Hospital Data Governance & Medical AI Project Inquiry

Medical data projects: from governance to verifiable delivery

For hospitals, research teams and medical AI developers. We start by defining the task, data boundaries, quality rules and deliverables for data integration, research datasets, and model training or evaluation—then assess ingestion, annotation and collaboration options.

Multimodal data governance

Map CT, MR, radiography, ultrasound, pathology slides and report data; define field mappings and checks for missing, duplicate or linked records. PACS, HIS, EMR and CDR interfaces, image routing and in-hospital platforms require a separate review of the local environment.

Research and cohort data preparation

For disease-specific studies, longitudinal records, cohorts or eCRF requirements, define inclusion criteria, clinical variables, time points, structured fields and quality rules. A full EDC or research-management system, and integration with expense or finance platforms, require separately scoped work.

AI training and evaluation data

Translate model questions into case strata, annotation guidance, pilot calibration, expert review, blinded or agreement assessment, and sampling plans. Modalities and model applicability are confirmed against agreed examples.

Delivery and acceptance

Agree on data provenance and permissions, de-identification scope, least-necessary access, version traceability, export formats, quality reports and sampling-based acceptance. If data must remain on premises or deployment must be private, confirm architecture, operations and responsibilities first.

A practical project path

Define goals and use

Review data and permissions

Set standards; run a pilot

Expert review and QA

Accept and deliver to spec

Bring the project goal, data types, intended use, deployment constraints and acceptance criteria. We can first map the scope and a validation checklist.

Submit purchase brief

Within one business day: data-card field notes or a trial-delivery discussion. Do not submit identifiable patient data.

After submit you will receive

Inquiry ID (traceable)
Data-card fields / trial-delivery notes (by interest)
NDA and next scheduling when needed

What makes a deliverable verifiable

Agree the content, access level and timing of these materials before delivery.

Task and counting

Define modality, inclusion rules, deduplication keys and units. Cases, studies, series, slices and annotations are distinct.

Source and permissions

Check provenance, lawful processing basis, training use, retention and redistribution. De-identification does not replace authorization.

Samples and distributions

Review samples, fields, strata and missingness. Sample approval is not batch acceptance.

Expertise and quality

Specify expert fit, review, sample denominators, disagreement resolution, rework and acceptance records.

Files and versions

Agree paths, checksums, versions, format checks and image–text pairing. Settle against accepted, deduplicated units.

Evaluation separation

Freeze tasks and rubrics; agree access, contamination checks, retest conditions and role disclosure.