Skip to main content
SpringerLink
Log in

Recommender Systems in Healthcare: A Socio-Technical Systems Approach

  • Chapter
  • First Online:
Recommender Systems for Medicine and Music

Part of the book series: Studies in Computational Intelligence ((SCI,volume 946))

  • 460 Accesses

Abstract

The design, development, implementation, and evaluation of recommender systems (RSs) in healthcare requires a multifaceted and multidisciplinary approach. Healthcare systems by their very nature are complex and dynamic, and their structure and functioning are determined by social values, conventional methods, and economical aspects. This chapter describes healthcare as a socio-technical system comprising of interacting human and technical agents. Thus, any change to the system, such as an introduction of RS tools or methods, requires careful consideration of the technical and social aspects. The chapter presents the main issues which should be considered in the deployment of RS-based tools and techniques in healthcare: (1) adherence to the medical regulations for medical software (e.g., Software as Medical Device), (2) compliance with the medical data privacy, (3) provisioning for transparency and trustworthiness of recommendations, (4) analysis of short-term and long-term ethical issues, such as belief formation and behavior modification, and (5) consideration for the patient empowerment (patient-centered care), doctor-patient relationship (relationship-centered care), and human aspects (human-centered care).

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 139.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 179.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Adomavicius, G., Sankaranarayanan, R., Sen, S., Tuzhilin, A.: Incorporating contextual information in recommender systems using a multidimensional approach. ACM Trans. Inform. Syst. (TOIS) 23(1), 103–145 (2005)

    Article  Google Scholar 

  2. Adomavicius, G., Tuzhilin, A.: Toward the next generation of recommender systems: a survey of the state-of-the-art and possible extensions. IEEE Trans. Knowl. Data Eng. 17(6), 734–749 (2005)

    Article  Google Scholar 

  3. Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender systems handbook, pp. 217–253. Springer (2011)

    Google Scholar 

  4. Balint, M.: The doctor, his patient and the illness (1957)

    Google Scholar 

  5. Baxter, G., Sommerville, I.: Socio-technical systems: from design methods to systems engineering. Interact. Comput. 23(1), 4–17 (2011)

    Article  Google Scholar 

  6. Ben-Shimon, D., Tsikinovsky, A., Rokach, L., Meisles, A., Shani, G., Naamani, L.: Recommender system from personal social networks. In: Advances in Intelligent Web Mastering, pp. 47–55. Springer (2007)

    Google Scholar 

  7. Berwick, D.M.: What ‘patient-centered’should mean: confessions of an extremist: a seasoned clinician and expert fears the loss of his humanity if he should become a patient. Health Aff. 28(Suppl1), w555–w565 (2009)

    Article  MathSciNet  Google Scholar 

  8. Burr, C., Cristianini, N., Ladyman, J.: An analysis of the interaction between intelligent software agents and human users. Minds Mach. 28(4), 735–774 (2018)

    Article  Google Scholar 

  9. Curran, J.: The doctor, his patient and the illness. Bmj 335(7626), 941–941 (2007)

    Article  Google Scholar 

  10. Eccles, D.W., Groth, P.T.: Problem solving systems theory: implications for the design of socio-technological systems. Technol. Instruct. Cognit. Learn. 3(3/4), 323 (2006)

    Google Scholar 

  11. Effken, J.A.: Different lenses, improved outcomes: a new approach to the analysis and design of healthcare information systems. Int. J. Med. Inform. 65(1), 59–74 (2002)

    Article  Google Scholar 

  12. Emery, F.E., Trist, E.L.: Socio-technical systems. management sciences, models and techniques. Churchman C.W. et al (1960)

    Google Scholar 

  13. Epstein, R.M., Street, R.L.: The values and value of patient-centered care (2011)

    Google Scholar 

  14. FDA: U.s. food and drug administration: proposed regulatory framework for modifications to artificial intelligence/machine learning (ai/ml)-based software as medical device (samd)

    Google Scholar 

  15. FDA: U.s. food and drug administration: What are examples of software as a medical device?

    Google Scholar 

  16. Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)

    Article  Google Scholar 

  17. Hägglund, M., Scandurra, I.: A socio-technical analysis of patient accessible electronic health records. Stud. Health Technol. Inform 244, 3–7 (2017)

    Google Scholar 

  18. Hofmann, B.: The myth of technology in health care. Sci. Eng. Eth. 8(1), 17–29 (2002)

    Article  Google Scholar 

  19. Konstan, J.A., Miller, B.N., Maltz, D., Herlocker, J.L., Gordon, L.R., Riedl, J.: Grouplens: applying collaborative filtering to usenet news. Commun. ACM 40(3), 77–87 (1997)

    Article  Google Scholar 

  20. Lamas, E., Salinas, R., Coquedano, C., Simon, M.P., Bousquet, C., Ferrer, M., Zorrilla, S.: The meaning of patient empowerment in the digital age: the role of online patient-communities. Stud. Health Technol. Inform. 244, 43–7 (2017)

    Google Scholar 

  21. Meskó, B., Radó, N., Győrffy, Z.: Opinion leader empowered patients about the era of digital health: a qualitative study. BMJ open 9(3), e025267 (2019)

    Article  Google Scholar 

  22. Milano, S., Taddeo, M., Floridi, L.: Recommender systems and their ethical challenges. AI & SOCIETY pp. 1–11 (2020)

    Google Scholar 

  23. Musco, C., Musco, C., Tsourakakis, C.E.: Minimizing polarization and disagreement in social networks. In: Proceedings of the 2018 World Wide Web Conference, pp. 369–378 (2018)

    Google Scholar 

  24. NM, G.: Framework for designing digital health tools with empathy, journal of participatory medicine

    Google Scholar 

  25. Paraschakis, D.: Recommender systems from an industrial and ethical perspective. In: Proceedings of the 10th ACM conference on recommender systems, pp. 463–466 (2016)

    Google Scholar 

  26. Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)

    Article  Google Scholar 

  27. Ramakrishnan, N., Keller, B.J., Mirza, B.J., Grama, A.Y., Karypis, G.: When being weak is brave: Privacy in recommender systems. arXiv preprint cs/0105028 (2001)

    Google Scholar 

  28. Ramos, G., Boratto, L., Caleiro, C.: On the negative impact of social influence in recommender systems: a study of bribery in collaborative hybrid algorithms. Inform. Proces. Manag. 57(2), 102058 (2020)

    Article  Google Scholar 

  29. Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender systems handbook, pp. 1–35. Springer (2011)

    Google Scholar 

  30. Shortliffe, E.H.: Artificial intelligence in medicine: weighing the accomplishments, hype, and promise. Yearb. Med. Inform. 28(1), 257 (2019)

    Article  Google Scholar 

  31. Sittig, D.F., Singh, H.: A new socio-technical model for studying health information technology in complex adaptive healthcare systems. In: Cognitive informatics for biomedicine, pp. 59–80. Springer (2015)

    Google Scholar 

  32. Weiner, M., Biondich, P.: The influence of information technology on patient-physician relationships. J. General Intern. Med. 21(1), 35–39 (2006)

    Article  Google Scholar 

  33. Wiesner, M., Pfeifer, D.: Health recommender systems: concepts, requirements, technical basics and challenges. Int. J. Environ. Res. Public health 11(3), 2580–2607 (2014)

    Article  Google Scholar 

  34. Xin, Y., et al.: Challenges in recommender systems: scalability, privacy, and structured recommendations. Ph.D. thesis, Massachusetts Institute of Technology (2015)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. The Department of Computing Science, Thompson Rivers University, 805 TRU Way, Kamloops, British Columbia, V2C 0C8, Canada

    Mila Kwiatkowska

Authors
  1. Mila Kwiatkowska
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Mila Kwiatkowska .

Editor information

Editors and Affiliations

  1. Department of Computer Science, University of North Carolina at Charlotte, Charlotte, NC, USA

    Zbigniew W. Ras

  2. Department of Multimedia, Polish-Japanese Academy of Information Technology, Warsaw, Poland

    Alicja Wieczorkowska

  3. Department of Medical Informatics, Faculty of Medicine, Shimane University, Izumo, Japan

    Shusaku Tsumoto

Rights and permissions

Reprints and permissions

Copyright information

© 2021 Springer Nature Switzerland AG

About this chapter

Check for updates. Verify currency and authenticity via CrossMark

Cite this chapter

Kwiatkowska, M. (2021). Recommender Systems in Healthcare: A Socio-Technical Systems Approach. In: Ras, Z.W., Wieczorkowska, A., Tsumoto, S. (eds) Recommender Systems for Medicine and Music. Studies in Computational Intelligence, vol 946. Springer, Cham. https://doi.org/10.1007/978-3-030-66450-3_2

Download citation

Publish with us

Policies and ethics

Search

Navigation