Start from the clinical use case
Programs that succeed tend to define, before any code is written, who reviews the data, how often, what triggers an action and where that action is documented. A post-discharge heart failure program needs daily weights and blood pressure with escalation rules. A cardiac rehab program may care about activity minutes and heart rate during exercise. A sleep study may need staged sleep intervals and nightly HRV.
We work through these questions with clinical and operational stakeholders and translate them into data requirements: which measurements, at what resolution, from which devices, with what latency, and what summaries clinicians will actually look at.
- Target population and enrollment criteria
- Measurements and minimum acceptable data quality
- Review cadence, thresholds and escalation paths
- Where findings are documented in the record
Consumer wearables and medical devices
Consumer devices such as Apple Watch, Fitbit, Garmin and Oura produce rich longitudinal data and patients often already own them. They are generally not validated as clinical-grade measurement for diagnosis or treatment decisions, and their algorithms change with firmware and app updates. They are well suited to trend monitoring, engagement and research, with appropriate caveats.
FDA-cleared devices such as cellular blood pressure cuffs, weight scales, pulse oximeters and continuous glucose monitors are the usual choice when readings drive clinical action or reimbursement. Many programs use both. We keep the two categories distinct in the data model so clinicians can see what kind of device produced a value. For regulated device connectivity, see medical device integration.
Enrollment, identity and consent
Every wearable data stream needs to be tied to the right patient. That sounds trivial until a patient enrolls with a personal email, uses a family member's phone, or switches devices midway through a program. We design enrollment flows that link the device or platform account to an internal patient identifier and, where needed, to the MRN used by the EHR.
Consent is captured at two levels: the platform permission the patient grants (for example, HealthKit read access for specific types, or a Fitbit OAuth scope) and your program's own consent for using the data clinically. Both are recorded, and revocation at either level stops ingestion and is logged.
Data quality before clinical review
Clinicians lose trust quickly when dashboards show impossible values or gaps they cannot explain. The integration layer should catch these before a nurse does. We apply plausibility checks, flag readings taken outside expected conditions where the device reports them, and surface data gaps as a distinct status rather than as missing rows.
Normalization covers UCUM units, timezone handling, deduplication of samples synced through more than one app, and provenance on every value. When data is sent onward as FHIR Observations, each type is mapped to LOINC. Our FHIR wearable integration page covers the resource modeling in more detail.
- Range and plausibility checks per measurement type
- Non-transmission and gap detection per patient
- Source and device type visible on every value
- Audit trail from raw sample to clinical summary
Fitting into clinician workflows
Pushing raw wearable data into an EHR flowsheet usually creates noise. Most programs work better with a review layer, either a care management dashboard or a SMART on FHIR app, where staff triage alerts and trends, and then a summarized, clinician-confirmed result is filed to the chart.
We design the hand-offs between these layers with your informatics team: which events create tasks, who owns them, how acknowledgement is recorded, and which values are written back to the EHR. The goal is that a clinician sees the right information at the moment they are making a decision, with a clear indication of where it came from.
Privacy, security and governance
We build healthcare wearable integrations with HIPAA-aligned controls: encryption, role-based access, audit logs, minimum necessary data collection and configurable retention. We also document every third party in the data path, since wearable vendors' own terms and data practices differ.
Whether a deployment satisfies HIPAA depends on your organization's compliance program, policies, risk analysis and BAAs with the vendors involved. Some consumer wearable vendors do not sign BAAs, which affects how their data can flow. We make these dependencies visible early so governance review does not stall a launch. See HIPAA-aligned wearable integration for more.
Frequently asked questions
Can consumer wearable data be used for clinical decisions?
Consumer wearable data is generally not a substitute for clinical-grade measurement, and using it for diagnosis or treatment decisions requires appropriate validation for the specific use. Many programs use consumer data for trends and engagement, and FDA-cleared devices for measurements that drive clinical action. Your clinical leadership should set that policy.
How do you match wearable data to the right patient?
We link the device or platform account to an internal patient identifier during enrollment and, where required, to the EHR's MRN through your existing patient matching process. Re-enrollment and device changes are handled as explicit events so data does not silently attach to the wrong record.
Should all wearable data go into the EHR?
Usually not. High-frequency data such as minute-level heart rate is better kept in a monitoring platform, with summaries, flagged events or clinician-reviewed results filed to the chart. The right split depends on your EHR's capabilities and your clinicians' preferences.
Who is responsible for HIPAA compliance?
Your organization is, as part of its overall compliance program. We provide HIPAA-aligned technical controls and documentation of data flows, and a BAA covers any role we have in handling PHI, but compliance also depends on your policies, risk analysis and agreements with every vendor in the data path.
Do you work with clinical informatics teams?
Yes. Most healthcare wearable projects need input from informatics on data mapping, EHR build, alert design and documentation. We work alongside your informatics and interface teams rather than around them.