Industry solutions · Pharmaceuticals

Build compliance into the process — and turn data into auditable decisions

Pharmaceutical companies must answer “who do we sell to, and how much?” as well as “is it compliant, and is it safe?” With SAM at the core, we build the key judgments in customer credit, spend compliance, pharmacovigilance and demand forecasting into models that can be reviewed and replayed — fully on-premises, with data staying inside the company.

Credit · compliance · safety · supplyFully replayable run manifestsOn-premisesTry online for up to 10,000 samples
Researchers in a pharmaceutical R&D center work beneath curved screens showing protein structures and model-training data
The challenge

Three hurdles for pharmaceutical analytics

01

Highly sensitive data

Health information is sensitive personal information. Prescription, patient and healthcare-institution data are tightly controlled and often cannot leave the company.

02

Constant compliance pressure

Medical education, meetings and expense claims must be genuine and traceable. After-the-fact sampling cannot cover the volume of documents, so risks surface late.

03

Conclusions must be reviewable

Findings from pharmacovigilance, quality and clinical data must trace back to the data, the method and the person who signed off, and hold up to inspection and audit.

Use cases

Six core models across credit, compliance, safety and supply

Every analysis runs the same SAM fourteen-step pipeline, with statistics computed by deterministic code and key conclusions signed by named professionals. Model outputs support professional judgment; they do not replace medical or compliance assessment.

01 · Credit

Distribution customer credit scorecard

Business question
How much credit, and what payment terms, should distributors, pharmacies and institutions get?
Method
A credit scorecard built on purchasing, payments, inventory and past delinquency, with limits and terms tiered by score.
Deliverable
Customer credit grades, recommended limits and payment-term rules, reviewed on a schedule.
02 · Compliance

Promotional-spend compliance scoring

Business question
Which meetings, expense claims and invoices carry compliance risk?
Method
Rules, anomaly detection and link analysis, using our fraud-ring detection methods from anti-fraud.
Deliverable
Document risk scores and a review list for compliance and audit teams.
03 · Safety

Pharmacovigilance signal detection

Business question
Which drug–event combinations are reported unusually often?
Method
Disproportionality analysis (PRR, ROR and others) computed by deterministic code, with DH S1 helping to read and classify report text.
Deliverable
A list of candidate signals with traceable calculations, for evaluation by pharmacovigilance staff.
04 · Supply

Demand and near-expiry forecasting

Business question
How much stock does each region and SKU need, and which batches are at risk of expiring?
Method
Hierarchical demand forecasts and inventory risk scores, deployed and refreshed on DH S1.
Deliverable
Demand forecasts, replenishment suggestions and a near-expiry watch list.
05 · Clinical

Clinical operations analytics

Business question
Which sites are enrolling slowly, and which participants risk dropping out?
Method
Enrollment-rate forecasts and dropout risk scores from site and visit data.
Deliverable
Site rankings and a watch list for clinical operations teams.
06 · Research

Real-world data analysis

Business question
How do treatment outcomes differ across populations in routine care?
Method
Cohort description, propensity-score matching and other statistical analysis, with every step recorded in the run manifest.
Deliverable
A replayable analysis report and full run manifest.
Researchers in cleanroom suits, goggles and masks handle test tubes at a lab bench
Auditable and replayable

Every analysis comes with an immutable run manifest

Each SAM release produces a run manifest binding the data snapshot, code version, parameter set, gate sign-offs, model package and monitoring plan into one release unit. For inspections or audits, the whole analysis can be replayed from the manifest — which helps meet computerized-system and data-integrity requirements.

  • Data snapshots and code versions locked one by one
  • Every key step signed by a named person, with timestamps
  • Computation stays inside a controlled sandbox — data and artifacts never leave
How the platforms work together

Four platforms, each with a job in pharmaceuticals

Where variables come from: time + product + status + value + statistic

VDC’s derivation method works just as well on pharmaceutical data. For a distribution-customer credit model, one variable combines five dimensions:

TimeLast 180 days
ProductPrescription SKUs
StatusReceivables due
ValueDays overdue
StatisticMaximum
Typical dataSales flowPurchasing & paymentsInventory & batch expiryExpense claims & invoicesMeeting & education recordsAdverse-event reportsClinical site & visit data
Compliance & data security

Data stays in-house. Conclusions stay traceable.

The sensitivity of pharmaceutical data and the regulatory requirements around it mean an analytics platform must put security and traceability first.

  • On-premises: all data and models stay in your own environment; modeling runs in a controlled sandbox and data and artifacts never leave it.
  • Sensitive personal information: under China’s Personal Information Protection Law, health information is sensitive personal information — processing requires a specific purpose, sufficient necessity and separate consent where required; modeling prefers de-identified data.
  • Pharmacovigilance: data, methods and conclusions of signal detection are fully recorded for evaluation by pharmacovigilance staff under Good Pharmacovigilance Practice.
  • Data integrity: run manifests and tamper-proof audit logs support inspections against the computerized-system requirements of Good Manufacturing Practice.
  • People decide: models provide scores and candidate lists; medical, compliance and business decisions are made by named professionals.
Try online

Every SAM feature, online, for up to 10,000 samples

Upload up to 10,000 records and run the whole fourteen-step pipeline — data contract to packaged scorecard, human gates included — to see for yourself whether SAM fits your business.

Up to 10,000 samplesEvery feature enabledThe full fourteen-step pipeline
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Get started

Start with one business question. Let SAM deliver a model you can sign off.

Whether you are in banking, FMCG and marketing, pharmaceuticals or another industry, try SAM online first — or talk to our modeling experts about your use case.