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).
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References
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)
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)
Adomavicius, G., Tuzhilin, A.: Context-aware recommender systems. In: Recommender systems handbook, pp. 217–253. Springer (2011)
Balint, M.: The doctor, his patient and the illness (1957)
Baxter, G., Sommerville, I.: Socio-technical systems: from design methods to systems engineering. Interact. Comput. 23(1), 4–17 (2011)
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)
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)
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)
Curran, J.: The doctor, his patient and the illness. Bmj 335(7626), 941–941 (2007)
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)
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)
Emery, F.E., Trist, E.L.: Socio-technical systems. management sciences, models and techniques. Churchman C.W. et al (1960)
Epstein, R.M., Street, R.L.: The values and value of patient-centered care (2011)
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)
FDA: U.s. food and drug administration: What are examples of software as a medical device?
Goldberg, D., Nichols, D., Oki, B.M., Terry, D.: Using collaborative filtering to weave an information tapestry. Commun. ACM 35(12), 61–70 (1992)
Hägglund, M., Scandurra, I.: A socio-technical analysis of patient accessible electronic health records. Stud. Health Technol. Inform 244, 3–7 (2017)
Hofmann, B.: The myth of technology in health care. Sci. Eng. Eth. 8(1), 17–29 (2002)
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)
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)
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)
Milano, S., Taddeo, M., Floridi, L.: Recommender systems and their ethical challenges. AI & SOCIETY pp. 1–11 (2020)
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)
NM, G.: Framework for designing digital health tools with empathy, journal of participatory medicine
Paraschakis, D.: Recommender systems from an industrial and ethical perspective. In: Proceedings of the 10th ACM conference on recommender systems, pp. 463–466 (2016)
Pazzani, M., Billsus, D.: Learning and revising user profiles: the identification of interesting web sites. Mach. Learn. 27(3), 313–331 (1997)
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)
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)
Ricci, F., Rokach, L., Shapira, B.: Introduction to recommender systems handbook. In: Recommender systems handbook, pp. 1–35. Springer (2011)
Shortliffe, E.H.: Artificial intelligence in medicine: weighing the accomplishments, hype, and promise. Yearb. Med. Inform. 28(1), 257 (2019)
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)
Weiner, M., Biondich, P.: The influence of information technology on patient-physician relationships. J. General Intern. Med. 21(1), 35–39 (2006)
Wiesner, M., Pfeifer, D.: Health recommender systems: concepts, requirements, technical basics and challenges. Int. J. Environ. Res. Public health 11(3), 2580–2607 (2014)
Xin, Y., et al.: Challenges in recommender systems: scalability, privacy, and structured recommendations. Ph.D. thesis, Massachusetts Institute of Technology (2015)
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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
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