Industry playbook

DPDP for healthtech (India): sensitive-data playbook

Audience: product, clinical ops, security, partnerships · Last reviewed: March 2026

Health-adjacent products collect dense personal data: symptoms, lab PDFs, wearables, insurance quotes, caregiver contacts. Users read medical trust into your UI; your legal and operational posture may differ. This page is informational only, not medical or legal advice—use counsel for high-risk claims. For execution, pair with children’s data where relevant and deletion workflows.

Separate user narrative, clinician/lab records, and derived scores in architecture and notices—mixed blobs break correction and deletion.

30-day playbook

  1. Week 1 — Classification: Tag data types and flows (chat, upload, device, claims) with owners.
  2. Week 2 — Vendor map: Telehealth, transcription, cloud ML—contract scope vs actual API usage.
  3. Week 3 — Deletion drill: Raw + derived + embeddings + analytics projects—document gaps.
  4. Week 4 — Support & safety: Screen-share policies; moderation queues; escalation to grievance contacts.

Lifecycle

  1. Acquisition: Symptom checkers, booking, assessments.
  2. Care delivery: Video, prescriptions, lab integrations.
  3. Monitoring: Wearables, nudges, adherence.
  4. Research / ML: If used, align with explicit consent narratives your counsel approves.

Systems grid

High-sensitivity layers (customize)
Layer Govern Questions
Televideo / chat Sessions, transcripts Recording opt-in; vendor retention; clinician access logs
Diagnostics HL7/FHIR bridges, manual QC Human reviewers; offshore processing; breach reporting paths
Device / wearable Streams, aggregates Re-identification risk; user-visible vs backend-only fields
CRM / growth Newsletters, trials Keep clinical and marketing stacks separated where possible

Disclosure: Planning categories only; no paid rankings. Affiliates/referrals will be disclosed per editorial policy.

Failure modes

Illustrative hypothetical (fiction, not factual): “PulseNest” adds an AI coach that stores nightly chat summaries beside raw chat to speed support. A user requests deletion; staff remove chat but not summaries in an analytics project. A generic wellness tip still echoes a symptom phrase the user believed erased. The lesson: derivative residue must be on the same deletion map as primary data.