26.7 million uninsured people
Estimated within the defined under-65 population. Explore two maps, state comparisons, an income analysis, and the assumptions behind each measure.
Explore the full case study →Healthcare Data Analytics
Connect your data.
Get a clearer answer.
Understand populations, access barriers, research findings, and product performance by bringing the right data sources into the same analysis.
Healthcare decisions rarely fit inside one dataset. Population estimates can show where a coverage gap is concentrated. Survey responses can show which barriers people report. Product data can show where users drop off. We help teams connect these perspectives, assess what the evidence supports, and decide what to investigate or change next.
Featured analytics case study
Mapping the US health insurance gap.
Which locations should a healthcare team investigate first? We used Census SAHIE estimates to compare uninsured rates, population counts, and income groups across 50 states and Washington, DC. The maps show why a high uninsured rate and a large uninsured population can lead to different geographic priorities.
A defined population, not a sales forecast
This independent public-data study demonstrates population analytics and geographic interpretation. It provides a starting point for access research and outreach planning. It does not identify eligible benefit recipients, reachable customers, or a measured commercial outcome.
Questions we help answer
Start with the decision, then choose the analysis.
The question determines the population, sources, measures, and level of detail. A geographic planning study needs different evidence from a survey analysis or an investigation of product drop-off.
Where should we focus?
Compare population size, demographic characteristics, and geographic variation. Separate rates from counts so a small area with a large proportional gap does not get confused with a large area containing more affected people.
What differs across audiences?
Analyze patient, caregiver, HCP, or staff survey responses across relevant groups. Look at reported needs, preferences, and barriers alongside sample composition and uncertainty before drawing conclusions about a wider population.
Where does the experience break down?
Examine adoption, engagement, conversion, and retention in a healthcare product or service journey. Use research findings to interpret behavioral patterns and identify questions for follow-up testing.
Multiple sources of evidence
One dataset rarely tells the whole story.
We can work with public population data, research datasets, product activity, and client-provided operational data. The value comes from knowing what each source measures and where the sources can meaningfully connect.
For example, a population estimate can describe the scale of a coverage gap in a region. A survey can describe barriers reported by its respondents. Product records can show whether users complete an onboarding step. These sources answer different questions. Bringing them together can give a team a fuller picture without treating them as interchangeable.
Public population and geographic data
Census and other documented public datasets can support population profiles, regional comparisons, and maps. We define the age group, geography, time period, denominator, and uncertainty before interpreting differences.
Surveys and research datasets
Questionnaire responses, audience characteristics, and coded research findings can support comparisons and segmentation. We review recruitment, missing responses, question wording, and sample limitations before generalizing results.
Product and service activity
Client-provided event exports, funnel data, and engagement measures can show how people move through a digital experience. We check event definitions and tracking coverage before treating a drop-off as a product problem.
Operational and market context
Aggregated service records, CRM exports, service locations, and market information can add context where relevant and available. Access, permissions, data sensitivity, and source compatibility are assessed before inclusion.
Combine sources without forcing a match.
Some datasets can be joined using a reliable shared identifier, geography, or time period. Others should be compared alongside each other. We check the unit of analysis, definitions, coverage, and update cadence before deciding which approach is valid. A state-level population estimate cannot establish what an individual survey respondent experienced.
The resulting analysis keeps the source of each measure visible. Differences between sources are documented rather than hidden inside a blended number.
Research areas and analytical scope
Analysis built around the question you need to answer.
Choose a focused analysis or connect several sources around the same decision. The methods depend on the population, data quality, permissions, and intended use.
Population and geographic analytics
Compare uninsured rates, population counts, income groups, and regional coverage gaps using documented sources such as Census SAHIE. Define the age range, year, geography, and denominator first. Pair rates with counts to distinguish a concentrated gap from a large population needing further investigation. Maps support geographic prioritization; they do not establish individual eligibility or expected sales.
Survey and audience analytics
Analyze patient, caregiver, HCP, and staff responses to understand reported barriers, preferences, and support needs. Crosstabs and group comparisons can show where experiences differ. Weighting, significance tests, and segmentation are used when the sampling design and available data support them. Keep recruitment and response limitations visible rather than treating a convenience sample as a population estimate.
Care journey and behavioral analytics
Examine intake, appointment booking, onboarding, and other defined steps in a healthcare service or digital product. Compare completion and drop-off by audience, channel, or period after checking event definitions and tracking coverage. Bring in interviews or usability findings to investigate the friction behind a pattern. A completed digital step is an operational measure, not evidence of an improved health outcome.
Data quality and source reconciliation
Review survey tables, product events, CRM exports, and suitable aggregated operational records for missing values, duplication, identifier inconsistencies, and reporting-period differences. Decide whether sources can be joined through a reliable key or should remain separate perspectives. Document matching rules, excluded records, and unresolved gaps so a combined view does not hide incompatible populations or definitions.
Measurement strategy
Define the question behind each metric, its numerator and denominator, source, owner, reporting cadence, and calculation rules. A healthcare team may need to distinguish inquiries from booked appointments, completed onboarding from ongoing use, or stated barriers from observed behavior. Establish a baseline and comparable reporting periods before interpreting change. Clinical outcomes require appropriate measures and a study design suited to that question.
Dashboards and decision reporting
Bring maps, audience comparisons, trends, and operational measures into views a team can use to choose a next step. Show the source and update period for each measure, with uncertainty where supplied or defensibly estimated. Keep incompatible sources separate and explain discrepancies. Deliver definitions, assumptions, and interpretation alongside the visuals so the dashboard supports a decision rather than simply displaying activity.
Methodology
Make the reasoning as clear as the chart.
We agree on the question and intended use before choosing methods. The work documents where the data comes from, how it was prepared, which comparisons are valid, and what remains uncertain.
Define and prepare
- Specify the population, geography, period, and unit of analysis.
- Review source provenance, availability, permissions, and limitations.
- Assess missingness, duplicates, consistency, and completeness.
- Document cleaning rules and any source matching or aggregation.
Analyze and interpret
- Select descriptive, survey, geographic, or behavioral methods that fit the question.
- Check sample limitations and uncertainty where available.
- Separate observed patterns from explanations that still need testing.
- Translate the findings into decisions, follow-up questions, and documented outputs.
Before a project starts
What do we need to know?
Can you work with more than one data source?
Yes. The scope can combine public datasets, surveys, product data, and suitable client-provided records. We first check whether definitions, units, identifiers, geography, and time periods allow a meaningful connection. Sources without a reliable match can still contribute separate perspectives to the same question.
What if our data is incomplete or inconsistent?
A data assessment can be the first deliverable. We identify what the available evidence can support, which gaps need attention, and whether the original question needs a narrower scope or additional research.
What will our team receive?
The agreed scope may include an analytical report, maps, a dashboard, a documented dataset, or a measurement framework. The output includes definitions, assumptions, limitations, and interpretation. We choose the deliverables around how your team will use the findings.
Can you analyze sensitive healthcare records?
Data sensitivity, permissions, and handling requirements must be reviewed before any transfer or analysis. Share a description of the data during initial scoping. Do not submit patient-identifiable records through the website's quote form.
Can analytics explain why a pattern occurs?
Analytics can identify differences and relationships. Those patterns do not automatically establish a cause. Interviews, additional data, or a suitable study design may be needed to test an explanation before acting on it.
Evidence generation support
Planning a real-world evidence project?
Explore how data assessment, descriptive analysis, and patient and HCP research can contribute to an RWE initiative.
Explore Real-World Evidence →Get a quote
What should your data help you decide?
Tell us the question, the sources you have, and your timeline. We will propose a focused scope, suitable methods, and useful deliverables.