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Choose data that matches what you need to test
Start by listing the screens, user actions, and data exchanges your prototype must demonstrate. Separate interface and usability work from API behavior and data-quality testing. Hand-authored fictional records may be enough to lay out screens or test navigation; API work calls for a sandbox whose operations and resource types fit the workflow.
Synthetic data is generated to represent fictional patients and events; it is not simply real patient data with names removed. A sandbox may provide synthetic records through an API, while a generator can produce records for use in your own test environment.
CMS Blue Button API sandbox
CMS says developers can use sandbox credentials to build and test with synthetic Medicare enrollee data. The sandbox documents the same endpoints, resource types, and parameters as production, but CMS cautions that its synthetic dataset may be less comprehensive. CMS recommends that new apps use v2. See the CMS Blue Button API documentation and its v2 guidance.
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AB2D sandbox
CMS’s AB2D sandbox lets anyone try synthetic claims data with a bearer token. AB2D v2 follows FHIR R4. Production AB2D holds real enrollee data, so keep the sandbox and production environments, credentials, and data clearly distinct. The CMS AB2D sandbox instructions describe the test claims environment.
Synthea
Synthea is software described in the HHS ASPE product library as generating synthetic patient records that simulate disease progression and treatment. It is an option when you need generated records rather than a hosted API sandbox. Its existence does not establish that generated records will represent every workflow or edge case in your app. See the ASPE OS-PCORTF Product Library.
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Keep prototype data out of production and incidental systems
Use fictional test identities and keep synthetic records in the prototype’s intended test environment. A record can leak beyond the app’s screens: design files, screenshots, demos, bug reports, analytics, logs, and third-party services may capture information too.
- Collect only the fields needed to demonstrate the workflow; avoid precise or unnecessary data.
- Review free-text fields, logs, analytics, and integrations for accidental identifiers or sensitive details.
- Use fictional examples in screenshots, demos, and issue trackers, and verify that debugging and telemetry do not record unintended content.
- For location-related features, consider whether aggregation can meet the testing need without retaining precise location.
These are practical privacy safeguards, not a universal regulatory checklist. HHS explains that identifiers must be addressed even when they appear in unstructured text; the FTC’s developer guidance recommends minimizing collection and considering aggregation for location use cases. Consult the FTC Mobile Health App Developers best practices alongside the applicable rules for your project.
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Do not equate removing names with de-identification
Deleting a name from a real patient record does not, by itself, meet the HIPAA de-identification standard. HHS describes two methods under HIPAA’s Privacy Rule:
Safe Harbor
Remove the specified identifiers and ensure there is no actual knowledge that the information left behind could identify a person. Identifiers can appear in narrative or other unstructured text, not only in dedicated fields. Dates and distinctive characteristics may also need attention.
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Expert Determination
A qualified person applies accepted statistical and scientific methods, determines that the risk of identification is very small for anticipated recipients, and documents the analysis.
HHS says the risk from information de-identified using either method is very small, but not zero. These are HIPAA methods under U.S. federal guidance, not a global standard or a determination that HIPAA applies to every health app. See HHS’s Guidance Regarding Methods for De-identification of Protected Health Information.
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Plan tests for what synthetic data cannot establish
Production-like API routes do not make a sandbox dataset clinically representative. For example, CMS explicitly says Blue Button’s synthetic data may be less comprehensive than production. Before relying on a sandbox, check whether its records exercise the events, resource types, and variations your workflow requires.
Document the scenarios your prototype does and does not cover. Consider data variety, unusual timelines, missing values, integration failures, and differences in real-world workflows. If synthetic data does not include a needed case, create a clearly fictional test record or test the behavior with a controlled stub; do not silently substitute a real patient record.
If a later stage proposes using data derived from real patients, treat that as a separate privacy, security, authorization, and governance decision. The right approvals depend on the project and applicable rules; synthetic testing alone does not make a deployed app compliant or secure. U.S. developers can use the ONC patient-access resources for developers and EHR vendors as orientation, but they do not determine the legal obligations of a particular implementation.
Keep U.S. guidance in its proper scope
The CMS, HHS, FTC, and ONC resources here are U.S. federal materials. Which laws apply depends on jurisdiction and implementation details; HIPAA does not automatically cover every health app or every developer. Confirm the requirements that apply to your product and data flows rather than treating a sandbox, synthetic dataset, or de-identification method as a blanket compliance guarantee.
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