Is It Possible to Implement a Rare Disease Case-finding Tool in Primary Care? A UK-based Pilot Study

Abstract

Introduction:This study implemented MendelScan, a primary care rare disease case-finding tool, into a UK NHS population. Rare disease diagnosis is challenging due to disease complexity and low physician awareness. The 2021 UK Rare Diseases Framework highlights a global need for faster diagnosis to improve clinical outcomes as a key priority.Methods & Results:A UK primary care locality with 68,705 patients was examined. MendelScan encodes diagnostic/screening criteria for multiple rare diseases, mapping clinical terms to appropriate SNOMED CT codes (UK primary care standardised clinical terminology) to create digital algorithms. These algorithms were applied to a pseudo-anonymised structured data extract of the electronic health records (EHR) in this locality to "flag" at-risk patients who may require further evaluation. All flagged patients then underwent internal clinical review; for those that passed this review, a report was returned to their GP. 55 of 76 disease criteria flagged at least one patient. 227 (0.33% of the total population) patients were flagged; 18 EHR were already diagnosed with the disease. 75/227 (33%) passed our internal review. Thirty-six reports were returned to the GP. Feedback was available for 28/36 of the reports sent. GP categorised nine reports as "Reasonable possible diagnosis" (advance for investigation), six reports as "diagnosis has already been excluded", ten reports as "patient has a clear alternative aetiology", and three reports as "Other" (patient left study locality, unable to reidentify accurately). All the 9 cases considered as "reasonable possible diagnosis" had a further actionable evaluation.Conclusions:This pilot demonstrates that implementing such a tool is feasible at a population level in an ethical, technical and efficient manner. The case-finding tool identified credible cases which were subsequently referred for further investigation. Future work includes performance-based validation studies of diagnostic algorithms and the scalability of the tool.