# SLR and JCA Extraction for a Health-Economics Consultancy | Super Cat > Claude pipelines for a London health-economics consultancy: SLR screening with quoted rationales and JCA extraction across 27 EU countries, every row traceable. Source: https://www.super-cat.tech/portfolio/london-health-economics-slr-jca Last modified: 2026-10-24 [← All case studies](https://www.super-cat.tech/portfolio)27 EU countries · 23-column extraction · every row traceable # London Health-Economics Consultancy: AI-Assisted SLR and JCA Extraction Two Claude pipelines for a London health-economics consultancy: SLR screening with a quoted rationale for every criterion, and JCA clinical-environment extraction across 27 EU countries into a 23-column, fully traceable schema. research.pipeline SLR · JCA - SLR · screen Per-criterion decision + quoted rationale - SLR · ground Every quote checked against its source - JCA · 27 countries Guidelines · HTA decisions · EMA product information - JCA · 23 columns Every row traceable to country, document, page and quote one pipeline in Docker · one in a Claude Code session · no API key Illustration ## The problem SLR screening and JCA evidence extraction are high-volume, repetitive and auditable. Reviewers read thousands of abstracts and full texts, every decision needs a reason, and a JCA dossier needs per-country population and comparator evidence from guidelines, HTA decisions and product information, with verbatim quotes and traceability. ## What we built An SLR screening pipeline that sends each study to Claude with the review’s eligibility criteria and returns per-criterion decisions with quoted rationales in the existing Excel layout, with batch mode, resume and grounding checks. A separate JCA pipeline runs in a Claude Code session with no API key, extracts population and comparator evidence for 27 EU countries from national guidelines, HTA decisions and EMA product information, and writes a 23-column schema where every row is traceable to country, document, page and quote. Before: Thousands of records to screen; Every decision needs a cited reason; 27 countries, three evidence streams. After: 27 EU countries · 23-column extraction · every row traceable.Before - Thousands of records to screen - Every decision needs a cited reason - 27 countries, three evidence streams How it works - 01 Ingest the library. PDFs, registry exports, EndNote files and saved search pages load by content, not by filename. - 02 Screen against the criteria. Each study returns a per-criterion decision and a quoted rationale, in the review’s existing Excel columns. - 03 Ground every quote. A post-run check verifies that each rationale quotes its source record. - 04 Batch, resume, never pay twice. The whole library runs at once, at roughly half the API cost, and a crash resumes from the checkpoint. - 05 Extract JCA evidence. Population and comparator evidence for 27 EU countries, from national guidelines, HTA decisions and EMA product information. - 06 Validate and hand over. The output is checked against the published JCA report; sources that cannot be fetched go to a human upload queue. After 27 EU countries · 23-column extraction · every row traceable Stack Systematic Literature Review JCA Health Economics Evidence Extraction Claude EU HTA Life Sciences UK ## Why it holds up Every decision is auditable: a per-criterion rationale with a quote, a row traceable to country, document, page and quote, and a validation run against the published JCA report. The model reads and extracts; it does not invent. Sources it cannot fetch go to a human queue. ## Team adoption Who uses it nowThe health-economics research team running the SLR and JCA work. What we handed overTwo pipelines, the review prompts, the 23-column JCA workbook, the validation run and the human upload queue. What changedFrom manual screening and per-country extraction to per-criterion decisions with quoted rationales and a traceable JCA sheet. Related service: [AI adoption for research teams](https://www.super-cat.tech/ai-adoption) ## More case studies Big 4 client · fully on-premise ### Enterprise RAG Chatbot for a Big 4 Accounting Firm Multimodal retrieval over a Big 4 firm’s full compliance library, tuned for citation instead of summary — every answer arrives with its source paragraph attached. - Juniors reading thousands of pages per question - Seniors acting as search engines Read the case → 200k calls/month · 67% booking rate vs 77% human ### 24/7 AI Agent Call Center: 200k Calls a Month Across 60 US HVAC Brands Voice agents orchestrating 200,000 calls a month across 60 US HVAC brands for a 5,000-person enterprise, booking 67% of calls against 77% for human agents — within 10 points of a trained team, around the clock. Built as a product, an AI front desk that answers, books and routes, not a pilot. - Night and weekend calls unanswered - Staffing every shift hard to justify Read the case → US$1M collected in the first 3 months ### AI Payment Collection and Reminders The companion product to the AI front desk: it chases unpaid invoices with polite, timed reminders and a payment link, and stops the moment the customer pays or replies. It collected over US$1 million for the client in its first three months. - Chasing payment is awkward and late - Billable time lost to collections Read the case → First-Sprint Data Scientist ## A vetted engineer embedded in your team, a shipped deliverable by end of sprint 1. Pick a time below and you will speak with me directly. Bring the use case, the data and the constraints, and I will give you a straight view on feasibility, effort and the first thing to ship. [Book a call](https://calendly.com/super-chain/catyung?utm_source=super-cat.tech&utm_campaign=first-sprint-data-scientist&utm_content=portfolio%2Flondon-health-economics-slr-jca)Cat Yung Hong Kong & London time zones Book a call · 30 min with Cat Yung