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Custom AI foundries are an emerging architecture for building and operating multiple healthcare AI capabilities on governed, reusable data and integration layers. They can let a hospital add models, agents, and workflow applications without replacing its electronic health record (EHR) or every departmental system. “Decoupling” means separating innovation from the pace and constraints of legacy infrastructure; it does not mean separating AI from clinical governance, data rights, workflow accountability, or patient safety.
What a custom AI foundry is—and is not
The term custom AI foundry is a strategic metaphor, not a settled technical standard or a universally available product category. McKinsey’s November 2025 analysis describes a possible move away from dozens of disconnected healthcare AI point solutions toward a modular architecture that links data foundations, models, intelligent agents, interoperability, and applications.
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In a foundry model, the organization curates clinical and operational data once, applies consistent controls, and makes that data usable for several approved models and use cases. A radiology assistant, a bed-capacity forecast, a revenue-cycle agent, and a research cohort tool can then share governed services instead of each project creating its own extraction, interface, and audit process.
This is different from simply moving an AI application to a cloud. The difficult work is making data usable and trustworthy, connecting outputs to existing systems, recording which model produced an answer, and ensuring a qualified person remains accountable for decisions. McKinsey’s authors—Aneesh Krishna, David Friend, Neeraj Gohad, and Prashanth Reddy—write that “the real battleground will be who controls the data and orchestration layers.” That is a strategic observation, not an industry-wide certification or technical specification.
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Why hospitals want the architecture
Point solutions can solve an immediate problem, but they often create parallel data feeds, separate user interfaces, duplicated security reviews, and incompatible model-monitoring practices. When a hospital buys or builds many tools independently, it may not know which version of a model is active, what data it used, or where a generated recommendation was displayed.
A foundry approach aims to make the reusable layers explicit:
- Shared data services: governed clinical, imaging, laboratory, operational, and financial data that can be discovered and reused.
- Reusable model access: a controlled way to use approved internal models, external models, and task-specific agents.
- Interoperability: connectors and standards that move context between the EHR, departmental systems, data platforms, and applications.
- Orchestration: rules that decide which model runs, with which inputs, where an output goes, and when a human must review it.
- Monitoring and accountability: versioning, audit trails, performance checks, incident handling, and ownership.
The objective is not to make the hospital’s core systems irrelevant. It is to keep innovation work from being blocked by a full replacement project while still fitting the systems that hold the official record and run care delivery.
The layers of a healthcare AI foundry
1. Governed data foundation
Start with data that has a defined owner, permitted uses, quality rules, and a documented path from source to application. This may include structured EHR fields, clinical notes, images, waveforms, laboratory results, scheduling data, supply data, and claims information. A catalog should identify the dataset, its provenance, refresh frequency, sensitivity, and allowed consumers.
Data rights are part of the architecture. The hospital needs policies for patient permissions, secondary use, research access, retention, sharing with suppliers, and deletion or correction. A technically elegant platform cannot cure an unclear legal or ethical basis for using the data.
2. Models and agents
The next layer provides access to models without allowing every application to call an unreviewed endpoint. It can include internally trained models, commercially supplied models, and task-specific agents. Each entry should have an owner, intended use, input requirements, output limitations, version, validation evidence, and retirement plan.
Model access should be separable from the user interface. If a model changes, the organization should be able to test the replacement and update the registry without rebuilding every workflow that consumes it.
3. Integration and interoperability
An integration layer translates between model services and hospital systems. Coforix describes an approach using FHIR, SNOMED CT, and ICD-10, with a single API and SaaS or customer-cloud deployment options. Those are the company’s stated capabilities, not an independent audit.
In practice, the layer must handle identity matching, terminology mapping, consent flags, event triggers, error handling, and write-back rules. A generated summary that cannot be associated with the correct patient, encounter, author, and timestamp is not ready for clinical use.
4. Orchestration and workflow applications
Orchestration places AI in a real process. It can route a new result to a model, apply confidence or eligibility rules, send a draft to a clinician, request a second model, or stop automation when required data is missing. The application layer then presents the result in a context where the responsible user can accept, edit, reject, or escalate it.
Workflow design determines whether an AI capability saves time or creates another inbox. The architecture should specify who sees an alert, how quickly it must be reviewed, what happens when nobody responds, and where the final decision is recorded.
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5. Observability and control
Every production workflow needs logs for input lineage, model and prompt versions where applicable, output delivery, user actions, overrides, and failures. Monitoring should look for drift in data, changes in performance across patient groups, latency, unexpected volume, and unsafe or unusable outputs. A foundry without observability is only a collection of opaque services.
How to build one without replacing the EHR
- Choose a high-value, bounded use case. Select a problem with a measurable baseline, a willing operational owner, and a workflow where a human can review the result. McKinsey recommends proving value with high-value use cases before scaling.
- Map the current process. Document where data originates, who edits it, which system is authoritative, how work is queued, and where delays or errors occur. Include exceptions, downtime procedures, and handoffs between departments.
- Define the decision boundary. State exactly what the model may recommend, what it may never decide, who approves an action, and how disagreement is handled. For a clinical use case, the accountable clinician and institutional policy remain in charge.
- Inventory and classify the data. Record fields, formats, quality, provenance, sensitivity, retention, and permitted use. Resolve identity and terminology issues before model development rather than hiding them in application code.
- Create governance before production access. Establish a multidisciplinary review group with clinical, informatics, privacy, security, legal, compliance, data-science, and operational representation. Assign a named owner for each dataset, model, and workflow.
- Select the deployment pattern. Decide whether data stays inside an institution-controlled environment, moves to a cloud service, or uses a hybrid or federated design. Base the choice on latency, security, data-use terms, local skills, resilience, and cost—not on the word “cloud” alone.
- Build the smallest reusable interface. Use existing standards and APIs where possible. Define authentication, authorization, terminology mapping, patient-context propagation, audit events, retries, and write-back behavior. Keep the model endpoint replaceable.
- Validate in silent or supervised mode. Run the system without changing care, or require explicit review, while measuring accuracy, coverage, latency, workload, and subgroup behavior. Compare against the current process and document exclusions.
- Release with safeguards. Provide clear status labels, confidence or uncertainty information where meaningful, escalation paths, downtime instructions, and a way to report unsafe or incorrect outputs. Train the users who must review the result.
- Monitor and expand deliberately. Review performance and incidents at a defined cadence. Add a second use case only when the data, integration, and governance services are genuinely reusable; do not scale a failing workflow because the platform exists.
Governance controls that make decoupling safe
A hospital can separate the innovation team from legacy release cycles, but it cannot separate the deployed system from institutional accountability. At minimum, governance should cover:
- Data rights and sharing: permitted purposes, patient and partner restrictions, retention, deletion, and cross-border or cross-entity transfers.
- Model ownership: a responsible person or committee for intended use, validation, updates, incident response, and retirement.
- Traceability: dataset versions, model versions, prompts or configuration where relevant, input lineage, output delivery, and user actions.
- Risk classification: different approval and monitoring requirements for administrative assistance, clinical decision support, triage, diagnosis, treatment, and autonomous action.
- Human oversight: the reviewer’s role, required response time, override process, and documentation of the final decision.
- Security: least-privilege access, secrets management, network controls, vulnerability management, and tested recovery procedures.
- Equity and performance: subgroup evaluation, drift detection, and a process for pausing a model when behavior changes.
Comparing foundry approaches
No single architecture is established as the universal winner. Compare an option against the hospital’s risk, data, and workflow requirements:
| Criterion | Questions to ask | Evidence to require |
|---|---|---|
| Data control and location | Does data remain in an institution-controlled environment, move to a provider cloud, or use a hybrid or federated arrangement? | Data-flow diagrams, tenancy model, access controls, retention terms, and deletion process. |
| Interoperability | How are EHR, imaging, laboratory, and operational systems connected? | Supported standards, terminology mappings, identity handling, error behavior, and write-back examples. |
| Model choice | Can the hospital combine internal, external, and multiple-vendor models? | Registry functions, version controls, substitution tests, and documented limitations. Treat “open” claims as vendor assertions until verified. |
| Governance | Can the platform show ownership, approvals, lineage, audit history, and monitoring? | Sample audit records, role definitions, incident workflow, and performance dashboards. |
| Workflow fit | Does the output appear where work is done, with an accountable human in the loop? | Usability testing, escalation behavior, downtime procedure, and evidence from the target workflow. |
| Operational resilience | What happens when a model, network, or data source is unavailable? | Service objectives, fallback behavior, recovery testing, and support responsibilities. |
What current examples demonstrate
McKinsey’s modular architecture thesis
McKinsey’s November 2025 article argues that the proliferation of point solutions is creating fragmentation and proposes connected layers, including clinical-data foundries, as a direction for healthcare organizations to prepare for. It is strategic analysis and a set of recommendations, not proof that a standardized foundry product or operating model already exists.
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In a July 25, 2024 announcement, GE HealthCare said it had selected AWS as a strategic cloud provider and planned to use AWS services to develop healthcare foundation models and generative AI applications. The announcement demonstrates a partnership strategy and intended future work. It does not establish that every planned capability has been completed or that clinical benefits have been independently demonstrated. The same announcement described a research-stage imaging tool as still in development and not cleared or approved for commercial availability.
Bavel Health
Bavel Health describes a sovereign clinical data platform containing data infrastructure, a virtual research room, a federated-learning layer, and a clinical AI fabric. The company says the platform operates inside hospital environments and that AI workloads move to the data. These statements illustrate an institution-controlled or federated design; they are company descriptions rather than independent verification of clinical outcomes.
Coforix
Coforix describes an integration platform for models and agents across hospitals and vendors. Its platform page lists FHIR, SNOMED CT, and ICD-10, offers SaaS or customer-cloud deployment, and says patient data is not used to train its models. These are vendor-stated product claims that a procurement team should validate in contract, architecture review, and technical testing.
deepc
deepc presents a single integration and governance foundation for AI workflows across health systems. It is a useful example of the infrastructure-and-orchestration category. The cited company page does not establish independent performance results, so buyers should request workflow evidence, monitoring details, and references relevant to their own setting.
How to measure whether the foundry is working
Platform adoption alone is not a benefit. Establish measures at three levels:
- Technical: uptime, latency, failed transactions, data freshness, interface errors, recovery time, and percentage of model calls with complete audit records.
- Operational: turnaround time, worklist volume, avoided rework, staff time, override rates, escalation rates, and adoption by the intended users.
- Clinical or organizational: the outcome the use case was meant to improve, such as earlier review, fewer delays, safer handoffs, or better resource planning. Use an appropriate comparison period or control where feasible.
Measure unintended effects as well: alert fatigue, inequitable performance, new documentation burden, privacy incidents, and work shifted from one team to another. A model that is accurate in a test set but ignored in practice has not delivered its intended value.
Common failure modes and practical fixes
Calling a disconnected collection of tools a foundry
If every application has its own data pipeline, identity logic, approval process, and monitoring, the organization has a portfolio of point solutions. Define reusable services and require new projects to use them unless an exception is approved.
Assuming “decoupled” means “outside the hospital”
External hosting may be appropriate, but the system still needs the hospital’s patient context, policies, records, and accountable users. Draw the complete data and decision path before selecting a location.
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Unassigned datasets become bottlenecks during incidents and contract reviews. Name owners and stewards, publish access rules, and record provenance in the catalog.
Automating before the workflow is ready
An alert inserted into an overloaded inbox is not an intervention. Pilot with the people who receive and act on the output, set response expectations, and provide a safe fallback.
Treating vendor language as outcome evidence
Product pages can explain an architecture, but they do not replace independent validation. Separate capability claims, contractual commitments, local acceptance testing, and measured patient or operational outcomes.
When a custom AI foundry is the right choice
The approach is most defensible when a hospital expects several AI use cases, has enough data and engineering capacity to create shared services, and is willing to operate governance as a permanent function. A single low-risk workflow may be better served by a contained application with a well-defined interface. The foundry investment pays off when reuse, traceability, and controlled model choice matter more than the speed of buying one isolated tool.
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Random freezes, missing sound and display glitches usually trace back to one bad driver. Find and replace yours safely.Free scan · under a minuteHospitals can therefore decouple the pace of innovation from legacy-system replacement, but not from the responsibilities that make healthcare trustworthy. The practical test is whether each new capability becomes easier to integrate, easier to audit, and safer to govern than the one before it.
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