PRD Template for Healthcare Products

Example: "Primary care physicians spend an average of 16 minutes per patient encounter on EHR documentation, compared to 12 minutes on direct patient care. This documentation burden contributes to a 63% physician burnout rate and an estimated 44% of clinical notes containing copy-pasted content from previous visits, increasing the risk of outdated information influencing treatment decisions. Existing voice-to-text solutions achieve only 78% accuracy on medical terminology, requiring manual correction that negates time savings."

Example: "Primary: Reduce average documentation time per encounter from 16 minutes to under 6 minutes within 90 days of deployment. Validation gate: Clinical accuracy review by medical director must confirm fewer than 1% clinically significant errors in AI-generated notes during 30-day pilot with 20 physicians. Secondary: Achieve 70% voluntary adoption rate among pilot physicians within 60 days (no mandated usage). Compliance gate: HIPAA Security Risk Assessment completed and remediated before any PHI processing."

Example: "As an attending physician, I want to review and approve an AI-generated clinical note before it is saved to the patient chart so that I maintain clinical responsibility for documentation accuracy. Acceptance criteria: AI-generated note is presented as a draft with visual distinction from finalized notes; physician can edit any section inline; approval requires explicit confirmation; approved note includes metadata indicating AI-assisted generation; unapproved drafts are auto-deleted after 72 hours and never visible to other providers."

Example: "FR-1: The system must generate clinical notes using structured medical terminology (SNOMED CT for diagnoses, RxNorm for medications, CPT for procedures). FR-2: All AI-generated content must be clearly labeled as 'AI-assisted' in the EHR with the generating model version recorded. FR-3: The system must support HL7 FHIR R4 for bidirectional data exchange with Epic, Cerner, and Athenahealth EHR systems. FR-4: Clinical notes must be retrievable for a minimum of 7 years per state medical record retention requirements (10 years for pediatric records)."

Example: "CR-1: HIPAA Security Rule — implement all required and addressable safeguards per 45 CFR 164.312. Minimum: AES-256 encryption at rest, TLS 1.2+ in transit, unique user authentication, automatic session timeout after 10 minutes, emergency access procedure documented. CR-2: HIPAA Privacy Rule — minimum necessary standard applied to all PHI access; role-based access controls enforce provider-patient relationship verification. CR-3: Business Associate Agreements (BAAs) executed with all subprocessors handling PHI (cloud provider, AI model provider, analytics service). CR-4: FDA — conduct regulatory assessment to determine if product qualifies as Clinical Decision Support under 21st Century Cures Act exemption criteria."

Example: "SEC-1: PHI encrypted at rest using AES-256 with customer-managed keys (BYOK). SEC-2: All PHI access logged with user ID, patient ID, timestamp, action type, and data elements accessed — logs retained for 6 years per HIPAA requirement. SEC-3: Role-based access control enforcing clinical relationship: physicians access only their patients' records; nurses access only patients in their assigned unit; administrative staff access only demographic and billing data. SEC-4: Break-glass emergency access procedure: any provider can access any patient record in an emergency; access triggers immediate alert to privacy officer and requires documented justification within 24 hours."

Example: "Clinical accuracy: AI-generated note accuracy rate as validated by physician review. Baseline: N/A (new product). Target: over 95% of notes approved without clinically significant edits. Adoption: Percentage of eligible encounters using AI-assisted documentation. Target: 60% within 90 days. Efficiency: Average documentation time per encounter. Baseline: 16 min. Target: under 6 min. Safety: Number of clinically significant errors reported via incident reporting system. Target: fewer than 1 per 1,000 encounters. Monitoring: Weekly safety review by clinical advisory board for first 90 days."

Example: "Phase 1 (Months 1-2): Core product development with synthetic (non-PHI) data. Milestone: Feature-complete in staging environment. Phase 2 (Month 3): HIPAA Security Risk Assessment and penetration testing. Milestone: Assessment complete with all critical findings remediated. Phase 3 (Months 4-5): Clinical validation pilot with 20 physicians at partner health system. Milestone: Clinical accuracy meets threshold; IRB approval obtained if required. Phase 4 (Month 6): IT security review and vendor assessment by health system partner. Milestone: Approved vendor status. Phase 5 (Months 7-8): Phased rollout to full department. Milestone: 100 active physician users."

Example: "Risk: AI-generated clinical note contains an incorrect medication dosage that is not caught by physician review. Likelihood: Low. Impact: Critical (patient safety). Mitigation: Medication references cross-checked against RxNorm database; dosages outside normal ranges flagged with visual warning; clinical safety review board monitors incident reports weekly. Risk: Health system IT department rejects product during vendor security assessment. Likelihood: Medium. Impact: High (blocks deployment). Mitigation: Complete SOC 2 Type II and HITRUST CSF certification before initial sales engagement; pre-populate vendor assessment questionnaire."

Example: "Integration 1: Epic EHR — bidirectional via FHIR R4 APIs (Epic on FHIR). Data elements: patient demographics, encounter data, clinical notes, problem list, medication list. Authentication: Epic OAuth 2.0 with SMART on FHIR scopes. Certification: Epic App Orchard review required (estimated 12-16 weeks). Integration 2: Cerner Millennium — via FHIR R4 APIs (Cerner Ignite). Same data elements. Authentication: Cerner OAuth 2.0. Integration 3: Clinical terminology services — UMLS API for SNOMED CT, ICD-10, RxNorm lookups. SLA: under 200ms response time for terminology lookups during note generation."