Alexis Balvair sits down with Faisal Zain, a healthcare expert specializing in medical technology and the manufacturing of diagnostics and treatment devices. With years spent translating regulatory intent into safe, scalable products, Faisal bridges engineering reality and policy ambition. In this conversation, he unpacks how Senator Cassidy’s Health Information Privacy Reform Act (HIPRA), unveiled on Nov. 4, would reshape consumer apps and wearables beyond HIPAA’s 1996-era boundaries—covering access, deletion, consent, breach response, de-identification for AI, and the practical workflows that make privacy stick. The themes center on closing gaps HIPAA never covered, building humane user controls, baking in safeguards aligned to HHS and NIST, and balancing interoperability with “minimum necessary” data use as health AI grows.
Cassidy unveiled HIPRA on Nov. 4 to cover wearables and wellness apps beyond HIPAA. What concrete gaps is it closing, and how do you see day‑one impacts on app practices? Walk me through a real-world workflow change, with timelines, staffing, and cost ranges.
HIPRA closes the long‑standing gap where data from wearables and wellness apps fell outside HIPAA simply because no covered entity was involved. That data—weight, blood pressure, sexual health—could travel to analytics partners and ad networks without the user protections HIPAA requires. Day one, product teams will need consent gates that are specific, revocable, and tied to “permitted uses and disclosures” mirroring HIPAA, plus a functional right to access, modify, and delete. Practically, I’d stand up a privacy-by-design sprint led by a privacy engineer, a backend engineer, a counsel liaison, and a product manager, focused on mapping data flows from device to cloud to partners and cutting any non‑essential transfers. Instead of a patchwork policy update, the core workflow change is a consent‑driven data‑use matrix at app launch and an authorization service that gates outbound data flows to partners, with written authorization standards built in. Teams feel the shift immediately: fewer silent SDKs, more explicit user prompts, and audit trails that show why any data moved.
HIPRA grants a right to delete health data, which HIPAA lacks. How should an app design a deletion request flow end-to-end? Share a step-by-step process, edge cases (backups, logs, vendors), and metrics you’d track to prove deletion at scale.
Start with a prominent “Delete my health data” entry in account settings, echoing HIPRA’s individual rights. Step one: identity verification that’s privacy‑preserving—device-bound confirmation and a short-lived code. Step two: a scoped deletion selector (all data versus certain categories like weight or sexual health), with clear warnings about impacts on features. Step three: orchestrate deletion through a job that touches the primary store, derived datasets, caches, and partner APIs; it should issue signed attestations from each system. Edge cases include immutable logs (mask rather than delete), legal holds (pause and notify), backups (flag for purge on restore), and vendors (contractually bind them to propagate deletion). To prove deletion at scale, I’d track the proportion of requests fully closed with partner attestations, the percentage of derived datasets refreshed, and the time from request to final confirmation. The confirmation should include what was deleted, what was masked, and the rationale, all consistent with permitted uses and disclosures.
HIPRA covers data like weight, blood pressure, and sexual health info. Can you map how those signals move from device to cloud to partners today, and how HIPRA would reshape that chain? Offer one anecdote showing current leakage points and fixes.
Today a wearable collects metrics via sensors, sends them to a phone app, batches them to a cloud API, and then syncs to analytics, crash reporting, personalization, and sometimes marketing partners. Even if identifiers are hashed, the combination of timestamps and location can re‑link to a person. HIPRA forces the chain to collapse to “minimum necessary”: device to app to cloud for core functionality, with partner sharing only under a documented permitted use or explicit written authorization. In one implementation review, I found sexual health entries flowing to a third‑party A/B testing tool because the SDK scooped up “page view” parameters indiscriminately. The fix was to segment health events into a protected pipeline, strip parameters in the client, and require a signed authorization before any external share. The user still got insights, but the leakage stopped.
The bill includes Part 2 data for substance use disorder. How would you segment, tag, and audit SUD data differently from other health data? Describe specific role-based controls, consent prompts, and breach playbooks, with any measurable performance or error rates.
I’d introduce a Part 2 data domain with labels that travel with the data—every record tagged at ingestion and in derived stores. Access would be role‑based and time‑bounded, with least‑privilege service accounts; even engineers would use break‑glass procedures with after‑action reviews. Consent prompts should be clearer and more affirmative—separate screens for SUD disclosures, with written authorization language and the ability to decline without losing non‑SUD features. The breach playbook would escalate faster: isolate affected systems, freeze partner transfers, notify individuals and authorities as required by a rule that mirrors HIPAA’s Health Breach Notification Rule, and document every decision. I expect fewer access denials when tags are consistent and prompts are unambiguous; audits should show that SUD queries are restricted to permitted roles and that any anomalies are investigated and closed.
HIPRA mirrors HIPAA’s permitted uses and disclosures and written authorization standards. How would you rewrite a consumer app’s data-use matrix under HIPRA? Give concrete categories, sample authorization language, and a change-control process for updating uses without breaking consent.
Categories would split into care‑related functions, operations, research with de‑identified data under minimum necessary, and marketing that requires written authorization. Sample language: “I authorize [App] to disclose my [category] information to [entity] for [purpose]. This authorization is voluntary and may be revoked at any time.” Each row would note whether the use is permitted without authorization or requires it, and whether de‑identification applies. Change‑control would route any new use through legal and privacy review, update the matrix, trigger in‑app re‑consent for affected users, and freeze data flows until authorizations are refreshed. Version history lives in the app and policy page so users can see what changed and when.
Consumers can access, modify, or delete app-linked health data. What UI patterns and backend APIs best support those rights? Share one before-and-after design example, SLAs for response times, and how you’d verify identity without harming user experience.
Put a “Your Data” hub in the profile with plain‑language actions: View, Fix, Download, Delete. Use wizards that preview consequences and show progress for long‑running tasks. Backend APIs should be idempotent and auditable—/export, /rectify, /delete—with status endpoints. Before, many apps hide these under support; after, they’re first‑class, with confirmations that enumerate exactly what happened. For identity, use in‑app confirmation linked to a known device and a short code sent to a verified channel, instead of demanding extra documents. Response times should be fast enough to feel respectful and provide interim receipts when work continues in the background; users should never wonder if their clicks vanished.
HIPRA requires physical, technical, and administrative safeguards aligned to NIST or HHS frameworks. Which controls would you prioritize in year one, and why? Lay out a 90‑day roadmap with specific safeguards, budgets, and verification artifacts auditors will expect.
First, harden identity and access management, encrypt data in transit and at rest, and enable continuous monitoring. On the administrative side, formalize data maps, vendor risk reviews, and workforce training tuned to HIPRA’s rights and permits. Physically, ensure device security policies and secure areas for any on‑prem assets. In a 90‑day roadmap, I’d deliver access control rollouts, encryption key management, logging with tamper‑evident storage, and an incident response plan aligned to a rule that mirrors HIPAA’s breach requirements. Auditors will expect policies, training logs, data maps, risk assessments, and evidence that controls are operating—think log excerpts, access reviews, and incident drill reports.
Breach notification would mirror HIPAA’s Health Breach Notification Rule. How would you operationalize detection, forensics, and multi-party notices? Walk through a sample incident timeline, thresholds for media notice, and templates that keep legal risk low but users informed.
Start with layered detection—application alerts and anomaly monitoring tied to protected data flows. Forensics needs a clean room: snapshot the environment, preserve logs, and use independent tooling to determine scope and whether health information was accessed without authorization. The timeline runs from detection to containment, to confirmation of affected individuals and partners, to drafting notices for individuals, authorities, and, when the mirrored rule triggers it, media. Templates should state what happened, what information was involved, what steps were taken, what users can do, and contact points, avoiding speculative language but offering concrete remediation. Coordination with partners is essential so users don’t receive conflicting messages.
The bill pushes “minimum necessary” for AI training on de-identified data. How would you engineer pipelines to enforce that? Detail dataset scoping, k‑anonymity or differential privacy choices, re-identification testing, and metrics proving utility versus privacy.
Begin with a strict purpose statement for each dataset—only the fields necessary for the model’s objective. Apply de‑identification that’s appropriate to the datk‑anonymity for structured metrics like weight and blood pressure, and careful aggregation or noise for sensitive categories like sexual health. Run re‑identification tests using challenge datasets and linkage attacks to probe weakness. Track model utility on task‑relevant benchmarks while documenting privacy protections—if utility falls, adjust features or de‑identification parameters rather than adding more raw data. Every training run should have a provenance record: source, transforms, approvals, and the “minimum necessary” rationale.
Cassidy flags risks of re-identification in AI. What techniques most reduce that risk without gutting model performance? Share a concrete case where you balanced privacy and accuracy, with measurements, ablation steps, and a rollback plan if drift appears.
The biggest wins come from careful feature engineering—binning continuous measures, removing rare combinations, and suppressing timestamps that enable linkage. For narrative or free text, avoid ingesting it altogether unless it’s scrubbed beyond de‑identification norms. In one case, we removed location granularity and limited time windows while preserving trends; performance stayed strong because the model relied on trajectories, not precise coordinates. We staged ablations in a sandbox, monitored for drift, and kept a ready rollback that reverted to a simpler model if privacy safeguards degraded utility over time. The guiding principle was to protect identity without erasing the signal that predicts outcomes.
HIPRA changes how health data is de-identified across the sector. How would you update de-ID policies for providers and apps together? Describe governance, data maps, expert determination protocols, and how you’d test vendors’ claims with audits or challenge datasets.
I’d form a joint governance council with providers and app teams to harmonize definitions and procedures. Start with unified data maps that trace data from collection to storage to sharing, flagging health and Part 2 categories. Adopt expert determination protocols that document risks and assumptions, then refresh them as interoperability expands. To validate vendors, require de‑identification documentation, run audits, and challenge their claims with test datasets designed to mimic linkage attacks. If a vendor can’t demonstrate resilience, de‑identified sharing pauses until they can.
Providers and apps must inform users when HIPAA no longer protects their data. What plain-language disclosures actually work? Give two versions—onboarding and in‑app—plus placement, click-through rates you’d aim for, and how you’d A/B test comprehension.
Onboarding: “When you use this app, some of your health data is not protected by HIPAA. We protect it under our privacy rules. You control how it’s used, and you can access, change, or delete it.” In‑app banner near data entry: “This feature uses health data outside HIPAA. Tap to see your choices, including deletion.” Place both where decisions happen—at signup and above the feature that collects sensitive data. I’d A/B test clarity with comprehension checks right after reading: users answer a simple question to confirm they understand who protects the data and their rights. The goal is genuine understanding, not just clicks.
The bill enables “directed disclosure” by patients to other entities. How would you design a secure, user-friendly flow for that? Include consent granularity, expiration, API standards, and a concrete example showing how a patient shares limited metrics to an insurer.
The flow should let users choose what to share, with whom, for what purpose, and for how long. Granularity matters: pick specific metrics like weight or blood pressure, set an expiration, and require written authorization language the user can review and revoke. Use standards‑based APIs and secure tokens bound to the scope and duration. For example, a user directs the app to send only weekly average blood pressure to an insurer for a wellness rebate, with an expiration and no location or sexual health data. The app shows an audit trail and a one‑tap revoke control.
The National Academies would study compensating patients for sharing identified data. What compensation models seem feasible? Compare cash, premium discounts, or data dividends; propose safeguards against coercion; and outline metrics to measure fairness and participation.
Cash is simple and transparent, but it can feel coercive if offered without guardrails. Premium discounts align with wellness programs, though they risk penalizing those who don’t or can’t participate. Data dividends tie compensation to the value created, yet they’re complex to administer. Safeguards include clear opt‑out paths, strict separation from clinical decision‑making, and independent oversight to ensure consent is voluntary. Fairness metrics should examine who participates, who declines, and whether benefits vary by demographics, with course corrections when gaps appear.
Cassidy ties privacy to growing interoperability. How can apps maintain seamless data exchange while meeting HIPRA’s tighter rules? Walk me through a reference architecture, including identity, consent registries, data minimization at query time, and monitoring to prevent scope creep.
Start with strong identity proofing linked to device trust and account verification. Layer a consent registry that records permitted uses and written authorizations, and bind every data exchange to that registry. At query time, minimize data by default—return only what the consent allows, with filters applied at the source. Monitoring should detect scope creep: alerts when partners request fields outside consent, and dashboards showing disclosures over time. The result is interoperability that feels smooth to users but is fenced by rules that are visible, revocable, and auditable.
Do you have any advice for our readers? Treat privacy as product quality. Map your data like you’d map a critical supply chain, and let users see and shape what happens to their information. Write consent and disclosures as if you were explaining them to a friend, and let people change their minds without friction. If you build trust into your systems now, HIPRA won’t be a scramble—it’ll be an affirmation of choices you’ve already made.
