A Streamlit app that turns a ward-level audit CSV into an NHS-styled PDF report — with topic-aware, NICE/RCP-grounded recommendations generated by Claude, not hardcoded boilerplate.
🔗 Live demo: audit-ai-mo.streamlit.app
Built by a resident doctor who got tired of writing the same audit recommendations by hand.
Upload a CSV / Excel of audit data → AuditAI:
- Auto-detects the boolean compliance fields (Yes/No, True/False, 1/0)
- Computes overall and per-component compliance with visual bars
- Detects the audit topic from the column names (e.g. "VTE risk assessment", "Hand hygiene (WHO 5 Moments)", "Inpatient medication prescribing")
- Generates 3-4 tailored recommendations via Claude, citing the relevant UK guidance (NICE NG89, WHO 5 Moments, Royal Pharmaceutical Society, etc.)
- Exports an NHS-styled PDF with summary, colour-coded compliance bars, AI recommendations, and a sign-off section
The whole flow takes ~10 seconds end-to-end.
Most "AI"-branded portfolio projects use hardcoded if/else recommendations dressed up in marketing language. AuditAI's recommendations are generated per-audit by Claude Sonnet 4.6, with the audit topic detected from column names.
For a VTE audit (vte_done, prophylaxis_given, compliant):
"Configure the EPR admission clerking template to include a mandatory VTE risk assessment field using the NICE-recommended tool (NICE NG89), preventing form submission until the assessment is completed — this directly targets the 30% gap in vte_done compliance."
For a hand hygiene audit (before_patient_contact, after_patient_surroundings, etc.):
"Launch a targeted education session specifically addressing Moments 1 and 5 (before patient contact and after patient surroundings), which scored lowest at 65% and 60% respectively, using WHO 5 Moments for Hand Hygiene training materials and NICE guideline PH69 as the framework."
For a drug chart audit (prescriber_signature, weight_recorded, prescriber_gmc_number, etc.):
"Embed a mandatory weight-recording field in the EPR admission clerking proforma, so prescriptions cannot be finalised without a documented weight — addressing the 33.3% gap in weight_recorded, in line with NICE medicines optimisation guidance (NG5)."
Same app. Different audits. Different guideline bodies. Different specific gaps targeted. That's what real LLM use looks like in a clinical tool.
Three example datasets ship in examples/ so you can see the topic detection switch live:
| File | Audit topic | What Claude detects |
|---|---|---|
vte_prophylaxis_audit.csv |
VTE prophylaxis prescribing | Cites NICE NG89, NHS CQUIN VTE framework |
hand_hygiene_audit.csv |
Hand hygiene compliance | Cites WHO 5 Moments, NICE PH69, NICE NG125 |
drug_chart_audit.csv |
Inpatient prescribing | Cites NICE NG5, RPS Prescribing Competency Framework |
Upload any one to the live demo and see how the recommendations adapt.
Each report includes the auto-detected topic, colour-coded compliance bars (red <75%, amber 75–95%, green ≥95%), Claude-generated recommendations with their guideline framework header, and a clinician sign-off section.
Audit-AI/
├── app/
│ ├── streamlit_app.py # UI + boolean detection + compliance maths
│ ├── llm.py # Claude topic detection + recommendation prompt
│ └── report_utils.py # ReportLab PDF generation + compliance bars
├── examples/ # 3 example audit datasets
├── assets/ # NHS logo + README screenshots
├── requirements.txt
└── README.md
The LLM module is decoupled — it loads its API key from .env (local) or st.secrets (Streamlit Cloud), and falls back gracefully to generic recommendations if the API is unavailable. The Streamlit UI shows a transparent badge (✨ Generated by Claude /
git clone https://github.com/M-Omarjee/Audit-AI.git
cd Audit-AI
python3.11 -m venv .venv
source .venv/bin/activate
pip install -r requirements.txt
# Add your Anthropic API key (get one at console.anthropic.com)
echo 'ANTHROPIC_API_KEY=sk-ant-...' > .env
streamlit run app/streamlit_app.pyThen upload examples/vte_prophylaxis_audit.csv to see it work.
- Single time-point audits only — doesn't yet support comparing audit cycles (cycle 1 vs cycle 2 trend graphs are on the roadmap)
- Synthetic example data — the shipped examples are illustrative; real audit data should be anonymised before upload (the deployed app does not log uploads, but defence-in-depth)
- LLM output is generated, not curated — recommendations are clinically literate but should be reviewed by the user before submission. Claude can be wrong.
- No persistence — each audit is processed fresh; no user accounts, no saved history. By design for a portfolio MVP.
- Multi-cycle audit comparison (cycle 1 → intervention → cycle 2 trend)
- Free-text "Notes" column summarisation (extract themes from clinician comments)
- Export-to-DOCX option alongside PDF
- Custom NHS Trust branding (logo + colour overrides)
- Optional NICE guideline link-out in PDF recommendations
Dr Muhammed Omarjee Resident Doctor (MBBS, King's College London 2023) Building practical AI tools for NHS frontline workflows.
Sister projects:
- ECG-Explain — 12-lead ECG classifier with Grad-CAM interpretability (PTB-XL, AUROC 0.91)
- Sepsis-AI — NEWS2 vs ML benchmark with decision curve analysis and subgroup audit