Is it possible to implement a rare disease case-finding tool in primary care? A UK-based pilot study

Area of Science:

• Medical Informatics
• Genetics and Genomics
• Public Health

Background:

• Rare disease diagnosis presents significant challenges due to complexity and low physician awareness.
• The UK Rare Diseases Framework prioritizes faster diagnosis to improve patient outcomes.
• Electronic Health Records (EHR) offer a valuable resource for identifying potential rare disease cases.

Purpose of the Study:

• To implement and evaluate MendelScan, a primary care rare disease case-finding tool, within a UK National Health Service population.
• To assess the feasibility and effectiveness of using a digital tool to identify patients at risk for rare diseases in a large primary care setting.

Main Methods:

• MendelScan, a tool encoding rare disease criteria and mapping to SNOMED CT codes, was applied to EHR data of 68,705 patients.
• Algorithms flagged patients requiring further evaluation, followed by an internal clinical review to validate potential cases.
• Reports on flagged patients were returned to General Practitioners (GPs) for review and potential further investigation.

Main Results:

• 55 out of 76 disease criteria flagged at least one patient, with 227 patients (0.33%) flagged overall.
• After internal review, 75/227 (33%) flagged EHRs were considered credible cases, with 36 reports returned to GPs.
• Nine cases were categorized by GPs as "Reasonable possible diagnosis" and advanced for further investigation.

Conclusions:

• The pilot study demonstrates the feasibility of implementing a population-level rare disease case-finding tool in primary care.
• MendelScan successfully identified credible rare disease cases that were subsequently referred for further evaluation.
• Future research will focus on performance validation and scalability of the diagnostic algorithms.