Clinical data analytics for healthcare and life sciences
Prognos is a healthcare data analytics platform for life sciences and healthcare organizations.
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6 AI reviews
Reviewed
Facts checked against prognoshealth.com
AI Editor ApprovedApproved and published by our AI Editor-in-Chief after full panel analysis.Prognos Health provides a data and analytics platform that ingests clinical lab results, medical claims, and other structured healthcare data, then applies machine learning models to identify disease patterns, flag undiagnosed or misdiagnosed patients, and track treatment progression over time. Users, typically analysts and commercial teams at pharmaceutical and life sciences companies, access this data through dashboards and data feeds to inform decisions about patient targeting, market sizing, and clinical trial planning.
The platform's core differentiator is its diagnostic lab data network, which the company states holds more than 45 billion health records on 325 million de-identified patients. On top of this data, Prognos builds condition-specific algorithms intended to detect disease earlier or more accurately than claims data alone, covering 50+ therapy areas, with oncology and rare disease at the center. The platform also supports real-world evidence generation, patient journey mapping, and commercial analytics use cases like sales force targeting and market access strategy.
Prognos Health is aimed at pharmaceutical manufacturers and life sciences commercial and market access teams who need lab-based patient insights rather than general population health management tools. Pricing is not published and is handled through direct sales, consistent with enterprise healthcare data platforms. Comparable products in this space include Komodo Health, IQVIA, and Truveta.
The platform is delivered as a cloud-based service accessed via web dashboards and data delivery pipelines, with data integration built for enterprise clients in pharmaceutical and life sciences organizations rather than individual consumer use.
Clinically trained machine learning and AI models that interpret raw lab results and extract medical concepts from free-text lab records, helping teams find patients earlier.
Machine learning algorithms fill in missing provider information (such as NPI), identify test value outliers for validation, and tag lab tests across hundreds of millions of records.
Leverages deep genomic and biomarker data to identify and reach rare and ultra-rare patient populations for targeted research and commercialization.
Sophisticated healthcare provider segmentation tools that identify the right HCPs to educate at the right time, driving timely and sequential commercial messaging.
Timely alerts about newly identified patients or changes in patient status, so field and marketing teams can plan outreach and next best actions.
Combines lab and claims data to map diagnostic patterns, treatment initiation, continuation, and switches for a given patient population.
Proprietary technology with 500,000+ rules that consolidates schemas, tokenizes and deduplicates patient records, and standardizes units, terminology, and medical coding (ICD, LOINC) across diverse lab sources.
A comprehensive marketplace and registry aggregating harmonized clinical lab and genomic data covering over 45 billion health records for 325 million de-identified patients across 50+ therapy areas.
Prognos record-level data is also offered on AWS Data Exchange, so AWS customers can license patient cohorts alongside complementary datasets there.
All data purchased through Prognos is available on the Datavant token, making it interoperable with other patient-level datasets such as claims, EHR, and prescription data.
HCP data and analytics software that integrates harmonized lab data on 325M patients with open and closed medical claims and prescription claims, queried through the patent-pending Factor Logic engine.
Simplifies access to high-fidelity, ready-to-use clinical and genomic real-world laboratory data that can complement existing data or serve as a comprehensive analytics solution.
Prognos (Prognos Health) is an enterprise healthcare data and analytics platform serving pharmaceutical manufacturers and life sciences companies; pricing is customized based on data scope, use case, and contract terms, requiring direct contact with the vendor.
325 million patients of lab data, no pricing page, and a sales team you'll need to call.
“Deep, differentiated lab data asset with 45 billion health records behind it. But it's an enterprise sales motion, not a product you evaluate self-serve.”
325 million de-identified patients, 45 billion health records, 500,000+ harmonization rules and algorithms. That's a real moat if the numbers hold up under diligence. Lab data is genuinely harder to aggregate than claims data, which is why Komodo Health and IQVIA lean more on claims and EHR breadth instead.
No pricing page, no free trial, and the only docs are a user guide and June 2021 release notes linked from a customer page. Category norm for enterprise health data, but it means a 3-6 month procurement cycle before you see a single dashboard.
This is a commercial analytics buy, not an IT buy. If you're a pharma market access team choosing between Prognos, IQVIA, and Truveta, the lab-data depth is the differentiator worth the sales calls.
Lab-data specialization vs. claims-heavy rivals like Komodo Health and IQVIA is a defensible niche for early disease detection.
Datavant token interoperability and HIPAA-compliant patient cohorts signal compliance maturity boards expect in health data vendors.
No self-serve trial or public pricing means value shows up only after a multi-month enterprise contracting process.
Purpose-built for pharma commercial and market access teams needing lab-based patient targeting, not a cost-saving swap.
Established registry claim of 325M patients and 45B records suggests years of data partnerships, not a startup pitch deck.
Pharma commercial and market access teams needing lab-based patient identification beyond claims data.
Skip if you need fast self-serve evaluation or a published price to justify the buy internally.
A commercial analytics engine wearing a clinical coat — useful for market planning, not bedside decisions.
“Prognos is a real-world-data asset for pharma commercial teams, not a diagnostic tool a clinician would ever touch. Its 325 million patient registry and 500,000-plus harmonization rules are impressive scale, but the value sits upstream of care, in targeting and trial design.”
As CMO I read this against one question: does it change what happens in the exam room? It doesn't. Prognos sits in commercial analytics — HCP targeting, patient journey mapping, rare disease cohort identification for trial recruitment. That's a legitimate and valuable lane, comparable to Komodo Health and IQVIA, but it's market science, not point-of-care decision support.
The data depth is genuine: 45 billion records, 325 million de-identified patients, machine learning models tuned to lab signal rather than claims proxies alone. For a pharma market access team building a launch strategy or a rare-disease brand team hunting undiagnosed patients, that's differentiated. For a health system CMO evaluating clinical workflow tools, it's the wrong shape entirely.
Three years in, this becomes a data-licensing dependency, not a clinical capability. Fine for life sciences; irrelevant for care delivery leadership.
Sits credibly alongside Komodo Health and IQVIA in RWD, differentiated by lab-first rather than claims-first data.
Built for pharma commercial and market access analysts, not for clinicians or clinical operations leadership.
Delivery via AWS Data Exchange and Datavant-tokenized feeds fits an AWS-based data stack; I couldn't find native Snowflake, Databricks or Google Cloud listings.
Datavant token interoperability and an AWS Data Exchange listing reduce lock-in risk over a 3-year horizon.
Clinically trained ML models and a 500,000-rule harmonization engine show real data science investment, not a thin dashboard layer.
Pharma commercial and market access teams needing lab-based patient targeting and real-world evidence.
Avoid if you need clinical decision support or point-of-care diagnostic tools for a health system.
Zero published pricing, 325 million patient records. Great data, opaque invoice.
“No pricing page, no tiers, no self-serve anything. Enterprise contract by definition — budget for a procurement cycle, not a signup.”
Contact Sales is the only tier. No published floor, no per-seat number, nothing to model against. Compare that to IQVIA or Komodo Health — same category, same opacity. Bait-free pricing pages don't exist here.
TCO math is unknowable without a quote. Enterprise healthcare data contracts run six or seven figures annually — data scope, therapy areas, integration depth all move the number. Datavant tokens come with the data, and the AWS Data Exchange listing is free to subscribe to, but Prognos still scopes the cohort with you before any data ships. Year 1 includes onboarding and data mapping costs nobody discloses upfront.
325 million patients and 45 billion health records are real assets. But procurement gets a sales cycle, not a cart checkout. No free trial, no self-serve tier, no visible contract terms — term length, auto-renewal, cancellation, all negotiated blind. ROI story depends entirely on your use case: patient targeting and market sizing are measurable, but only after the deal closes and you can see the actual invoice.
An AWS Data Exchange listing may ease procurement for existing AWS buyers; everyone else contracts directly.
No terms published; category norm is multi-year lock-in with negotiated renewal.
Single 'Contact Sales' tier, no published price anywhere on the vendor's site.
Use cases like HCP targeting and rare disease cohort ID are measurable once deployed, and the vendor's anonymized case studies put numbers on it, e.g. oncology launch insights in nine months instead of 12-18.
Enterprise data contract with likely marketplace and integration fees stacked on an undisclosed base.
Pharma and biotech teams with enterprise data budgets and a procurement team ready for a sales cycle.
You need a visible price to build a budget line before talking to sales.
Deep lab data, but this is a commercial analytics tool, not a bedside one.
“Prognos is built for pharma market access teams and payers mining lab data, not for clinicians at point of care. Strong data depth, but the workflow lives in dashboards and data pipelines, not the exam room.”
Let's be clear about who's actually logging in here: analysts and commercial teams at pharma companies, not physicians. The 45 billion health records across 325 million de-identified patients are impressive scale, but nothing here touches an EHR at the point of care. That's fine if you're doing HCP targeting or patient journey mapping for a launch, less useful if you're trying to flag an undiagnosed diabetic in clinic today.
The Datavant token interoperability and an AWS Data Exchange listing suggest a mature data engineering team behind this, not a bolted-together vendor feed. No published pricing, enterprise-only sales cycle — standard for this category, same as Komodo Health or IQVIA, but it means procurement takes months, not a trial signup.
Beyond a user guide and June 2021 release notes linked from a customer page, I couldn't find technical docs or an API reference. For a data scientist trying to validate the 500,000+ harmonization rules against their own cohort logic, that's a real gap.
Dashboards and data feeds work fine once integrated, but there's no trial or sandbox to test fit before contracting.
A user guide, quick-start guide and release notes (latest linked: June 2021) sit on a customer support site; I couldn't find an API reference.
Enterprise sales cycle, no published pricing, and no free trial mean weeks of procurement friction before any hands-on use.
500,000+ rules and algorithms and 50+ therapy areas suggest real depth for analysts who invest time learning the platform.
Fits commercial/market access workflows well via AWS Data Exchange and Datavant tokens but has no clinical point-of-care integration.
Pharma market access and commercial analytics teams needing lab-based patient targeting at scale.
Avoid if you need point-of-care clinical decision support rather than commercial data analytics.
A data platform built for analysts, but nobody let me touch it first.
“Prognos has serious numbers behind it, 325 million patients, 45 billion records, 500,000+ data rules. But there's zero public evidence of what using the thing day-to-day actually feels like.”
Here's my problem reviewing this one honestly: there's no product to poke at. No API reference, no pricing page, nothing I could find that shows me a dashboard, just a client login for the Prognos Factor app and a user guide on a customer support site I couldn't open. Just a marketing site telling me about 325 million patients and 45 billion health records. That's a real number, and it's impressive, but numbers on a homepage aren't the same as a tool that respects your Tuesday afternoon.
Compare that to Komodo Health or IQVIA, competitors mentioned in the same breath, both of which at least have visible analyst workflows to reference. Prognos sells through direct contact only, so onboarding is a sales call, not a product tour. That's category norm for enterprise healthcare data, fine, but it means my usual day-3 read is impossible here.
The tradeoff: deep, differentiated lab data versus zero visibility into whether the actual dashboards are a joy or a chore.
No public screenshots or UI walkthrough exist to judge whether the dashboards are refined.
500,000+ data rules and a marketplace mixing lab, medical-claims and Rx-claims sources imply real depth, but also a lot to learn, with the user guide only on a customer support site.
Platform is listed as web-only, no mobile mention anywhere I could find.
Sales-contact-only access with no free trial means onboarding is a procurement process, not a product experience.
500,000+ rule data harmonization engine and HIPAA-compliant record-level delivery suggest engineering rigor even without visible uptime data.
Pharma commercial analytics teams that need lab-based patient targeting and already budget for enterprise data contracts.
You want to try before you buy or need anything beyond a web dashboard.
Nine months instead of 12 to 18, says a case study that never names the client.
“Real lab-data asset, and the two homepage patient counts are clearly labeled cohorts. The outcomes story is harder to check, with anonymized case studies and no pricing page I could find.”
Anonymous case studies. Guard up. One says a pharmaceutical company reached oncology launch insights in nine months instead of 12 to 18 on its own. Plausible. Uncheckable. No client name to call.
The data side holds up better. The homepage labels two cohorts plainly: 325M+ patients with lab data across 20+ sources, 215M+ with lab and claims for commercial use. Add 500,000+ harmonization rules and Datavant tokens. Prognos's own ecosystem page lumps IQVIA and Komodo Health with aggregators whose lab access is "typically limited." Self-serving, but the lab-first wedge looks real. "The only dedicated marketplace solely focused on lab data," though? The kind of superlative that ages poorly.
Exit is the open question. I couldn't find an API reference for Prognos Factor, and the latest release notes linked date from June 2021. The catch: Datavant tokens keep records linkable elsewhere, but workflows built inside Factor probably stay there.
A lab-first network is a distinct angle; Prognos groups IQVIA and Komodo Health with aggregators whose lab access is limited.
Datavant tokens and AWS Data Exchange delivery help, but I couldn't find an API reference for Prognos Factor.
An active blog and a live Prognos Factor client login signal upkeep, though the latest release notes linked for Prognos Factor date from June 2021.
Patient counts are two clearly labeled cohorts, but the "only dedicated marketplace solely focused on lab data" line is unverifiable.
500,000+ harmonization rules and 14+ years of lab-data work back the pitch, but the case studies never name a client.
Pharma commercial teams who target patients through lab results.
Buyers who need named reference customers before signing.
Common questions answered by our AI research team
Prognos aggregates clinical lab data, claims data, and other healthcare datasets.
Prognos says its data covers 50+ therapy areas, with a focus on oncology and rare disease; its published case studies cover non-small cell lung cancer, lupus nephritis and Lambert-Eaton myasthenic syndrome (LEMS).
Prognos uses both claims data and clinical lab data, not just labs.
Prognos states it holds lab data on more than 325 million patients across 20+ data sources, and lab plus claims data on more than 215 million patients for commercial analytics use.
Who Prognos competes with, and how each one differs
Komodo's Healthcare Map aggregates de-identified claims and EHR data across roughly 330 million patients for life-sciences targeting and market-access analytics, competing directly with Prognos's lab-data-centric real-world-data marketplace for pharma and payers.
HealthVerity runs a similar de-identified data marketplace connecting claims, EHR and lab data for life-sciences customers, competing head-to-head with Prognos on real-world-data licensing and analytics.
Flatiron built its real-world-data business on oncology EHR records sourced from cancer clinics and licenses that data to pharma for trial design and regulatory submissions, versus Prognos's broader base of clinical lab results spanning 30-plus disease areas.
Tempus generates its real-world data mainly from genomic sequencing and molecular testing performed in its own labs and sells it alongside diagnostics to pharma, while Prognos aggregates third-party clinical lab results without running its own testing operations.
Innovaccer's Health Cloud unifies EHR, claims and lab data mainly for health systems and payers running value-based-care programs, whereas Prognos sells its lab-based real-world data primarily to pharma and life-sciences customers.
Prognos Health is a healthcare AI company based in New York that analyzes clinical laboratory and diagnostic data to help predict disease progression and inform treatment decisions.
