Abstract
Patient-facing technology to support rare disease patients seeking diagnosis has received comparatively little focus from the literature, despite the recognition of its importance. We hypothesise that this is due to the challenges presented when designing pre-diagnostic patient-facing technology within this area. A significant obstacle for research in this area is the lack of data which represents the patient’s perspective. Existing data typically does not present the temporal aspects of diagnosis which are crucial to evaluate the diagnosis time of technology and consists of clinical terminology which is not representative of patients. This work aims to bridge this gap by creating open-source data which: (i) utilises patient-friendly terms and (ii) facilitates the sequencing of phenotypes to temporally recreate the informational journey of a rare disease patient. Therefore, this work facilitates evaluations on whether pre-diagnostic technology reduces the time to a rare disease diagnosis, thus providing more meaningful metrics for success.
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wikipedia.org/wiki/Gaucher’s_disease/ https://www.ncbi.nlm.nih.gov/books/NBK1269/.
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rheumatic fever, dermatomyositis, erythromelalgia, myelofibrosis, five rare forms of leukemia, two rare types of avascular necrosis, two rare forms of rheumatoid arthritis, vEDS, cEDS, cardiac-valvular EDS.
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References
Berglund, B., Nordström, G., Lützén, K.: Living a restricted life with ehlers-danlos syndrome (eds). Int. J. Nurs. Stud. 37(2), 111–118 (Apr 2000). https://doi.org/10.1016/S0020-7489(99)00067-X, https://www.sciencedirect.com/science/article/pii/S002074899900067X
Colomba, P., et al.: Fabry disease and multiple sclerosis misdiagnosis: the role of family history and neurological signs. Oncotarget 9, 7758–7762 (2018). https://doi.org/10.18632/oncotarget.23970
De Choudhury, M., Morris, M.R., White, R.W.: Seeking and sharing health information online: comparing search engines and social media. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, pp. 1365–1376. CHI ’14, Association for Computing Machinery, New York, NY, USA (Apr 2014). https://doi.org/10.1145/2556288.2557214
Department of Health UK: The UK strategy for rare diseases (2013). https://assets.publishing.service.gov.uk/government/uploads/system/uploads/attachment_data/file/260562/UK_Strategy_for_Rare_Diseases.pdf
Depping, M.K., Uhlenbusch, N., von Kodolitsch, Y., Klose, H.F.E., Mautner, V.F., Löwe, B.: Supportive care needs of patients with rare chronic diseases: multi-method, cross-sectional study. Orphanet J. Rare Dis. 16, 44 (2021). https://doi.org/10.1186/s13023-020-01660-w
Faurisson, F.: Survey of the delay in diagnosis for 8 rare diseases in Europe: Eurordiscare2 (2004). https://www.eurordis.org/wp-content/uploads/2009/12/EURORDISCARE_FULLBOOKr.pdf
Faviez, C., et al.: Diagnosis support systems for rare diseases: a scoping review. Orphanet J. Rare Dis. 15(1), 94 (2020). https://doi.org/10.1186/s13023-020-01374-z
Genetic Alliance, The New York Mid-Atlantic Consortium for Genetic and Newborn Screening Services: Understanding Genetics: A New York, Mid-Atlantic Guide for Patients and Health Professionals. Genetic Alliance, Washington (DC) (Jul 2009). https://pubmed.ncbi.nlm.nih.gov/23304754/
Halverson, C.M.E., Cao, S., Perkins, S.M., Francomano, C.A.: Comorbidity, misdiagnoses, and the diagnostic odyssey in patients with hypermobile ehlers-danlos syndrome. Genetics Med. Open 1(1), 100812 (Apr 2023). https://doi.org/10.1016/j.gimo.2023.100812, https://www.sciencedirect.com/science/article/pii/S294977442300821X
Hershenfeld, S.A., et al.: Psychiatric disorders in Ehlers-Danlos syndrome are frequent, diverse and strongly associated with pain. Rheumatol. Int. 36(3), 341–348 (2016). https://doi.org/10.1007/s00296-015-3375-1
Kruse, C.S., Smith, B., Vanderlinden, H., Nealand, A.: Security techniques for the electronic health records. J. Med. Syst. 41, 127 (2017). https://doi.org/10.1007/s10916-017-0778-4
Kühnle, L., Mücke, U., Lechner, W.M., Klawonn, F., Grigull, L.: Development of a social network for people without a diagnosis (rarepairs): Evaluation study. J. Med. Internet Res. 22(9), e21849 (Sep 2020). https://doi.org/10.2196/21849, http://www.jmir.org/2020/9/e21849/
Haendel, M., et al.: How many rare diseases are there? Nat. Rev. Drug Discov. 19(2), 77–78 (2020). https://doi.org/10.1038/d41573-019-00180-y, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7771654/
Mehta, A., et al.: Fabry disease defined: baseline clinical manifestations of 366 patients in the fabry outcome survey. Europ. J. Clin. Invest. 34(3), 236–242 (2004). https://doi.org/10.1111/j.1365-2362.2004.01309.x
Mistry, P.K., et al.: A reappraisal of Gaucher disease-diagnosis and disease management algorithms. Am. J. Hematol. 86(1), 110–115 (2011). https://doi.org/10.1002/ajh.21888
Muir, E.: The rare reality - an insight into the patient and family experience of rare disease (2016). https://www.raredisease.org.uk/media/1588/the-rare-reality-an-insight-into-the-patient-and-family-experience-of-rare-disease.pdf
Ronicke, S., Hirsch, M.C., Türk, E., Larionov, K., Tientcheu, D., Wagner, A.D.: Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet J. Rare Dis. 14(1), 69 (2019). https://doi.org/10.1186/s13023-019-1040-6
Köhler, S., et al.: The human phenotype ontology in 2021. Nucleic Acids Res. 49(D1), D1207–D1217 (2021). https://doi.org/10.1093/nar/gkaa1043
Shahsavar, Y., Choudhury, A.: User intentions to use chatgpt for self-diagnosis and health-related purposes: Cross-sectional survey study. JMIR Hum Factors 10, e47564 (May 2023). https://doi.org/10.2196/47564, http://www.ncbi.nlm.nih.gov/pubmed/37195756
Walker, H.K., Hall, W.D., Hurst, J.W.: Clinical Methods: The History, Physical, and Laboratory Examinations. Butterworth-Heinemann Ltd, Boston, 3rd edn. (Apr 1990), https://www.ncbi.nlm.nih.gov/books/NBK201/, chapter 215 The Family History
Walkowiak, D., Domaradzki, J.: Are rare diseases overlooked by medical education? awareness of rare diseases among physicians in Poland: an explanatory study. Orphanet J. Rare Dis. 16, 400 (2021). https://doi.org/10.1186/s13023-021-02023-9
Wen, Q., Ouyang, Z., Zhang, J., Qian, Y., Ye, Y., Zhang, C.: Disentangled dynamic heterogeneous graph learning for opioid overdose prediction. In: Proceedings of the 28th ACM SIGKDD Conference on Knowledge Discovery and Data Mining. p. 2009-2019. KDD ’22, Association for Computing Machinery, New York, NY, USA (2022). https://doi.org/10.1145/3534678.3539279
Acknowledgments
The authors would like to thank Amicus Therapeutics for their support during this project. The main author is funded by the EPSRC Centre for Doctoral Training in Enhancing Human Interactions and Collaborations with Data and Intelligence Driven Systems (EP/S021892/1).
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Nielsen, E., Owen, T., Roach, M., Dix, A. (2024). Synthetic Patient Perspective Data for the Curation and Evaluation of Rare Disease Patient-Facing Technology. In: Xie, X., Styles, I., Powathil, G., Ceccarelli, M. (eds) Artificial Intelligence in Healthcare. AIiH 2024. Lecture Notes in Computer Science, vol 14976. Springer, Cham. https://doi.org/10.1007/978-3-031-67285-9_24
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