The HLS industry map

Last reviewed: 2026-06-28 — see the freshness policy.

Learning objectives

After this chapter you will be able to:

  • Name the major segments of health & life science and what each one buys technology for.
  • Explain why "healthcare" and "life science" have different architectural centers of gravity.
  • Identify who your stakeholders are likely to be in each segment.

The segments

"Health & life science" is not one industry — it is a value chain with very different buyers, data, and regulators at each stage.

flowchart LR
  subgraph LifeScience["Life science (discovery → product)"]
    Biotech["Biotech / Pharma<br/>drug discovery, trials"]
    CRO["CROs<br/>outsourced trial execution"]
    Medtech["Medtech / Devices<br/>instruments, SaMD"]
    Dx["Diagnostics / Genomics<br/>labs, sequencing"]
  end
  subgraph Healthcare["Healthcare delivery (care → payment)"]
    Providers["Providers<br/>hospitals, clinics, AMCs"]
    Payers["Payers<br/>insurers"]
    PBM["PBMs<br/>pharmacy benefits"]
    DigitalHealth["Digital health<br/>apps, telehealth"]
    PublicHealth["Public health<br/>CDC, state/provincial"]
  end
  Biotech --> CRO --> Dx --> Providers --> Payers --> PBM
  Medtech --> Providers
  DigitalHealth --> Providers
  Providers --> PublicHealth

Life science

  • Biotech & pharma — R&D and clinical trials. Data is experimental, longitudinal, and regulated for integrity (GxP, 21 CFR Part 11). Architectures center on data platforms, HPC/genomics, and trial systems. Cost of a wrong answer is measured in failed trials.
  • CROs (Contract Research Organizations) — sponsors (pharma/biotech) outsource most trial execution to CROs (IQVIA, ICON, Parexel, LabCorp, Charles River): patient recruitment, site monitoring, data management, regulatory submissions. Over half of industry-sponsored trials now run through a CRO, and it's a $80B+ market — architecturally this means your trial-data platform usually has to integrate with an external CRO's EDC/systems, not just your own.
  • Medtech & devices — physical instruments and Software as a Medical Device (SaMD). Architectures center on edge/IoT, telemetry, and FDA-cleared software lifecycles.
  • Diagnostics & genomics — high-throughput labs. Architectures center on sequencing pipelines, the LIS/LIMS, and turning terabytes of reads into a clinical report.

Healthcare delivery

  • Providers (hospitals, health systems, clinics) — run on EHRs (Epic, Oracle Health/ Cerner). Architectures center on interoperability (HL7v2, FHIR), integration engines, and analytics on top of clinical data. HIPAA is the dominant regulation. Academic Medical Centers (AMCs) are a hybrid worth naming separately — they are providers and life-science research institutions at once, so both regulatory worlds (HIPAA and GxP/IRB) apply inside the same organization.
  • Payers (insurers) — claims, eligibility, actuarial models. Architectures center on large-scale data warehousing and increasingly FHIR (CMS mandates).
  • PBMs (Pharmacy Benefit Managers) — a distinct segment from general payers: they administer the drug benefit specifically — real-time claims adjudication, formulary management, and rebate negotiation. See Document & claims standards (NCPDP) and the PBM claims-engine lab.
  • Digital health — apps, telehealth, remote monitoring. Cloud-native, fast-moving, but still HIPAA-bound the moment they touch PHI.
  • Public health agencies (CDC, state/provincial health departments) — surveillance, mandated reporting (e.g. cancer registries — see Oncology data), and increasingly a TEFCA/FHIR exchange participant, not just a downstream report recipient.

Providers, payers, and public health agencies also connect through Health Information Exchanges (HIEs) — regional or national networks that move data between organizations that don't share a vendor; see EHR integration for point-to-point patterns and HIE architecture for the network-hub design HIEs use instead.

Why the center of gravity differs

Segment Dominant data Dominant regulator Architect's first question
Pharma R&D experimental, genomic FDA (GxP / Part 11) Is the data trustworthy and traceable?
Providers clinical (EHR) HHS/OCR (HIPAA) Can systems interoperate safely?
Payers claims, financial CMS / state Can we scale and report?
Medtech telemetry, SaMD FDA (510k / PMA) Is the software safe and validated?

A pattern that is excellent for a provider (a FHIR interoperability gateway) is irrelevant to a pharma HPC pipeline. Know which segment you are in before you reach for a pattern.

Stakeholders you will meet

  • Clinical / scientific — physicians, nurses, bench scientists, bioinformaticians. They own the "is this correct and useful?" question.
  • Compliance / regulatory / privacy — HIPAA security officers, GxP QA, IRB. They can stop a project; engage them on day one, not at launch.
  • IT / security — the CISO and platform teams who will operate what you design.
  • Business — the CFO who pays for it and the product owner who scoped it.

Check yourself

  1. A startup builds a sequencing-based cancer test sold to hospitals. Which segments does it touch, and which regulators apply?
  2. Why is "interoperability" the provider architect's first concern but rarely the pharma R&D architect's?
  3. Who can unilaterally stop your project, and when should you involve them?

Further reading

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