Analisi dei fattori determinanti l’adozione dell’Intelligenza Artificiale in sanità

Titolo Rivista MECOSAN
Autori/Curatori Francesco De Domenico, Guido Noto, Carlo Vermiglio
Anno di pubblicazione 2024 Fascicolo 2023/128
Lingua Italiano Numero pagine 26 P. 135-160 Dimensione file 0 KB
DOI 10.3280/mesa2023-128oa18595
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FrancoAngeli è membro della Publishers International Linking Association, Inc (PILA)associazione indipendente e non profit per facilitare (attraverso i servizi tecnologici implementati da CrossRef.org) l’accesso degli studiosi ai contenuti digitali nelle pubblicazioni professionali e scientifiche

Lo studio si concentra sull’adozione di strumenti di intelligenza artificiale (IA) nelle aziende sanitarie e analizza le determinanti dell’adozione da parte dei professionisti sanitari. Sebbene l’adozione di nuove tecnologie, e in particolare di tecnologie emergenti come l’IA, possa offrire soluzioni innovative per migliorare la salute dei pazienti e l’efficienza delle aziende sanitarie, la loro adozione può essere ostacolata dall’emergere di possibili resistenze organizzative, individuali e professionali. Sulla base del TOE framework e mediante l’utilizzo di NVivo sono state condotte e analizzate alcune interviste semi-strutturate con farmacisti ospedalieri italiani.Il lavoro fornisce nuove evidenze sull’adozione di tecnologie emergenti nel settore sanitario e identifica le principali determinanti che i decisori aziendali dovrebbero considerare al fine di promuovere l’implementazione di tecnologie di IA. I risultati ottenuti forniscono informazioni utili ai produttori di tecnologie, ai policy makers e ai manager nella formulazione di strategie più idonee per facilitare l’adozione di tali tecnologie nel contesto sanitario.;

Keywords:; Intelligenza artificiale; Determinanti dell’adozione; Aziende sanitarie; Farmacisti ospedalieri; NVivo; Technology-Organizational-Environment framework

  1. Ogrezeanu A. (2015). Models of technology adoption: an integrative approach. Network Intelligence Studies, 3(05), 55-67.
  2. Aboelmaged M. G. (2014). Predicting E-Readiness at Firm-Level: An Analysis of Technological, Organizational and Environmental (TOE) Effects on e-Maintenance Readiness in Manufacturing Firms. International Journal of Information Management, 34(5): 639-51. DOI: 10.1016/J.IJINFOMGT.2014.05.002.
  3. Aboelmaged M., and Gharib H. (2018). RFID Application in Patient and Medical Asset Operations Management: A Technology, Organizational and Environmental (TOE) Perspective into Key Enablers and Impediments. International Journal of Medical Informatics, 118: 58-64. DOI: 10.1016/J.IJMEDINF.2018.07.009.
  4. Alhashmi S. F., Salloum S. A., and Mhamdi C. (2019). Implementing Artificial Intelligence in the United Arab Emirates Healthcare Sector: An Extended Technology Acceptance Model. Internationalnternational Journal of Information Technology and Language Studies, 3(3): 27-42.
  5. Alhashmi Shaikha F. S., Alshurideh M., Al Kurdi B., and Salloum S. A. (2020). A Systematic Review of the Factors Affecting the Artificial Intelligence Implementation in the Health Care Sector, 37-49. DOI: 10.1007/978-3-030-44289-7_4.
  6. Alshamaila Y., Papagiannidis S., and Li F. (2013). Cloud Computing Adoption by SMEs in the North East of England: A Multi-Perspective Framework. Journal of Enterprise Information Management, 26(3): 250-75. DOI: 10.1108/17410391311325225.
  7. Armeni P., Meregaglia M., Borsoi L., Callea G., Torbica A., Benazzo F., & Tarricone R. (2023). Collecting Physicians’ Preferences on Medical Devices: Are We Doing It Right? Evidence from Italian Orthopaedists Using 2 Different Stated Preference Methods. Medical Decision Making. DOI: 10.1177/0272989X231201805.
  8. Backman R., Bayliss S., Moore D., and Litchfield I. (2017). Clinical Reminder Alert Fatigue in Healthcare: A Systematic Literature Review Protocol Using Qualitative Evidence. Systematic Reviews, 6(1). DOI: 10.1186/S13643-017-0627-Z.
  9. Bajgain B., Lorenzetti D., Lee J., and Sauro K. (2023). Determinants of Implementing Artificial Intelligence-Based Clinical Decision Support Tools in Healthcare: A Scoping Review Protocol. BMJ Open, 13(2). DOI: 10.1136/BMJOPEN-2022-068373.
  10. Bandara W., Furtmueller E., Gorbacheva E., Miskon S., & Beekhuyzen J. (2015). Achieving rigor in literature reviews: Insights from qualitative data analysis and tool-support. Communications of the Association for Information systems, 37(1), 8.
  11. Barth S., de Jong M. D. T., Junger M., Hartel P. H., and Roppelt J. C. (2019). Putting the Privacy Paradox to the Test: Online Privacy and Security Behaviors among Users with Technical Knowledge, Privacy Awareness, and Financial Resources. Telematics and Informatics, 41: 55-69. DOI: 10.1016/J.TELE.2019.03.003.
  12. Bazeley P., and Jackson K. (2013). Qualitative Data Analysis with NVivo (2nd Ed.). Qualitative Research in Psychology, 12(4): 492-94. DOI: 10.1080/14780887.2014.992750.
  13. Bedué P., and Fritzsche A. (2022). Can We Trust AI? An Empirical Investigation of Trust Requirements and Guide to Successful AI Adoption. Journal of Enterprise Information Management, 35(2): 530-49. DOI: 10.1108/JEIM-06-2020-0233.
  14. Bell E., Bryman A., & Harley B. (2022). Business research methods. Oxford university press.
  15. Benkirane R. R., R-Abouqal R., Haimeur C. C., Ec. Cherif El Kettani S. S. S., Azzouzi A. A., M’Daghri A. A., Amal A. Thimou A., Nejmi, Wajdi M. M., Maazouzi W., Madani N. N., Edwards I. R, and Soulaymani R. R. (2009). Incidence of Adverse Drug Events and Medication Errors in Intensive Care Units: A Prospective Multicenter Study. Journal of Patient Safety, 5(1): 16-22. DOI: 10.1097/PTS.0B013E3181990D51.
  16. Bohr A., and Memarzadeh K. (2020). The Rise of Artificial Intelligence in Healthcare Applications. Artificial Intelligence in Healthcare, 25. DOI: 10.1016/B978-0-12-818438-7.00002-2.
  17. Boonstra A., and Broekhuis M. (2010a). Barriers to the Acceptance of Electronic Medical Records by Physicians from Systematic Review to Taxonomy and Interventions. MIT Economics, 10. DOI: 10.1186/1472-6963-10-231.
  18. Boonstra A., and Broekhuis M. (2010b). Barriers to the Acceptance of Electronic Medical Records by Physicians from Systematic Review to Taxonomy and Interventions. BMC Health Services Research, 10. DOI: 10.1186/1472-6963-10-231.
  19. Bracci E. (2023). The loopholes of algorithmic public services: an “intelligent” accountability research agenda. Accounting, Auditing & Accountability Journal, 36(2), 739-763.
  20. Bronsoler A., Doyle J., Schell E. H., and Van Reenen J. (2020). The Impact of New Technology on the Healthcare Workforce. MIT Economics.
  21. Cao Q., Jones D. R., and Sheng H. (2014). Contained Nomadic Information Environments: Technology, Organization, and Environment Influences on Adoption of Hospital RFID Patient Tracking. Information and Management, 51(2): 225-39. DOI: 10.1016/j.im.2013.11.007.
  22. Chau P. Y. K., and Tam K. Y. (2000). Organizational Adoption of Open Systems: A ‘technology-Push, Need-Pull’ Perspective. Information and Management, 37(5): 229-39. DOI: 10.1016/S0378-7206(99)00050-6.
  23. Chau P. Y. K., and Yan Tam K. (1997). Factors Affecting the Adoption of Open Systems: An Exploratory Study. MIS Quarterly: Management Information Systems, 21(1): 1-20. DOI: 10.2307/249740.
  24. Chaudhry B., Wang J., Wu S., Maglione M., Mojica W., Roth E., Morton S. C., and Shekelle P. G. (2006). Systematic Review: Impact of Health Information Technology on Quality, Efficiency, and Costs of Medical Care. Annals of Internal Medicine, 144(10): 742-52. DOI: 10.7326/0003-4819-144-10-200605160-00125.
  25. Classen D. C., Pestotnik S. L., Evans R. S., Lloyd J. F., and Burke J. P. (1997). Adverse Drug Events in Hospitalized Patients. Excess Length of Stay, Extra Costs, and Attributable Mortality | Digital Healthcare Research. JAMA, 277(4): 301-6.
  26. Compeau D. R., and Higgins C. A. (1995). Computer Self-Efficacy: Development of a Measure and Initial Test. MIS Quarterly: Management Information Systems, 19(2): 189-210. DOI: 10.2307/249688.
  27. Cooper R. B., Zmud R. W., Cooper R. B., and Zmud R. W. (1990). Information Technology Implementation Research: A Technological Diffusion Approach. Management Science, 36(2): 123-39. DOI: 10.1287/MNSC.36.2.123.
  28. Damali U., Kocakulah M., and Ozkul A. S. (2021). Investigation of Cloud ERP Adoption in the Healthcare Industry Through Technology-Organization-Environment (TOE) Framework: Qualitative Study. Int. J. Heal. Inf. Syst. Informatics, 16(4): 1-14. DOI: 10.4018/IJHISI.289463.
  29. Dash S., Chakraborty C., Giri S. K., and Kumar Pani S. (2021). Intelligent Computing on Time-Series Data Analysis and Prediction of COVID-19 Pandemics. Pattern Recognit Lett, 151: 69-75. DOI: 10.1016/J.PATREC.2021.07.027.
  30. Davis F. D. (1985). A Technology Acceptance Model for Empirically Testing New End-User Information Systems: Theory and Results. Massachusetts Institute of Technology.
  31. DiMaggio P. J., & Powell W. W. (1983). The iron cage revisited: Institutional isomorphism and collective rationality in organizational fields. American sociological review, 147-160.
  32. DePietro R., Wiarda E., & Fleischer M. (1990). The context for change: Organization, technology and environment. In Tornatzky L.G. & Fleischer (Eds). The Process of Technological Innovation. Lexington, MA: Lexington Books, 151-175.
  33. Figueroa C. A., Harrison R., Chauhan A., and Meyer L. (2019). Priorities and Challenges for Health Leadership and Workforce Management Globally: A Rapid Review. BMC Health Services Research, 19(1). DOI: 10.1186/S12913-019-4080-7.
  34. Gagnon M. P., Desmartis M., Labrecque M., Car J., Pagliari C., Pluye P., Frémont P., Gagnon J., Tremblay N., and Légaré F. (2012). Systematic Review of Factors Influencing the Adoption of Information and Communication Technologies by Healthcare Professionals. Journal of Medical Systems, 36(1): 241-77. DOI: 10.1007/S10916-010-9473-4.
  35. Gaube S., Suresh H., Raue M., Merritt A., Berkowitz S. J., Lermer E., Coughlin J. F., Guttag J. V., Colak E., and Ghassemi M. (2021). Do as AI Say: Susceptibility in Deployment of Clinical Decision-Aids. Npj Digital Medicine 2021 4:1, 4(1): 1-8. DOI: 10.1038/s41746-021-00385-9.
  36. Gibbs J. L., and Kraemer K. L. (2004). A Cross-Country Investigation of the Determinants of Scope of E-Commerce Use: An Institutional
  37. Approach AuthorS. Electronic Markets, 14(2): 124-37. DOI: 10.1080/10196780410001675077.
  38. Gibbs G. R. (2007). Thematic coding and categorizing. Analyzing Qualitative Data, 703, 38-56.
  39. Goldman D. P., Shang B., Bhattacharya J., Garber A. M., Hurd M., Joyce G. F., Lakdawalla D. N., Panis C., and Shekelle P. G. (2005). Consequences of Health Trends and Medical Innovation for the Future Elderly. Health Affairs (Project Hope), 24 Suppl 2(Suppl 2). DOI: 10.1377/HLTHAFF.W5.R5.
  40. Goldstein S. Meyer, Ward P. T., Leong G. K., and Butler T. W. (2002). The Effect of Location, Strategy, and Operations Technology on Hospital Performance. Journal of Operations Management, 20(1): 63-75. DOI: 10.1016/S0272-6963(01)00081-X.
  41. Greenhalgh T., Macfarlane G. R. F., Bate P., and Kyriakidou O. (2004). Diffusion of Innovations in Service Organizations: Systematic Review and Recommendations. The Milbank Quarterly, 82(4): 581-629. DOI: 10.1111/J.0887-378X.2004.00325.X.
  42. Grodal S., Anteby M., & Holm A. L. (2021). Achieving rigor in qualitative analysis: The role of active categorization in theory building. Academy of Management Review, 46(3): 591-612.
  43. Guo J., and Li B. (2018). The Application of Medical Artificial Intelligence Technology in Rural Areas of Developing Countries. Health Equity, 2(1): 174. DOI: 10.1089/HEQ.2018.0037.
  44. Gutierrez A., Boukrami E., and Lumsden R. (2015). Technological, Organisational and Environmental Factors Influencing Managers’ Decision to Adopt Cloud Computing in the UK. Journal of Enterprise Information Management, 28(6): 788-807. DOI: 10.1108/JEIM-01-2015-0001/FULL/XML.
  45. Hennemann S., Beutel M. E., and Zwerenz R. (2017). Ready for EHealth? Health Professionals’ Acceptance and Adoption of EHealth Interventions in Inpatient Routine Care. Journal of Health Communication, 22(3): 274-84. DOI: 10.1080/10810730.2017.1284286.
  46. Herzlinger R. (2006). Why Innovation in Health Care Is so Hard – PubMed. Harvard Business Review, 84(5): 58-66.
  47. Iacovou C. L., Benbasat I., and Dexter A. S. (1995). Electronic Data Interchange and Small Organizations: Adoption and Impact of Technology. MIS Quarterly: Management Information Systems, 19(4): 465-85. DOI: 10.2307/249629.
  48. Ilin V., Ivetić J., and Simić D. (2017). Understanding the Determinants of E-Business Adoption in ERP-Enabled Firms and Non-ERP-Enabled Firms: A Case Study of the Western Balkan Peninsula. Technological Forecasting and Social Change, 125: 206-23. DOI: 10.1016/J.TECHFORE.2017.07.025.
  49. Javaid M., Haleem A., Singh R. P., Suman R., and Rab S. (2022). Significance of Machine Learning in Healthcare: Features, Pillars and Applications. International Journal of Intelligent Networks, 3: 58-73. DOI: 10.1016/J.IJIN.2022.05.002.
  50. Jeyaraj A., Rottman J. W., & Lacity M. C. (2006). A Review of the Predictors, Linkages, and Biases in IT Innovation Adoption Research. Journal of Information Technology, 21(1): 1-23. DOI: 10.1057/palgrave.jit.2000056.
  51. Jha A. K., DesRoches C. M., Campbell E. G., Donelan K., Rao S. R., Ferris T. G., Shields A., Rosenbaum S., and Blumenthal D. (2009). Use of Electronic Health Records in U.S. Hospitals. The New England Journal of Medicine, 360(16): 1628-38. DOI: 10.1056/NEJMSA0900592.
  52. Kimberly J. R., and Evanisko M. J. (1981). Organizational Innovation: The Influence of Individual, Organizational, and Contextual Factors on Hospital Adoption of Technological and Administrative Innovations. Academy of Management Journal, 24(4): 689-713. DOI: 10.5465/256170.
  53. Klumpp M., Hintze M., Immonen M., Ródenas-Rigla F., Pilati F., Aparicio-Martínez F., Çelebi D., Liebig T., Jirstrand M., Urbann O., Hedman M., Lipponen J. A., Bicciato S., Radan A. P., Valdivieso B., Thronicke W., Gunopulos D., and Delgado-Gonzalo R. (2021). Artificial Intelligence for Hospital Health Care: Application Cases and Answers to Challenges in European Hospitals. Healthcare, 9(8), 961. DOI: 10.3390/HEALTHCARE9080961.
  54. Kuan K. K. Y., and Chau P. Y. K. (2001). A Perception-Based Model for EDI Adoption in Small Businesses Using a Technology-Organization-Environment Framework. Information and Management, 38(8): 507-21. DOI: 10.1016/S0378-7206(01)00073-8.
  55. Liang Y., Qi G., Wei K., and Chen J. (2017). Exploring the Determinant and Influence Mechanism of E-Government Cloud Adoption in Government Agencies in China. Government Information Quarterly, 34(3): 481-95. DOI: 10.1016/J.GIQ.2017.06.002.
  56. Lillis A. M., and Mundy J. (2005). Cross-Sectional Field Studies in Management Accounting Research: Closing the Gaps between Surveys and Case-Studies. Article in Journal of Management Accounting Research, 17: 119-44. DOI: 10.2308/jmar.2005.17.1.119.
  57. Longoni C., Bonezzi A., and Morewedge C. K. (2019). Resistance to Medical Artificial Intelligence. Journal of Consumer Research, 46(4): 629-50. DOI: 10.1093/JCR/UCZ013.
  58. Low C., Chen Y., and Wu M. (2011). Understanding the Determinants of Cloud Computing Adoption. Industrial Management and Data Systems, 111(7): 1006-23. DOI: 10.1108/02635571111161262.
  59. McCoy A. B., Waitman L. R., Lewis J. B., Wright J. A., Choma D. P., Miller R. A., and Peterson J. F. (2012). A Framework for Evaluating the Appropriateness of Clinical Decision Support Alerts and Responses. Journal of the American Medical Informatics Association: JAMIA, 19(3): 346-52. DOI: 10.1136/AMIAJNL-2011-000185.
  60. Menachemi N., Chukmaitov A., Saunders C., and Brooks R. G. (2008). Hospital Quality of Care: Does Information Technology Matter? The Relationship between Information Technology Adoption and Quality of Care. Health Care Management Review, 33(1): 51-59. DOI: 10.1097/01.HMR.0000304497.89684.36.
  61. Merchant K. A., and Manzoni J.F. (1989). The Achievability of Budget Targets in Profit Centers: A Field Study. The Accounting Review, 64(3): 539-58.
  62. Miles M. B., & Huberman A. M. (1984). Drawing valid meaning from qualitative data: Toward a shared craft. Educational researcher, 13(5), 20-30.
  63. Mishra V. (2018). Artificial Intelligence: The Beginning of a New Era in Pharmacy Profession. Asian Journal of Pharmaceutics, 12(2).
  64. 22377/AJP.V12I02.2317.
  65. Moi L., Frau M., and Cabiddu F. (2018). Exploring the Role of NVivo Software in Marketing Research. Mercati & Competitività, (4): 65-86. DOI: 10.3280/MC2018-004005.
  66. Moi L., Rashkova Y., & Cabiddu F. (2023). The next frontier of Digital Business Model Innovation. In Cennamo C., Battista Dagnino G. & Zhu F. (Ed.). Research Handbook on Digital Strategy. Edward Elgar Publishing. DOI: 10.4337/9781800378902.
  67. Montague E. N. H., Winchester W. W., and Kleiner B. M. (2010). Trust in Medical Technology by Patients and Health Care Providers in Obstetric Work Systems. Behaviour & Information Technology, 29(5): 541. DOI: 10.1080/01449291003752914.
  68. Nadr H., Rahimi B., Lotfnezhad Afshar H., Samadbeik M., and Garavand A. (2018). Factors Affecting Acceptance of Hospital Information
  69. Systems Based on Extended Technology Acceptance Model: A Case Study in Three Paraclinical Departments. Applied Clinical Informatics, 9(2): 238. DOI: 10.1055/S-0038-1641595.
  70. Noorbakhsh-Sabet N., Zand R., Zhang Y., and Abedi V. (2019). Artificial Intelligence Transforms the Future of Health Care. The American Journal of Medicine, 132(7): 795-801. DOI: 10.1016/J.AMJMED.2019.01.017.
  71. Nuti S., Noto G., Vola F., and Vainieri M. (2018). Let’s Play the Patients Music: A New Generation of Performance Measurement Systems in Healthcare. Management Decision, 56(10): 2252-72. DOI: 10.1108/MD-09-2017-0907.
  72. Oliveira T., and Fraga Martins M. (2011). Literature Review of Information Technology Adoption Models at Firm Level. Electronic Journal of Information Systems Evaluation, 14(1): 110‑121.
  73. Oliveira T., Thomas M., and Espadanal M. (2014). Assessing the Determinants of Cloud Computing Adoption: An Analysis of the Manufacturing and Services Sectors. Information & Management, 51(5): 497-510. DOI: 10.1016/J.IM.2014.03.006.
  74. Olshansky S. J. (2018). From Lifespan to Healthspan. JAMA, 320(13): 1323-24. DOI: 10.1001/JAMA.2018.12621.
  75. Owusu Kwateng K., Appiah C., and Afo Osei Atiemo K. (2021). Adoption of Health Information Systems: Health Professionals Perspective. International Journal of Healthcare Management, 14(2): 517-33. DOI: 10.1080/20479700.2019.1672004.
  76. Pearson-Stuttard J., Ezzati M. and Gregg E. W. (2019). Multimorbidity-a Defining Challenge for Health Systems. The Lancet. Public Health, 4(12): e599-600. DOI: 10.1016/S2468-2667(19)30222-1.
  77. Premkumar G., and Potter M. (1995). Adoption of Computer Aided Software Engineering (CASE) Technology. ACM SIGMIS Database: The DATABASE for Advances in Information Systems, 26(2-3): 105-24. DOI: 10.1145/217278.217291.
  78. Price W. N. II. (2017). Artificial Intelligence in Health Care: Applications and Legal Implications. The Sci Tech Lawyer, 14(1).
  79. Ramdani B., Duan B., and Berrou I. (2020). Exploring the Determinants of Mobile Health Adoption by Hospitals in China: Empirical Study. JMIR Med Inform, 8(7): E14795. DOI: 10.2196/14795.
  80. Renaudin P., Baumstarck K., Daumas A., Esteve M. A., Gayet S., Auquier P., Tsimaratos M., Villani P., and Honore S. (2017). Impact of a Pharmacist-Led Medication Review on Hospital Readmission in a Pediatric and Elderly Population: Study Protocol for a Randomized Open-Label Controlled Trial. Trials, 18(1). DOI: 10.1186/S13063-017-1798-6.
  81. Rezaeibagha F., Win K. T., and Susilo W. (2015). A Systematic Literature Review on Security and Privacy of Electronic Health Record Systems: Technical Perspectives. Health Information Management: Journal of the Health Information Management Association of Australia, 44(3): 23-38. DOI: 10.1177/183335831504400304.
  82. Rigby M. (2006). Essential Prerequisites to the Safe and Effective Widespread Roll-out of e-Working in Healthcare. International Journal of Medical Informatics, 75(2): 138-47. DOI: 10.1016/J.IJMEDINF.2005.06.006.
  83. Rindfleisch T. C. (1997). Privacy, Information Technology, and Health Care. Communications of the ACM, 40(8): 92-100. DOI: 10.1145/257874.257896.
  84. Rogers E. M. (1983). Diffusion of Innovations.
  85. Rotolo D., Hicks D., & Martin B. R. (2015). What is an emerging technology?. Research policy, 44(10): 1827-1843. DOI: 10.1016/j.respol.2015.06.006.
  86. Rye C. B., and Kimberly J. R. (2007). The Adoption of Innovations by Provider Organizations in Health Care. Medical Care Research and Review: MCRR, 64(3): 235-78. DOI: 10.1177/1077558707299865.
  87. Secinaro S., Calandra D., Secinaro A., Muthurangu V., and Biancone P. (2021). The Role of Artificial Intelligence in Healthcare: A Structured Literature Review. BMC Medical Informatics and Decision Making, 21(1). DOI: 10.1186/S12911-021-01488-9.
  88. Shimizu H. E., Pacheco Santos L. M., Niskier Sanchez M., Hone T., Millett C., and Harris M. (2021). Challenges Facing the More Doctors Program (Programa Mais Médicos) in Vulnerable and Peri-Urban Areas in Greater Brasilia, Brazil. Human Resources for Health, 19(1). DOI: 10.1186/S12960-021-00672-2.
  89. Strauss A., & Corbin J. (1998). Basics of qualitative research techniques.
  90. Van Der Sijs H., Aarts J., Vulto A., and Berg M. (2006). Overriding of Drug Safety Alerts in Computerized Physician Order Entry. Journal of the American Medical Informatics Association: JAMIA, 13(2): 138-47. DOI: 10.1197/JAMIA.M1809.
  91. Silven A. V., van Peet P. G., Boers S. N., Tabak M., de Groot A., Hendriks D., van Os H. J. A., Bonten T. N., Atsma D. E., de Graaf T. J., Sombroek M. P., Chavannes N. H., and Villalobos-Quesada M. (2022). Clarifying Responsibility: Professional Digital Health in the Doctor-Patient Relationship, Recommendations for Physicians Based on a Multi-Stakeholder Dialogue in the Netherlands. BMC Health Services Research, 22(1): 1-10. DOI: 10.1186/S12913-021-07316-0.
  92. Sirois C., Khoury R., Durand A., Luc Deziel P., Bukhtiyarova O., Chiu Y., Talbot D., Bureau A., Després P., Gagné C., Laviolette F., Savard A. M., Corbeil J., Badard T., Jean S., and Simard M. (2021). Exploring Polypharmacy with Artificial Intelligence: Data Analysis Protocol. BMC Medical Informatics and Decision Making, 21(1). DOI: 10.1186/S12911-021-01583-X.
  93. Sligo J., Gauld R., Roberts V., and Villa L. (2017). A Literature Review for Large-Scale Health Information System Project Planning, Implementation and Evaluation. International Journal of Medical Informatics, 97: 86-97. DOI: 10.1016/J.IJMEDINF.2016.09.007.
  94. Stead W. W. (2018). Clinical Implications and Challenges of Artificial Intelligence and Deep Learning. JAMA, 320(11): 1107-8. DOI: 10.1001/JAMA.2018.11029.
  95. Tagliaferri S. D., Angelova M., Zhao X., Owen P. J., Miller C. T., Wilkin T., and Belavy D. L. (2020). Artificial Intelligence to Improve Back Pain Outcomes and Lessons Learnt from Clinical Classification Approaches: Three Systematic Reviews. NPJ Digital Medicine, 3(1). DOI: 10.1038/S41746-020-0303-X.
  96. Thiesse F., Staake T., Schmitt P., and Fleisch E. (2011). The Rise of the ‘next-Generation Bar Code’: An International RFID Adoption Study. Supply Chain Management, 16(5): 328-45. DOI: 10.1108/13598541111155848.
  97. Thimbleby H. (2013). Technology and the Future of Healthcare. Journal of Public Health Research, 2(3), jphr.2013.e28. DOI: 10.4081/JPHR.2013.E28.
  98. Vanany I., and Shaharoun A. M. (2008). Barriers and Critical Success Factors towards RFID Technology Adoption in South-East Asian Healthcare Industry. In Proceedings of the 9th Asia Pacific Industrial Engineering & Management Systems Conference, Bali, pp. 148-55.
  99. Venkatesh V., Brown S. A., Maruping L. M., and Bala H. (2008). Predicting Different Conceptualizations of System USE: The Competing Roles of Behavioral Intention, Facilitating Conditions, and Behavioral Expectation. MIS Quarterly: Management Information Systems, 32(3): 483-502. DOI: 10.2307/25148853.
  100. Venkatesh V., Morris M. G., Davis G. B., and Davis F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. Quarterly, 27(3): 425-78.
  101. Villamañán E., Larrubia Y., Ruano M., Vélez M., Armada E., Herrero A., and Álvarez-Sala R. (2013). Potential Medication Errors Associated with Computer Prescriber Order Entry. International Journal of Clinical Pharmacy, 35(4): 577-83. DOI: 10.1007/S11096-013-9771-2.
  102. Vogeli C., Shields A. E., Lee T. A., Gibson T. B., Marder W. D., Weiss K. B., and Blumenthal D. (2007). Multiple Chronic Conditions: Prevalence, Health Consequences, and Implications for Quality, Care Management, and Costs. Journal of General Internal Medicine, 22(Suppl 3): 391-95. DOI: 10.1007/S11606-007-0322-1.
  103. Wang Y., Kung L. A., and Byrd T. A. (2018). Big Data Analytics: Understanding Its Capabilities and Potential Benefits for Healthcare Organizations. Technological Forecasting and Social Change, 126: 3-13. DOI: 10.1016/J.TECHFORE.2015.12.019.
  104. Wang Y. M., Wang Y. S., and Yang Y. F. (2010). Understanding the Determinants of RFID Adoption in the Manufacturing Industry. Technological Forecasting and Social Change, 77(5): 803-15. DOI: 10.1016/J.TECHFORE.2010.03.006.
  105. Wang Z., and Majewicz Fey A. (2018). Deep Learning with Convolutional Neural Network for Objective Skill Evaluation in Robot-Assisted Surgery. International Journal of Computer Assisted Radiology and Surgery, 13(12): 1959-70. DOI: 10.1007/S11548-018-1860-1.
  106. Wieringa M. (2020). What to account for when accounting for algorithms. Proceedings of the 2020 Conference on Fairness, Accountability, and Transparency, New York, NY, ACM, pp. 1-18. DOI: 10.1145/3351095.3372833.
  107. WHO (2021). Global Strategy on Digital Health 2020-2025.
  108. Wu J. H., Shen W. S., Lin L. M., Greenes R. A., and Bates D. W. (2008). Testing the Technology Acceptance Model for Evaluating Healthcare Professionals’ Intention to Use an Adverse Event Reporting System. International Journal for Quality in Health Care: Journal of the International Society for Quality in Health Care, 20(2): 123-29. DOI: 10.1093/INTQHC/MZM074.
  109. Wu J. H., Wang S. C., and Lin L. M. (2007). Mobile Computing Acceptance Factors in the Healthcare Industry: A Structural Equation Model. International Journal of Medical Informatics, 76(1): 66-77. DOI: 10.1016/J.IJMEDINF.2006.06.006.
  110. Yang J., Luo B., Zhao C., and Zhang H. (2022). Artificial Intelligence Healthcare Service Resources Adoption by Medical Institutions Based on TOE Framework. Digital Health, 8. DOI: 10.1177/20552076221126034.
  111. Yeh C.-H., Lee G.-G., & Pai J.-C. (2015). Using a technology-organization-environment framework to investigate the factors influencing
  112. e-business information technology capabilities. Information Development, 31(5): 435-450. DOI: 10.1177/0266666913516027.
  113. Yin R. K. (2014). Case study research: Design and methods (Vol. 5). Sage.
  114. Yin J., Ngiam K. Y., and Teo H. H. (2021). Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review. Journal of Medical Internet Research, 23(4). DOI: 10.2196/25759.
  115. Yu K. H., Beam A. L., and Kohane I. S. (2018). Artificial Intelligence in Healthcare. Nature Biomedical Engineering, 2(10): 719-31. DOI: 10.1038/S41551-018-0305-Z.
  116. Zhu K., Kraemer K. L., & Xu S. (2006). The Process of Innovation Assimilation by Firms in Different Countries: A Technology Diffusion Perspective on E-Business. Management Science, 52: 1557-1576. DOI: 10.1287/mnsc.1050.0487.
  117. Zhu K., Kraemer K. L., Xu S., and Dedrick J. (2004). Information Technology Payoff in E-Business Environments. Journal of Management
  118. Information Systems, 21(1): 17-54. DOI: 10.1080/07421222.2004.11045797.

Francesco De Domenico, Guido Noto, Carlo Vermiglio, Analisi dei fattori determinanti l’adozione dell’Intelligenza Artificiale in sanità in "MECOSAN" 128/2023, pp 135-160, DOI: 10.3280/mesa2023-128oa18595