01 / BUILD
Patient Cohort Builder
Generate condition-aware populations with configurable demographics, acuity, diagnoses, labs, medications, edge cases, expected outcomes, and replayable scenario identities.
Executable synthetic patients for healthcare system validation. Build a clinically coherent cohort, run the complete patient journey through your environment, and receive reproducible evidence of what passed, what failed, and why.
Test the whole clinical journey before a real patient enters it.
MediFlow combines five connected capabilities into one validation system. The result is not another static dataset. It is a controlled patient story, executed against your environment, with an answer key.
Generate condition-aware populations with configurable demographics, acuity, diagnoses, labs, medications, edge cases, expected outcomes, and replayable scenario identities.
Turn each patient into a longitudinal story: registration, admission, orders, results, deterioration, intervention, recovery, discharge, and claim.
Transmit through HL7 v2, FHIR R4 REST and Bundles, C-CDA, CSV, JSON, MLLP, HTTP, SFTP, and customer-specific interface profiles.
Check acceptance, patient identity, encounter linkage, critical alerts, idempotency, downstream state, timing, retries, and duplicate handling.
Deliver expected-versus-actual results, request and response hashes, ACK history, throughput, assertion results, and a signed reproducible run manifest.
MediFlow starts with the workflow a hospital needs to test, not with a copy of a real chart. It constructs fictional patients whose demographics, diagnoses, encounters, observations, medications, and outcomes remain clinically coherent across the complete journey.
A hospital does not simply need 500 fake names. It may need diabetic admissions that trigger a specific rule, a sepsis cohort with progressive lactate results, duplicate-MRN cases for identity testing, or denied claims with known correction paths. MediFlow turns that requirement into an executable cohort with expected answers.
The same cohort can be replayed during an EHR migration, interface-engine upgrade, FHIR API release, clinical decision-support validation, downtime drill, or go-live rehearsal.
Choose the workflow, patient mix, volume, rare conditions, failure cases, destination systems, and exact outcomes that should be observed.
Generate age-appropriate demographics, ICD-10 diagnoses, LOINC-coded observations, vitals, medications, allergies, orders, notes, procedures, and longitudinal state changes.
Encode facility codes, assigning authorities, MRN rules, code systems, message conventions, FHIR profiles, and transport settings for the target environment.
Deliver the clinical story, capture acknowledgements and downstream state, compare actual behavior with expected outcomes, and produce reproducible evidence.
Each pack combines synthetic patients, executable clinical events, interface traffic, and expected outcomes. Run one scenario, a complete pack, or a hospital-specific validation program.
Prove that registrations, encounters, transfers, discharges, updates, and identity corrections reach the correct chart.
Exercise alerts and clinical escalation across a controlled worsening and recovery trajectory.
Validate orders, specimens, serial results, critical-value handling, amendments, and result-to-patient linkage.
Follow one medication identity from order through dispense, administration, bedside give, change, and discontinuation.
Test the financial journey from coverage and eligibility through charge capture, claim, denial, correction, and remittance.
Deliberately break transport and message sequencing to prove how the environment fails, recovers, and reconciles.
We can configure units, patient mix, facility codes, assigning authorities, MRN format, code systems, custom segments, message timing, target endpoints, expected alerts, and evidence requirements around one real implementation objective.
Every simulation run follows the same path — patient generation, AI content, HL7 assembly, and simultaneous 4-system delivery. 27 messages. Every time.
Purpose-built for hospital workflows. Trained on real clinical patterns. Running locally — no cloud dependency, no latency, no PHI exposure.
Beyond patient generation — a full simulation ecosystem with real-time monitoring, clinical events, and automated workflows.
"Before CareCompile ever touches a real patient, it runs through thousands of synthetic encounters — every scenario, every edge case, every integration point. That's how we know it works."
Every synthetic encounter is delivered simultaneously to all four target systems — each with its own protocol, acknowledgement path, and delivery confirmation.
Earlier versions proved breadth — every message type, every specialty, four live target systems, all of it on the happy path. v9 asks the harder question: what does your interface do when the traffic is wrong? A cancellation retried after a dropped acknowledgement. A patient merge that lands after the update it invalidates. A discharge that arrives before the admit. One transposed digit in a medical record number.
Both diagonals are failures, in opposite directions. An interface that refuses everything scores perfectly on the top row and catastrophically on the bottom — so v9 reports defence and compatibility as two separate numbers and never averages them into one.
The harness ships with a conforming reference receiver so the oracle can be validated against a known-good endpoint before it is ever aimed at a real one — if the reference stops scoring perfectly, the oracle is wrong, not the integration. Transport-level fault injection and the exportable certification report are in active development.
Upgrading an EHR, cutting over an interface engine, standing up a new lab feed — every one of those projects needs weeks of environment testing. MediFlow does that testing with AI instead of headcount.
MediFlow scenarios are not just demo data. Each scripted patient declares what your downstream system should conclude — the alerts, the flags, the critical values. Run the scenario, diff your system's actual output against the declared expectations, and a demo becomes a regression test.
MediFlow turns the simulation engine into a productized, JWT-secured platform: a full REST API, a managed HL7 Integration Engine, clinical workflow tooling, and enterprise-grade audit, retention, and multi-tenancy — all running locally with zero PHI.
Buy a reusable scenario asset, validate one environment with our team, or run MediFlow continuously as your systems change.
A custom population and reusable scenarios for one workflow, interface, product demonstration, or release.
We configure MediFlow against one environment, execute controlled clinical scenarios, and deliver the evidence.
Continuous scenario generation and regression validation for engineering, integration, and clinical AI teams.
What hospital, integration, and clinical AI teams usually ask before a first validation engagement.
Synthetic patient data represents fictional people and clinical events created for development, testing, training, and validation. MediFlow builds complete, coherent patient journeys with known expected outcomes rather than copying a real patient chart.
MediFlow-generated cohorts are constructed without copying production patient records. Synthetic identifiers and provenance markers distinguish test traffic. A customer engagement can be designed to keep generation local and delivery disabled until an approved test destination is configured.
Yes. MediFlow creates HL7 v2 workflows and FHIR R4 resources and Bundles, with additional JSON and CSV exports. Customer profiles control identifiers, facility codes, assigning authorities, code systems, message conventions, and transport.
Common uses include EHR migrations, interface-engine cutovers, lab and pharmacy validation, FHIR API testing, clinical decision-support evaluation, downtime and recovery drills, staff training, regression testing, and go-live rehearsals.
Each scenario is executable and carries an answer key. MediFlow sends the patient journey into the target environment, observes acknowledgements and downstream state, then compares actual behavior with expected clinical and technical outcomes.
A run can include the scenario identity, synthetic cohort manifest, expected-versus-actual assertions, raw request and response hashes, ACK history, timing, throughput, failure details, and a signed reproducible manifest.
Tell us what you are testing. We will define the synthetic patients, interface profile, expected outcomes, delivery path, and proof of completion.
We will reply within one business day. For anything urgent, email hello@carecompile.com.
Pick a scenario, click Run — watch MediFlow build a complete encounter with AI notes, 27 HL7 messages, and 4-system delivery in under 30 seconds.
Before CareCompile ever touches a real patient, it runs through thousands of synthetic encounters — every scenario, every edge case, every integration point, with AI-authored notes and 4 live target systems. That's how we know it works. Zero PHI. Full clinical fidelity. Every time.
Questions? hello@carecompile.com