer patients at their workplace are more valuable.

Estimated Visits/Cases Current Year & 5-Year Growth

The demand projections are reasonable estimates of healthcare utilization for the current year and a five-year forecast. The estimates are built on the foundation of actual claims, including all payor types, and modeled out to represent the full population by 5-Digit ZIP based on patient origin.

Average Expenditures

Estimated out-of-pocket healthcare related expenses per household.

Population/Demographic Estimates

Built from the most recent census, with annual estimates and five-year projections.

Area Draw

These variables account for the presence of area businesses, institutions, and healthcare facilities that bring traffic and visibility to locations and have a positive effect on industry model scores. Measured at one mile.

Count of facility type or provider type that is relevant to each model type.

Competition:

These variables account for the presence and density of competition within the trade area where higher competition has a negative impact on the model score. Measured at the full drive-time trade area unless otherwise noted.

Core Households per Service Line Physician/Facility

Ratio of core households from the profile to the provider type or facility type.

Count of model-specific facility types at the full drive-time trade area plus one mile.

Decayed counts are used for additional negative impact from competitors that are nearby over those that are farther away. This accounts for the likelihood that a nearby competitor hurts more than one that is farther away from your facility.

Count of model-specific facility types at the full drive-time trade area less ½ mile.

Donut counts are used to account for effects where competitive proximity is either positive or neutral if a facility is very close but negative if farther away. This variable measure is meant to capture the medical district or medical professional building effect whereby locating directly next to a competitor can be beneficial to both practices due to the draw of a medical activity area.

The healthcare industry models support both industry profiles that are based on Buxton research and client-specific profiles that are based on your own patient data.

The model solution cannot toggle back and forth between options. Each healthcare model can only be set up to run with either the industry profile or the desired patient profile for that industry model.

Additionally, both standard industry models and enhanced industry models (those with a custom profile) use the industry standard trade areas. There is not a custom trade area analysis even when custom profiles are generated.

Yes. For standard industry solutions, only primary segments are loaded to SCOUT. Given the breadth of many industry profiles, this allows clients to understand where the best likely patients are. For enhanced solutions with custom profiles, each core segment is loaded to SCOUT.

Yes! The healthcare industry models provide a benchmark score and can be used to provide a potential analysis via the Optimizer product just like any other benchmark or predictive model. The Optimizer would take the client’s existing network into account when spacing and recommending new potential locations.

Did this answer your question?

😞😐😃