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Weblinks and references

Digital health revolution and its transformative potential for rare diseases – WEBLINKS AND REFERENCES

REFERENCES: Autumn 2021 Issue 021 – refer to page 56 for respective article.

References

  1. Why does the NHS struggle to adopt eHealth innovations? A review of macro, meso and micro factors | BMC Health Services Research | Full Text (biomedcentral.com)
  2. Forgotten families: Families feel more isolated than ever under lockdown – Together for Short Lives
  3. Digital technologies in the public-health response to COVID-19 | Nature Medicine
  4. Implementation of remote consulting in UK primary care following the COVID-19 pandemic: a mixed-methods longitudinal study | British Journal of General Practice (bjgp.org)
  5. World Health Organization. WHO Guideline: Recommendations on Digital Interventions for Health System Strengthening. https://www.who.int/reproductivehealth/publications/digital-interventions-health-system-strengthening/en/ 
  6. deloitte-uk-connected-health.pdf
  7. Radu Dragusin, Paula Petcu, Christina Lioma, Birger Larsen, Henrik L Jørgensen, Ingemar Cox, Lars K Hansen, Peter Ingwersen, and Ole Winther, FindZebra: a Search Engine for Rare Diseases, in International Journal of Medical Informatics, IJMI doi:10.1016/j.ijmedinf.2013.01.005 (2013)
  8. Frontiers | 3D Facial Analysis in Acromegaly: Gender-Specific Features and Clinical Correlations | Endocrinology (frontiersin.org) Attachment-2
  9. Frontiers | Identifying Facial Features and Predicting Patients of Acromegaly Using Three-Dimensional Imaging Techniques and Machine Learning | Endocrinology (frontiersin.org)
  10. Tzung-Chien Hsieh et al. PEDIA: prioritization of exome data by image analysis. Genetics in Medicine, 2019; DOI: 10.1038/s41436-019-0566-2]
  11. Esteva A, Kuprel B, Novoa RA, Ko J, Swetter SM, Blau HM, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542:115–8.
  12. Liang H, Tsui BY, Ni H, Valentim CCS, Baxter SL, Liu G, et al. Evaluation and accurate diagnoses of pediatric diseases using artificial intelligence. Nat Med. 2019;25:433.
  13. Gulshan V, Peng L, Coram M, Stumpe MC, Wu D, Narayanaswamy A, et al. Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs. JAMA. 2016;316:2402–10.
  14. Ronicke S, Hirsch MC, Türk E, Larionov K, Tientcheu D, Wagner AD. Can a decision support system accelerate rare disease diagnosis? Evaluating the potential impact of Ada DX in a retrospective study. Orphanet J Rare Dis. 2019;14:69.
  15. Is the $1,000 genome for real? | Nature
  16. The Cost of Sequencing a Human Genome
  17. Project Baseline by Verily | Join Clinical Trials and Research Opportunities
  18. Genome UK: the future of healthcare (publishing.service.gov.uk)
  19. https://www.gov.uk/government/publications/uk-rare-diseases-framework
  20. Conduct of Clinical Trials of Medical Products During COVID-19 Public Health Emergency (fda.gov)
  21. Microsoft Word – CT-C19 guidance v4 04-02-2020- final (europa.eu)
  22. The use of machine learning in rare diseases: a scoping review | Orphanet Journal of Rare Diseases | Full Text (biomedcentral.com)
  23. https://www.mckinsey.com/industries/healthcare-systems-and-services/our-insights/telehealth-a-quarter-trillion-dollar-post-covid-19-reality
  24. https://ojrd.biomedcentral.com/articles/10.1186/s13023-020-01473-x
  25. https://www.docwirenews.com/future-of-medicine/researchers-use-fitbit-wearable-device-to-monitor-rare-disease-remotely/
  26. https://www.hl7.org/fhir/
  27. https://www.thelancet.com/journals/landig/article/PIIS2589-7500(20)30161-8/fulltext
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