Platform

Everything you need
to build stronger cases.

Six integrated capabilities — from AI record analysis to enterprise-grade encryption — working together in one platform.

AI Record Analysis Protocol Compliance Actionable Findings AI Case Chat PII Protection Security & GDPR

Analyse Medical Records in Minutes, Not Weeks

Upload PDF medical records — including scanned and handwritten documents — and let our AI extract key clinical events, medications, diagnoses, and treatment timelines automatically.

  • Handles large PDF documents, including scanned pages
  • Intelligent OCR detection — only applies where needed
  • Chronological medical timeline extraction by event category
  • Real-time progress tracking as your case is processed

medical-records-2024.pdf

PDF — 847 pages

75%
OCR active — 23 scanned pages detected
12 Mar 2023 GP Referral to Specialist
04 Jun 2023 MRI Scan Result
18 Sep 2023 Surgical Consultation

47 events extracted...

Seven Parallel Protocol Compliance Analyses

Our AI runs seven simultaneous analyses against clinical protocols — checking delays, missed appointments, care pathways, treatments, medications, and more — using semantic search to find relevant protocol sections.

01

Care Delays

Complete
02

Missed Appts

Complete
03

Pathways

Complete
04

Treatment

Complete
05

Medication

Complete
06

Follow-up

Running...
07

Referrals

Running...
Semantic search: finding "missed follow-up appointment" in NICE CG76 Section 4.2...
Delays in care, missed appointments, and care pathway compliance
Treatment and medication compliance checking
Semantic protocol search — finds relevant guidance even with different terminology
User-focused analysis guided by your specific areas of concern
Finding #3: Delayed Referral 8/10

"Patient was not referred to cardiology despite presenting with persistent chest pain on three consecutive visits..."

NICE CG95 Section 3.1 Page 47
Finding #7: Incomplete Medication Review 5/10

"Annual medication review was overdue by 4 months. No documented assessment of side effects..."

NICE CG76 Section 2.4 Page 112
12 findings total
3 Critical 5 Medium 4 Low

Severity-Scored Findings with Evidence Citations

Every identified compliance issue is structured as an actionable finding with a 1–10 severity score, direct evidence from the medical record, protocol references, and recommendations.

  • Severity scoring from 1 (minor) to 10 (critical patient harm)
  • Direct quotes from the medical record as evidence
  • Specific protocol section references for each finding
  • Page-level citations back to the original document

Ask Anything About Your Case

Ask natural language questions about any case and get accurate, source-cited answers grounded in the actual medical record. Powered by RAG with page-level references.

Case Chat — Smith v. NHS Trust

Was the patient referred to a specialist after the abnormal blood results?

Based on the medical records, the patient was not referred to a specialist following the abnormal blood results on 15 March 2023. The GP noted the results but documented "monitor and repeat in 3 months" without referral.

Source: Page 47, Para 3

What does the relevant NICE guideline recommend in this situation?

Ask a question about this case...
Natural language questions with page-level source citations
Answers grounded in the document — no hallucination
Conversation memory for follow-up questions
Persistent chat history across sessions

Triple-Layer PII Sanitisation

Before any text reaches an AI model, all personally identifiable information is stripped through three independent detection systems — Microsoft Presidio, spaCy NER, and 30+ UK medical record regex patterns.

Raw Text

John Smith, DOB 15/04/1962, NHS No: 943 476 5919, seen at Royal London Hospital

Triple-Layer Sanitisation

Layer 1: Microsoft Presidio
Layer 2: spaCy NER
Layer 3: 30+ UK Regex Patterns

NHS numbers, postcodes, GMC numbers...

Sanitised Text

[PATIENT], DOB [DATE], NHS No: [NHS_NUMBER], seen at [HOSPITAL]
Microsoft Presidio enterprise PII engine as first layer
spaCy named entity recognition as second layer
30+ custom regex patterns for UK medical identifiers
NHS numbers, postcodes, GMC numbers, and more — all removed

Enterprise-Grade Security and GDPR Compliance

AES-256-GCM encryption at rest with per-record nonces and tamper detection. Full GDPR compliance with comprehensive audit logging.

AES-256

GCM encryption at rest

Algorithm:AES-256-GCM
Key Derivation:Per-record nonce
Auth Tag:128-bit GMAC
Tamper Detection:Enabled

GDPR Compliant

Full regulatory compliance

Security Layers

Rate Limiting
CSRF Protection
Security Headers
Audit Logging

End-to-End Data Protection

Every layer designed to keep sensitive medical data secure.

Automatic data purge after configurable retention period
Full audit trail for every record access and action
Data Processing Agreement available for all plans
PII stripped before any data reaches AI models

See It in Action

Book a personalised demo to see how MedCase AI can transform your case preparation workflow.

Schedule a Demo