Healthcare commercial intelligence (HCI) turns provider, payer, and market data into sharper targeting, territory design, and access decisions. Learn what it is, how it works, the data behind it, and how to build an HCI capability that drives growth.
Healthcare has plenty of data. What it often lacks is clarity that survives contact with the real world.
A “top account” might be a health system with multiple campuses, a physician enterprise with shifting affiliations, and a payer mix that quietly blocks adoption. Meanwhile, field teams are expected to prioritize, forecast, and grow in an ecosystem where the decision maker is not always obvious and the buying path rarely looks like a clean funnel.
That is the problem healthcare commercial intelligence is designed to solve.
This article breaks down what healthcare commercial intelligence is, what data powers it, the most valuable use cases, and a practical playbook to implement it without creating yet another dashboard nobody trusts.
Healthcare commercial intelligence (HCI) is the practice of integrating and analyzing healthcare market data to make better commercial decisions, such as which accounts to target, how to size and segment opportunities, how patients move through care, and what payer or access barriers will slow adoption.
In most industries, an “account” is relatively stable. In healthcare, an account is often a network.
Healthcare commercial intelligence exists to make that complexity usable for targeting and growth.
BI is usually internal-first: pipeline, conversion rates, product usage, revenue, churn.
HCI is external-first: who delivers care, where volume concentrates, how patients move, how organizations are structured, and how payers control access.
Market research is often episodic and qualitative. Commercial intelligence is operational. It needs refresh cycles, traceability, and a path into daily workflows.
The FDA defines real-world data (RWD) as data relating to patient health status and/or the delivery of healthcare, routinely collected from a variety of sources, including EHRs, claims, registries, and digital health technologies.
RWE is clinical evidence derived from analyzing RWD. HCI may use some of the same raw materials, but its outputs are commercial decisions: segmentation, targeting, territory planning, patient journey insights, and access strategy.
A mature HCI capability usually does four things well. If one is missing, the program tends to feel impressive but unreliable.
Healthcare data is full of duplicates and mismatched identifiers. One organization can appear under multiple names. A clinician can have multiple practice addresses and affiliations. Systems change ownership.
If you cannot reliably connect “who is who,” every downstream insight is fragile.
In healthcare, relationships often define the commercial strategy.
Who is affiliated with whom? Which facilities belong to which system? Where do referrals flow? Who influences clinical adoption versus contracting decisions?
Commercial intelligence creates a coherent map of these relationships so sales, marketing, and market access teams can act with more precision.
Good sizing is not “big market.” It is “reachable market.”
The Reachable market considers volumes, care settings, payer mix, and access constraints, plus whether the product fits the operational reality of those accounts.
An insight that lives only in a dashboard tends to die in a dashboard.
Commercial intelligence needs to show up where teams work:
If HCI does not change day-to-day decisions, it becomes reporting, not intelligence.
You rarely build strong HCI from one dataset. You build it from a blend, and the blend depends on your business model.
Here is a practical view of common inputs and what they are good for.
|
Data source |
What it captures |
Strengths for commercial teams |
Common blind spots |
Best questions it answers |
|
Claims data (medical + pharmacy) |
Billed events tied to reimbursement activity |
Excellent for utilization patterns, scale, patient journey signals |
Lag, coding variability, gaps for cash pay or uninsured |
Where is volume? Who treats what? What does patient flow look like? |
|
EHR-derived data |
Clinical record data like diagnoses, labs, meds |
Rich clinical context, closer to the care moment |
Fragmented access, variable quality across systems |
Which cohorts exist and how are they managed clinically? |
|
Provider + facility reference data |
Who and where care is delivered, plus affiliations |
Better targeting, territory logic, account mapping |
Becomes stale without refresh and de-duplication |
Who should we engage, and how are they organized? |
|
Formulary, medical policy, coverage rules |
Coverage status and restrictions (PA, step therapy, tiers, etc.) |
Explains access friction and uptake issues |
Changes often, controller relationships can be complex |
What barriers block adoption in specific plans and regions? |
|
Government and public datasets |
Public signals about facilities and programs |
Authoritative baseline validation |
Not designed for commercial workflows |
Where are eligible sites and programs? |
|
Unstructured and digital signals |
Websites, publications, hiring, announcements |
Early indicators of change |
Noisy, requires governance |
What is changing before it shows up in claims? |
A note on claims data, because it is often treated as “ground truth.”
Claims are a record of billed and processed healthcare transactions. Datavant distinguishes closed claims as finalized and resolved transactions that have undergone review, processing, and reimbursement determination, offering a comprehensive view of completed encounters and associated costs.
For teams that care about speed, “open claims” datasets can surface signals earlier. HealthVerity describes open claims as sourced from clearinghouses, pharmacies, and software platforms, often capturing activity earlier than fully adjudicated payer claims.
The practical takeaway: claims can be incredibly powerful for market and patient-flow understanding, but every commercial intelligence team should be explicit about freshness, completeness, and confidence.
If payer coverage shapes adoption, payer intelligence is not a side project. It is part of commercial reality.
MMIT describes the entity that controls the formulary decision as the controller, and notes that controller may be a payer, a PBM, an MCO, or a government entity, depending on the situation.
This matters because commercial teams often build payer strategy around the wrong decision owner. When uptake misses forecast, the symptom is “low adoption,” but the cause can be a restriction pattern, controller dynamic, or coverage delay that the GTM plan did not model.
If you are selling into categories affected by coverage and utilization management, good HCI links provider opportunity with access reality.
Commercial intelligence becomes valuable when it changes decisions. These are the use cases that most often move revenue outcomes.
This is the headline use case, but it is also the one most likely to fail if entity resolution is weak.
High-quality targeting typically combines:
Territories built purely on geography tend to ignore where care actually happens and how systems are structured.
Claims-based patterns can reveal diagnosis, treatment, and prescription activity at scale.
In many care pathways, referrals determine where adoption spreads and where it stalls. Commercial intelligence that helps map patient movement is a major competitive advantage, especially in specialized care areas.
Some “influential” targets are not high-volume targets. Some high-volume targets are not early adopters.
HCI helps you separate influence from scale, then build launch sequencing that makes sense across regions, networks, and care settings.
Market access issues rarely announce themselves loudly. They show up as slow starts and inconsistent initiation.
HCI that includes payer rules and controller dynamics makes it easier to diagnose why adoption is missing, and where to focus access efforts.
You do not need to boil the ocean to get value, but you do need sequencing.
Pick 2 to 3 decisions where uncertainty is expensive, such as:
Define what success looks like in plain terms. If a commercial leader cannot explain how it will change action, it is not a good starting point.
At minimum, you need a consistent structure linking:
This becomes your data spine.
For many teams, claims is the leverage dataset because it scales and reveals patterns across billions of events.
For some healthtech products, provider and facility reference data might produce faster wins in targeting accuracy.
HCI should land inside workflows, not beside them.
Examples:
The best HCI programs treat data quality like a product.
Track a handful of metrics:
Commercial intelligence often touches health data, even when it is de-identified.
HHS guidance on HIPAA de-identification describes two methods under the Privacy Rule: Safe Harbor and Expert Determination.
From a practical standpoint, this means your HCI capability should define:
Field adoption is fragile. If teams do not trust provenance or governance, they disengage, and the organization slides back to gut-driven targeting.
Healthcare commercial intelligence is how teams use provider, payer, and market data to decide who to target, where opportunity is real, how patients move through care, and what access barriers will slow adoption.
Common inputs include claims, EHR-derived data, provider and facility reference data, and payer coverage or formulary rules.
No. Healthtech companies, medtech firms, services providers, and even healthcare providers can use HCI to identify opportunities, prioritize resources, and understand how care and decision-making are structured.
The FDA defines real-world data and describes real-world evidence as clinical evidence derived from analyzing that data. Commercial intelligence may use similar data sources, but focuses on commercial decisions like targeting, territory planning, and access strategy.
Healthcare commercial intelligence is not about collecting more data. It is about turning fragmented market reality into decisions that hold up.
When done well, it helps you: