Advertisement

Software Requirements Engineering in Digital Healthcare: A Case Study of the Diagnosis and Monitoring of Autism Spectrum Disorders in Children in the UK’s National Health Service

  • Catherine Tryfona
  • Tom Crick
  • Ana Calderon
  • Simon Thorne
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10287)

Abstract

A major issue in designing digital healthcare software solutions is ensuring they meet the clinical needs and requirements of key services, as well as the expectations of various healthcare professionals. Modern software requirements engineering must be adapted to cater for this demand; we argue that traditional (and popular) requirements engineering processes – particularly in relation to the elicitation and validation of key requirements – may not be the most appropriate within the context of a multi-disciplinary team of healthcare professionals. Successful software requirements engineering is vital in ensuring that digital healthcare solutions fulfill expectations and meet the clinical needs; we thus propose that new methods of gathering requirements in the ‘third space’ are needed. This paper draws on a case study of the multi-disciplinary team of healthcare professionals involved in the diagnosis and support of autism spectrum disorders (ASD) in young children within the UK’s National Health Service (NHS). It is worth noting that, in the context of our case study, requirements engineering is an iterative process and requires the input of numerous stakeholders from often stretched and fragmented services.

Keywords

Autism spectrum disorder M-Health User behaviour analysis Software engineering Requirements engineering 

References

  1. 1.
    Thummler, C.: Digital Health. In: Fricker, S., Thummler, C., Gavras, A. (eds.) Requirements Engineering for Digital Health. London, pp. 1–22 (2015)Google Scholar
  2. 2.
    Commission, E.: Health, Demographic Change and Wellbeing. European Commission: Horizon 2020 (2015). http://ec.europa.eu/programmes/horizon2020/en/h2020-section/health-demographic-change-and-wellbeing. Accessed 20 Apr 2015
  3. 3.
    Alepis, E., Lambrinidis, C.: M-health: supporting automated diagnosis and electronic health records. SpringerPlus 2(1), 103–111 (2013). doi: 10.1186/2193-1801-2-103 CrossRefGoogle Scholar
  4. 4.
    Istepanian, R.S.H.: m-health : a decade of evolution and impact on services and global health (2010)Google Scholar
  5. 5.
    Tryfona, C., Oatley, G., Calderon, A., Thorne, S.: M-health solutions to support the National Health Service in the diagnosis and monitoring of autism spectrum disorders in young children. In: Antona, M., Stephanidis, C. (eds.) UAHCI 2016. LNCS, vol. 9739, pp. 249–256. Springer, Cham (2016). doi: 10.1007/978-3-319-40238-3_24 CrossRefGoogle Scholar
  6. 6.
    National Autistic Society (2015). http://www.autism.org.uk/about.aspx. Accessed 28 Feb 2016
  7. 7.
    Corsello, C.: Diagnositic instruments in autistic spectrum disorders. In: Volkmar, F.R. (ed.) Encyclopedia of Autism Spectrum Disorders, pp. 919–926. Springer, Heidelberg (2013)Google Scholar
  8. 8.
    Centers for Disease Control and Pre. Autism Spectrum Disorder (ASD) (2015). http://www.cdc.gov/ncbddd/autism/facts.html. Accessed 25 Apr 2015
  9. 9.
    Taylor, C.M., Vehorn, A., Noble, H., Weitlauf, A.S., Warren, Z.E.: Brief report: can metrics of reporting bias enhance early autism screening measures? J. Autism Dev. Dis. 44, 2375–2380 (2014). doi: 10.1007/s10803-014-2099-5 CrossRefGoogle Scholar
  10. 10.
    Filipek, P.A., et al.: The screening and diagnosis of autistic spectrum disorders. J. Autism Dev. Dis. 29(6), 439–484 (1999)CrossRefGoogle Scholar
  11. 11.
    Bishop, S., Luyster, R., Richler, J., Lord, C.: Diagnostic assessments. In: Chawarska, K., Klin, A., Volkmar, F. (eds.) Autism Spectrum Disorders in Infants and Toddlers, pp. 23–43. New York (2008)Google Scholar
  12. 12.
    Abowd, G.: Pilot evaluation of a novel telemedicine platform to support diagnostic assessment for autism spectrum disorder. In: ITASD 2014 Paris Conference, Paris (2014)Google Scholar
  13. 13.
    Anzulewicz, A.: HARIMATA-Embracing mobile devices for early diagnosis of autism spectrum disorders. In: ITASD 2014 Paris Conference, France (2014). http://www.dailymotion.com/video/x27j03l_harimata-embracing-mobile-devices-for-early-diagnosis-of-autism-spectrum-disorders_webcam
  14. 14.
    Billeci, L.: Eye-tracking technology to assess joint attention deficit in children with autism spectrum disorders. In: ITASD 2014 Paris Conference, France (2014). http://www.dailymotion.com/video/x27izmy_eye-tracking-technology-to-assess-joint-attention-deficit-in-children-with-autism-spectrum-disorders_webcam
  15. 15.
    BSI Standards. PD ISO/BSI Standards Publication Software Engineering—Guide to the Software Engineering Body of Knowledge (SWEBOK) (2016)Google Scholar
  16. 16.
    Loniewski, G., Insfrán Pelozo, C.E.: OpenUP/MDRE: A Model-Driven Requirements Engineering Approach for Health-Care Systems. Valencia University (2011)Google Scholar
  17. 17.
    Gorschek, T., Tempero, E., Angelis, L.: On the use of software design models in software development practice: an empirical investigation. J. Syst. Softw. 95, 176–193 (2014). doi: 10.1016/j.jss.2014.03.082 CrossRefGoogle Scholar
  18. 18.
    Wiegers, K., Beatty, J.: Software Requirements, 3rd edn. Microsoft Press, Washington (2013)Google Scholar
  19. 19.
    Sørby, I.D.: Observing and analysing clinicians’ information and communication behaviour: an approach to requirements engineering for mobile health information systems (2007)Google Scholar
  20. 20.
    NHS: Asperger’s not in DSM-5 mental health manual (2012). http://www.nhs.uk/news/2012/12December/Pages/Aspergers-dropped-from-mental-health-manual-DSM-5.aspx. Accessed 24 Apr 2015
  21. 21.
    Ousley, O., Cermak, T.: Autism spectrum disorder: defining dimensions and subgroups. Curr. Dev. Dis. Rep. 1, 20–28 (2013). http://link.springer.com/10.1007/s40474-013-0003-1
  22. 22.
    Boucenna, S., et al.: Interactive technologies for autistic children: a review. Cognitive Computation 6, 722–740 (2014)CrossRefGoogle Scholar
  23. 23.
    Fricker, S.: Requirements engineering: best practice. In: Fricker, S., Thummler, C., Gavras, A. (eds.) Requirements Engineering for Digital Health, pp. 25–43. Springer, New York (2015)Google Scholar
  24. 24.
    Bourquard, K., Gall, F., Cousin, P.: Standards for interoperability in digital health: selection and implementation in an eHealth project. In: Fricker, S.A., Thümmler, C., Gavras, A. (eds.) Requirements Engineering for Digital Health, pp. 95–115. Springer, Cham (2015). doi: 10.1007/978-3-319-09798-5_5 Google Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  • Catherine Tryfona
    • 1
  • Tom Crick
    • 1
  • Ana Calderon
    • 1
  • Simon Thorne
    • 1
  1. 1.Cardiff Metropolitan UniversityCardiffUK

Personalised recommendations