Completed work summary (Cycles 1–4) What we produced successfully A practical, DIY architecture built around Airtable as the patient-controlled PHR vault with four ingestion lanes: historical portal exports / PDFs OCR/parsing fallback for legacy documents provider-connected live ingestion via a middleware/aggregator bridge (because direct DIY FHIR to Epic/Cerner is rarely feasible) mobile structured capture via Apple Health / Google Health Connect An end-to-end multi-phase roadmap (0–30 days / 30–60 days / ongoing) including: schema concepts (Documents → extracted values → canonical reconciled values → curated outputs) normalization/conflict handling approach (canonical concepts, confidence scoring, review queues) operational maintenance loop (monthly reconciliation + low-confidence routing) provider usability strategy (export curated summaries like medication/allergy/emergency card rather than granting DB access) System-level data flow diagrams (ASCII + Mermaid-style) describing: where data gaps occur (“GAP: No data transfer occurs here…”) how data should traverse Sources → Aggregator/Bridge → Normalization → Airtable → Output where HIPAA touchpoints and authorization handshakes happen (primarily inside the aggregator/provider-authorized layer) A tool comparison matrix and stack recommendation that identified: which components are low-code/no-code friendly where structured data is expected to exist (mobile vaults and aggregators) which tools are best for legacy PDF reconstruction Key outcomes (the “so what”) You now have a coherent system design that avoids the biggest real-world blocker: direct DIY integration with Epic/Cerner. You have a repeatable pattern for keeping data reliable despite messy duplicates: preserve raw truth separately store extracted values apply canonical mapping + confidence maintain a “Needs Review” workflow so “current medications/allergies” becomes trustworthy over time. You have a practical plan to transition from historical OCR-heavy reconstruction to ongoing structured sync using aggregators + mobile bridges. Lessons learned during planning & execution The architecture is strong, but the implementation specificity must be airtight. The earlier cycles kept things conceptual, and Cycle 4 explicitly flagged that as a mismatch with the requested rigor—especially around: concrete mapping of FHIR/mobile payloads into specific Airtable fields dedup/upsert keys and date/unit handling HIPAA/BAA requirements cannot be left as generic text if the deliverable demands a matrix. Cycle 4 identified a major gap: the “HIPAA/BAA available vs workaround vs unclear” categorization wasn’t consistently delivered as actionable per-tool classification. The roadmap correctly emphasized verifying vendor posture, but it didn’t fully satisfy the “tabulated, per-tool” requirement. Data portability and “changes since 2023” need to be structured, not narrative. The matrix repeatedly included narrative flags, but didn’t consistently implement “Data portability” as a dedicated evaluation column, and “changes since 2023” wasn’t delivered as a standardized per-tool mechanism. What remains imperfect (based on Cycle 4 verification) Cycle 4 concluded that the deliverables were not fully meeting the requested evaluation criteria, primarily because: The Tool Comparison Matrix did not consistently implement required criteria as explicit structured columns (notably Data portability). HIPAA/BAA status was not tabulated in the requested per-tool/actionable manner. The FHIR/mobile-to-Airtable pipeline was described conceptually but not implemented with field-level mapping and upsert logic. Bottom line Core deliverable quality (architecture + phased roadmap): strong and usable. Deliverable quality against the strict formatting/rigor requirements (matrix specificity + mapping specificity): incomplete. If you want, I can now “finish the job” by producing the missing rigor: 1) a revised Tool Comparison Matrix that strictly includes the required columns and per-tool HIPAA/BAA categorization style, and 2) a concrete FHIR/mobile payload mapping + Airtable upsert/dedup key strategy for meds, allergies, labs, encounters, and immunizations (including canonicalization + confidence thresholds).