Skip to main content

Intelligent Systems in Health Care: A Socio-Technical View

  • Conference paper
  • First Online:
Reshaping Accounting and Management Control Systems

Abstract

This chapter reflects on the relationship between various stakeholders in the health-care industry and intelligent medical systems. It takes into consideration the potential impact that intelligent systems have on health care. The aim of the chapter is to emphasise a set of decisive factors for the successful deployment of intelligent systems in health care including the individual needs of patients and medical staff. The motivation for this study was the publicity and investment that intelligent agents like Watson have benefitted from since the outset of their trial deployments in health-care organisations, which have preceded doctors’ feedback. In this chapter, we discuss some incentives to use intelligent medical systems and the ethical considerations. Potential roles of intelligent systems in health care are explored from a socio-technical perspective. Additionally, potential decision-makers and their responsibilities in assessing the medical personnel’s attitude towards the intelligent systems before their final deployment are discussed. The conclusion outlines limitations of both human clinicians and intelligent agents and how they can work together to overcome them.

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

Access this chapter

Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 169.99
Price excludes VAT (USA)
  • Compact, lightweight 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. Patel, V. L., Shortliffe, E. H., Stefanelli, M., Szolovits, P., Berthold, M. R., Bellazzi, R., et al. (2009). Position paper: The coming of age of artificial intelligence in medicine. Artificial Intelligence in Medicine, 46, 5–17. doi:10.1016/j.artmed.2008.07.017.

    Article  Google Scholar 

  2. Checkland, P., & Holwell, S. (1998). Information, systems and information systems: Making sense of the field. Chichester: Wiley.

    Google Scholar 

  3. Mumford, E. (1983—Revised 2013). Designing human systems for new technology: The ETHICS method. Manchester: Manchester Business School.

    Google Scholar 

  4. Castaneda, C., Nalley, K., Mannion, C., Bhattacharyya, P., Blake, P., Pecora, A., et al. (2015). Clinical decision support systems for improving diagnostic accuracy and achieving precision medicine. Journal of Clinical Bioinformatics, 5(1), 4.

    Article  Google Scholar 

  5. Parasuraman, R., & Manzey, D. H. (2010). Complacency and bias in human use of automation: An attentional integration. Human Factors: The Journal of the Human Factors and Ergonomics Society, 52(3), 381–410.

    Article  Google Scholar 

  6. IBM. (2015). IBM’s Watson supercomputer to speed up cancer care. Retrieved from BBC News website: http://www.bbc.co.uk/news/technology-32607688

  7. Nilsson, N. J. (2014). Principles of artificial intelligence. Los Altos, CA: Morgan Kaufmann.

    Google Scholar 

  8. Liebowitz, J. (1997). The handbook of applied expert systems. Boca Raton, FL: CRC Press.

    Google Scholar 

  9. Stubbs, E. (2015). A season of major incidents.

    Google Scholar 

  10. Porter, M. E., & Lee, T. H. (2013, October). The strategy that will fix health care. Harvard Business Review. Retrieved from https://hbr.org/2013/10/the-strategy-that-will-fix-health-care/

  11. Wanjiku, R. (2014). IBM pushes Watson’s role in healthcare. Retrieved from IT World website: http://www.itworld.com/article/2698674/hardware/ibm-pushes-watson-s-role-in-healthcare.html

  12. Haddad, P., Gregory, M., & Wickramasinghe, N. (2014). Evaluating business value of IT in healthcare in Australia: The case of an intelligent operational planning support tool solution. Submitted Bled econference, Bled June.

    Google Scholar 

  13. Elina, V., Juhani, L., Tiina, T. J., Kari, M., Irma, V., Mauri, I., et al. (2006). Doctor-managers as decision makers in hospitals and health centres. Journal of Health Organization and Management, 20(2), 85–94.

    Article  Google Scholar 

  14. Prahalad, C. K., & Ramaswamy, V. (2012). The new frontier of experience innovation. Image.

    Google Scholar 

  15. Cabitza, F., & Simone, C. (2012). “Whatever works”: Making sense of information quality. In G. Viscusi, G. M. Campagnolo, & Y. Curz (Eds.), Phenomenology, organizational politics, and it design: The social study of information systems (p. 79). Hershey, PA: Information Science Reference.

    Chapter  Google Scholar 

  16. Husain, I. (2011). Why IBM’s artificial intelligence “Watson” could not replace a physician. Retrieved from: http://www.imedicalapps.com/2011/02/ibm-watson-replace-physician-artificial-intelligence/

  17. Allain, J. S. (2012). From Jeopardy to Jaundice: The medical liability implications of Dr. Watson and other artificial intelligence systems. Louisiana Law Review, 73, 1049.

    Google Scholar 

  18. HP (2014). Kainos Harnesses HP IDOL for next-generation healthcare analytics. Retrieved from https://www.kainos.com/kainos-harnesses-hp-idol-next-generation-healthcare-analytics/

  19. Ubpin, B. (2013, August 2). IBM’s Watson gets its first piece of business in healthcare. Forbes. Retrieved from http://www.forbes.com/sites/bruceupbin/2013/02/08/ibms-watson-gets-its-first-piece-of-business-in-healthcare/

  20. Cohn, J. (2013). The robot will see you now. Retrieved from: http://www.theatlantic.com/magazine/archive/2013/03/the-robot-will-see-you-now/309216/

  21. Carr, N. G. (2015). The glass cage: Where automation is taking us. London: The Bodley Head.

    Google Scholar 

  22. Freudenheim, M. (2012). The ups and downs of electronic medical records. Retrieved from http://www.nytimes.com/2012/10/09/health/the-ups-and-downs-of-electronic-medical-records-the-digital-doctor.html?_r=0

  23. Keim, B. (2012). Paging Dr. Watson: Artificial intelligence as a prescription for health care. Retrieved from http://www.wired.com/2012/10/watson-for-medicine/

  24. Simonite, T. (2014). IBM aims to make medical expertise a commodity. Retrieved from: http://www.technologyreview.com/news/529021/ibm-aims-to-make-medical-expertise-a-commodity/

  25. Bednar, P., Imrie, P., & Welch, C. (2014). Personalized support with ‘little’data. In B. Bergvall-Kåreborn & P. A. Nielsen (Eds.), Creating value for all through IT (pp. 355–358). Berlin: Springer.

    Chapter  Google Scholar 

  26. Imrie, P., & Bednar, P. (2014). End user effects of centralized data control. ItAIS2014: XI Conference of the Italian Chapter of AIS, Digital Innovation and Inclusive Knowledge in Times of Change. ItAIS, University of Genova.

    Google Scholar 

  27. Payne, K. F. B., Wharrad, H., & Watts, K. (2012). Smartphone and medical related App use among medical students and junior doctors in the United Kingdom (UK): A regional survey. BMC Medical Informatics and Decision Making, 12(1), 121.

    Article  Google Scholar 

  28. Gaudin, S. (2014). Cleveland Clinic uses IBM’s Watson in the cloud to fight cancer. Retrieved from http://www.computerworld.com/article/2840226/cleveland-clinic-uses-ibms-watson-in-the-cloud-to-fight-cancer.html.

    Google Scholar 

  29. The computing system that won ‘Jeopardy!’ is helping doctors fight cancer. (2015). Retrieved from: http://www.businessinsider.in/The-computing-system-that-won-Jeopardy-is-helping-doctors-fight-cancer/articleshow/46124256.cms

  30. Ferrucci, D. A. (2012, May–June). Introduction to “This is Watson”. IBM Journal of Research and Development, 56(3.4), 1:1,1:15.

    Google Scholar 

  31. Truog, R. D. (2012). Patients and doctors—The evolution of a relationship. New England Journal of Medicine, 366(7), 581–585.

    Article  Google Scholar 

  32. Dilsizian, S. E., & Siegel, E. L. (2014). Artificial intelligence in medicine and cardiac imaging: Harnessing big data and advanced computing to provide personalized medical diagnosis and treatment. Current Cardiology Reports, 16(1), 1–8.

    Article  Google Scholar 

  33. Hospital inpatient care: Almost 900 more admissions per day compared to previous year. (n.d.). Retrieved from: http://www.hscic.gov.uk/article/6053/Hospital-inpatient-care-almost-900-more-admissions-per-day-compared-to-previous-year

  34. Greenhalgh, T., Stramer, K., Bratan, T., Byrne, E., Mohammad, Y., & Russell, J. (2008). Introduction of shared electronic records: Multi-site case study using diffusion of innovation theory. BMJ, 337, a1786.

    Article  Google Scholar 

  35. Khosla, V. (2012). Technology will replace 80% of what doctors do. Retrieved from Fortune website: http://fortune.com/2012/12/04/technology-will-replace-80-of-what-doctors-do/

  36. Bednar, P., Welch, C., & Imrie, P. (2014). Supporting business decision-making: One professional at a time. DSS 2.0–Supporting Decision Making with New Technologies, 261, 471–482.

    Google Scholar 

  37. Arnaout, R. (2012). Elementary, my dear doctor Watson. Clinical Chemistry, 58(6), 986–988.

    Article  Google Scholar 

  38. Charani, E., Castro-Sanchez, E., Sevdalis, N., Kyratsis, Y., Drumright, L., Shah, N., et al. (2013). Understanding the determinants of antimicrobial prescribing within hospitals: The role of “prescribing etiquette”. Clinical Infectious Diseases, 57(2), 188–196.

    Article  Google Scholar 

  39. Alvesson, M., & Spicer, A. (2012). A stupidity-based theory of organizations. Journal of Management Studies, 49(7), 1072–1194. http://dx.doi.org/10.1111/j.1467-6486.2012.01072.

    Article  Google Scholar 

  40. Eberhardt, J., Bilchik, A., & Stojadinovic, A. (2012). Clinical decision support systems: Potential with pitfalls. Journal of Surgical Oncology, 105(5), 502–510.

    Article  Google Scholar 

  41. Global health workforce shortage to reach 12.9 million in coming decades. (2013). Retrieved from WHO website: http://www.who.int/mediacentre/news/releases/2013/health-workforce-shortage/en/

  42. England, K., & Henry, C. (2013). Care work, migration and citizenship: International nurses in the UK. Social and Cultural Geography, 14(5), 558–574.

    Article  Google Scholar 

  43. Duerr-Specht, M., Goebel, R., & Holzinger, A. (2015). Medicine and health care as a data problem: Will computers become better medical doctors? In A. Holzinger, C. Rocker, & M. Ziefle (Eds.), Smart health (pp. 21–39). Cham: Springer.

    Google Scholar 

  44. Bednar, P., & Welch, C. (2008). Bias, misinformation and the paradox of neutrality. Informing Science, 11, 85–106.

    Google Scholar 

  45. Luxton, D. D. (2014). Recommendations for the ethical use and design of artificial intelligent care providers. Artificial Intelligence in Medicine, 62(1), 1–10.

    Article  Google Scholar 

  46. Edney, A. (2015). This medical supercomputer isn’t a pacemaker, IBM Tells Congress. Retrieved from: http://www.bloomberg.com/news/articles/2015-01-29/this-medical-supercomputer-isn-t-a-pacemaker-ibm-tells-congress

  47. Searle, J. (2012). Watson doesn’t know it won on “Jeopardy!”. The Wall Street Journal, 23, 15A.

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Andreea-Roxanna Obreja .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2017 Springer International Publishing AG

About this paper

Cite this paper

Obreja, AR., Ross, P., Bednar, P. (2017). Intelligent Systems in Health Care: A Socio-Technical View. In: Corsi, K., Castellano, N., Lamboglia, R., Mancini, D. (eds) Reshaping Accounting and Management Control Systems. Lecture Notes in Information Systems and Organisation, vol 20. Springer, Cham. https://doi.org/10.1007/978-3-319-49538-5_14

Download citation

Publish with us

Policies and ethics