FDB (First Databank) vs MedAware (2026)
This is the clearest statement in the index of the argument between rules and learned models in medication safety, and both sides are represented by a vendor that means it. FDB supplies the drug knowledge sitting under most prescribing systems in the United States, which means most interaction and dose alerts a clinician has ever dismissed were built on its content. MedAware was founded after a child died because a physician picked the wrong drug from a dropdown, an error no interaction rule could catch, and it detects prescriptions that do not fit the patient rather than pairs that do not fit each other. The honest framing is that these are layers, not alternatives. The rules catch the known hazard and generate the override fatigue; the outlier model catches what the rules cannot express and has to earn its alerts against an already exhausted prescriber.
- The drug knowledge underneath most American prescribing systems is this company's content, so its interaction and dose warnings are the baseline against which any additional layer is measured.
- The licensing architecture is clean by design: content is embedded in the customer's own systems, which places hosting, residency and processing questions with the customer rather than with the supplier.
- Its own newer products are framed conservatively, with a prescription agent producing orders explicitly for physician review rather than acting.
- It exists because rules missed the error that killed a child, and it detects prescribing outliers a rule library cannot express, using unsupervised outlier detection over longitudinal patient data.
- The evidence was produced mainly by investigators who do not work for the company, including a Harvard Medical School group, which is rare in this category and rarer still for a machine learning product.
- Integration is named and corroborated by customers rather than asserted, with deployments confirmed inside a health system pharmacy workflow and in ambulatory practice.
This comparison is published by AI Health Index, an independent research platform that compares healthcare AI vendors objectively. FDB (First Databank) and MedAware are each graded against the same capability taxonomy, from each vendor's own public materials and the regulatory record, under the AI Health Index verification standard. No vendor pays for placement, and no vendor has reviewed this page. How this evidence is graded
Plain facts
| Fact | FDB (First Databank) | MedAware |
|---|---|---|
| Primary category | Medication Safety & Prescribing | Medication Safety & Prescribing |
| Founded | Not recorded | 2012 |
| Headquarters | South San Francisco, California, United States | Ra'anana, Israel |
| Website | fdbhealth.com | medaware.com |
Side by Side
Each record in one paragraph
Written to be quoted whole. Each paragraph states what the AI Health Index verified about the vendor, with the caveats attached. Generated from this pair’s live capability grades, so it moves when a grade moves.
The AI Health Index awards FDB (First Databank) its top capability grade on EHR and Interoperability Depth. Set against MedAware, FDB (First Databank) grades higher on EHR and Interoperability Depth. Its thinnest published disclosure sits on several axes, including AI Centrality, Model Supply Chain Disclosure and HIPAA and BAA Posture. Grades reflect evidence the AI Health Index could verify at the last review, so a low grade records disclosure the vendor has not published rather than a capability it has been shown to lack.
Source: AI Health Index, August 2026
The AI Health Index awards MedAware its top capability grade on AI Centrality. Set against FDB (First Databank), MedAware grades higher on AI Centrality and Clinical and Operational Evidence. Its thinnest published disclosure sits on several axes, including Model Supply Chain Disclosure, HIPAA and BAA Posture and Security Certifications and Trust Center. Grades reflect evidence the AI Health Index could verify at the last review, so a low grade records disclosure the vendor has not published rather than a capability it has been shown to lack.
Source: AI Health Index, August 2026
Questions buyers ask
Should we choose FDB (First Databank) or MedAware?
On the axes where the AI Health Index separates them, FDB (First Databank) grades higher on EHR and Interoperability Depth, and MedAware grades higher on AI Centrality and Clinical and Operational Evidence. MedAware leads on the greater share of scored axes, but the split means the decision turns on which constraint is binding rather than on an overall winner.
Where do FDB (First Databank) and MedAware differ most?
The widest separation the AI Health Index records between FDB (First Databank) and MedAware is on AI Centrality, where FDB (First Databank) grades D and MedAware grades A. That axis sits in the AI Capability group, so it should carry the most weight for a buyer whose binding constraint is how much of the work the model itself is trusted to do.
Where do FDB (First Databank) and MedAware grade the same?
The AI Health Index grades FDB (First Databank) and MedAware the same on several axes, including Autonomy and Oversight Model, Model and Technology Transparency and Model Supply Chain Disclosure. Neither holds an advantage the index can evidence on those axes, so they should not carry weight in a selection between these two.
What have FDB (First Databank) and MedAware not disclosed?
At the last review, at least one of FDB (First Databank) and MedAware published thin or absent detail on several axes, including AI Centrality, Model Supply Chain Disclosure and HIPAA and BAA Posture. The AI Health Index treats an absent disclosure as a gap in the public record rather than a failure of the product, so these are the axes to get in writing during diligence instead of inferring from the grade.
Related comparisons
Other published head to head assessments involving these vendors or their closest peers. The full set for this category is on the Clinical Decision Support page.
These are complementary layers more often than substitutes: rule based checking catches the known interaction, outlier detection catches the prescription that does not fit the patient, and a hospital running both will still see the majority of its alerts from the rules. Neither vendor publishes a security attestation, trust centre or business associate agreement terms.
MedAware publishes no subgroup performance by age, sex, race, ethnicity, insurance status or site, which is the specific gap for an outlier detection model, since what counts as anomalous varies with the population the model learned from. Neither publishes pricing.