Information content as a health system screening tool for rare diseases

Rare diseases (RD) affect 3.5-5.9% of the global population, yet patients often endure a diagnostic odyssey averaging around 5-6 years, navigating multiple specialties and referrals. This study investigates the use of information content (IC) as a screening tool to identify RD candidates using SNOMED CT, the clinical terminology standard for the electronic health record (EHR) in the Singapore health system. A longitudinal dataset of 1,274,199 patients (2018-2022) containing 35,898 unique SNOMED terms was analyzed. Rare disease patients (n = 17,575) were identified through SNOMED-Orphanet mappings. IC effectively distinguished RD patient profiles from the first clinical encounter by applying outcome-driven thresholds. The simplicity of implementing the proposed method enables a screening test that can achieve ~95% sensitivity, while maintaining the potential follow-up burden at a reasonable level (20% precision starting from 3 encounters, with an IC threshold of 8.17). This approach also surfaced 71 underdiagnosed rare diseases in the population (57 of genetic origin). To our knowledge, this is the first study to apply information-theoretic metrics to EHR for RD screening, demonstrating its potential as a screening tool at both hospital and health system levels.

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Publication Details

Journal
npj Digital Medicine
Published
2025-11-25
DOI
https://doi.org/10.1038/s41746-025-02096-x
Citations
1
Primary Topic
Genomics and Rare Diseases
Type
article
Field-Weighted Citation Impact
1.82

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article

Information content as a health system screening tool for rare diseases

Saumya Shekhar Jamuar, Tudor Groza, Khung Keong Yeo, Jenny Hsieh et al.
1 citations
npj Digital Medicine
Genomics and Rare Diseases
1.82
article

Information content as a health system screening tool for rare diseases

Saumya Shekhar Jamuar, Tudor Groza, Khung Keong Yeo, Jenny Hsieh, Peter N. Robinson, Terrence Thomas, Tien Yin Wong, Kaavya Narasimhalu, Neerja Karnani, Gareth S Baynam, Weng Khong Lim, Goh Bee Keow
article en
1 citations

Abstract

Rare diseases (RD) affect 3.5-5.9% of the global population, yet patients often endure a diagnostic odyssey averaging around 5-6 years, navigating multiple specialties and referrals. This study investigates the use of information content (IC) as a screening tool to identify RD candidates using SNOMED CT, the clinical terminology standard for the electronic health record (EHR) in the Singapore health system. A longitudinal dataset of 1,274,199 patients (2018-2022) containing 35,898 unique SNOMED terms was analyzed. Rare disease patients (n = 17,575) were identified through SNOMED-Orphanet mappings. IC effectively distinguished RD patient profiles from the first clinical encounter by applying outcome-driven thresholds. The simplicity of implementing the proposed method enables a screening test that can achieve ~95% sensitivity, while maintaining the potential follow-up burden at a reasonable level (20% precision starting from 3 encounters, with an IC threshold of 8.17). This approach also surfaced 71 underdiagnosed rare diseases in the population (57 of genetic origin). To our knowledge, this is the first study to apply information-theoretic metrics to EHR for RD screening, demonstrating its potential as a screening tool at both hospital and health system levels.

npj Digital MedicineVol. 8(1)
Agency for Science, Technology and Research (SG), International Rescue Committee (ET), SingHealth (SG), Curtin University (AU), Singapore General Hospital (SG), Singapore National Eye Center (SG), Princess Margaret Hospital for Children (AU), King Edward Memorial Hospital (AU), SingHealth Duke-NUS Academic Medical Centre (SG), KK Women's and Children's Hospital (SG), Singapore Eye Research Institute (SG), Duke-NUS Medical School (SG), National Neuroscience Institute (SG), Berlin Institute of Health at Charité - Universitätsmedizin Berlin (DE), National Heart Centre Singapore (SG), Bioinformatics Institute (SG), Perth Children's Hospital, Genome Institute of Singapore (SG), Tsinghua University (CN)
Sanofi, Duke-NUS Medical School, Medical Research Council, National Medical Research Council
Openalex Percentile: Top 11%
Genomics and Rare Diseases
1.82
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