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12 - Organisational Readiness for the Adoption of Artificial Intelligence in Hospitals

from Part IV - Balancing Regulation, Innovation and Ethics

Published online by Cambridge University Press:  08 September 2022

By
Maximilian Schuessler ,
Till Bärnighausen  and
Anant Jani
Edited by
Marcelo Corrales Compagnucci ,
Michael Lowery Wilson ,
Mark Fenwick ,
Nikolaus Forgó  and
Till Bärnighausen
Marcelo Corrales Compagnucci
Affiliation:
University of Copenhagen
Michael Lowery Wilson
Affiliation:
University of Turku, Finland
Mark Fenwick
Affiliation:
Kyushu University, Japan
Nikolaus Forgó
Affiliation:
Universität Wien, Austria
Till Bärnighausen
Affiliation:
Universität Heidelberg
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Summary

AI harbours considerable potential to improve diagnosis and therapy, enhance access to healthcare, and promote population health. AI-enabled healthcare is increasingly seen as part of the solution needed to address the growing gap between the supply and demand of hospital care. AI is well placed to help us tackle new challenges, though these novel applications are likely to render technology implementation even more complex. Yet, many hospitals within the EU are unprepared for this change. Historically, hospitals have faced multiple challenges when implementing new technologies. This chapter discusses the importance of AI readiness and highlights the benefits and limitations of a new policy tool: an AI Readiness Index for Hospitals (AI-RIH). We conceptualise AI readiness from an organisational perspective and discuss the dual functionality of the AI-RIH. For hospital managers, it could serve as a benchmarking tool. For policy-makers, it can help create targeted technology policies and measure their effectiveness. This chapter also discusses the conceptual challenges of indices and illustrates why a hospital index might provide more policy insights than an aggregated or national index. Finally, we explain how AI readiness can strengthen hospitals’ role as innovators and support the development and deployment of AI.

Keywords

AImachine learningdigital healtheHealthtechnology implementationhospital managementorganisational readiness
Type
Chapter
Information
AI in eHealth
Human Autonomy, Data Governance and Privacy in Healthcare
 , pp. 334 - 377
Publisher: Cambridge University Press
Print publication year: 2022

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References

Armenakis, AA and Bedeian, AG, ‘Organizational Change: A Review of Theory and Research in the 1990s’ (1999) 25(3) Journal of Management 293–315. doi:10.1177/014920639902500303Google Scholar
Atun, R, ‘Transitioning Health Systems for Multimorbidity’ (2015) 386 Lancet 721.CrossRefGoogle ScholarPubMed
Bacchi, S and others, ‘Machine Learning in the Prediction of Medical Inpatient Length of Stay’ (2020) 52(2) Journal of Internal Medicine 176–85. doi: 10.1111/imj.14962Google Scholar
Bansler, JP, ‘Challenges in User-Driven Optimization of EHR: A Case Study of a Large Epic Implementation in Denmark’ (2021) 148 International Journal of Medical Informatics 104394.CrossRefGoogle Scholar
Beede, E and others, ‘A Human-Centered Evaluation of a Deep Learning System Deployed in Clinics for the Detection of Diabetic Retinopathy’ in Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (Association for Computing Machinery 2020) 1–12.Google Scholar
Benjamens, S, Dhunnoo, P and Mesko, B, ‘The State of Artificial Intelligence-Based FDA-Approved Medical Devices and Algorithms: An Online Database’ (2020) 3 npj Digital Medicine 118.Google Scholar
Bevan, G, ‘If Neither Altruism nor Markets Have Improved NHS Performance, What Might?’ (2010) 16 Eurohealth 20.Google Scholar
Bevan, G and Wilson, D, ‘Does “Naming and Shaming” Work for Schools and Hospitals? Lessons from Natural Experiments Following Devolution in England and Wales’ (2013) 33 Public Money & Management 245.Google Scholar
Bitterman, DS, Aerts, H and Mak, RH, ‘Approaching Autonomy in Medical Artificial Intelligence’ (2020) 2 Lancet Digital Health e447.CrossRefGoogle ScholarPubMed
Boonstra, A, Versluis, A and Vos, JF, ‘Implementing Electronic Health Records in Hospitals: A Systematic Literature Review’ (2014) 14 BMC Health Services Research 370.Google Scholar
Braunstein, ML, ‘Healthcare in the Age of Interoperability: The Promise of Fast Healthcare Interoperability Resources’ (2018) 9 IEEE Pulse 24.CrossRefGoogle ScholarPubMed
Brynjolfsson, E and Mcafee, A, ‘The Business of Artificial Intelligence’ (2017) 7 Harvard Business Review 3.Google Scholar
Bughin, J and others, Notes from the AI Frontier: Tackling Europe’s Gap in Digital and AI (McKinsey, 2019).Google Scholar
Buntin, MB and others, ‘The Benefits of Health Information Technology: A Review of the Recent Literature Shows Predominantly Positive Results’ (2011) 30 Health Affairs 464.Google Scholar
Cahan, EM and others, ‘Putting the Data Before the Algorithm in Big Data Addressing Personalized Healthcare’ (2019) 2 npj Digital Medicine 78.CrossRefGoogle ScholarPubMed
Char, DS, Shah, NH and Magnus, D, ‘Implementing Machine Learning in Health Care – Addressing Ethical Challenges’ (2018) 378 New England Journal of Medicine 981.CrossRefGoogle ScholarPubMed
Cresswell, K and Sheikh, A, ‘Organizational Issues in the Implementation and Adoption of Health Information Technology Innovations: An Interpretative Review’ (2013) 82 International Journal of Medical Informatics e73.Google Scholar
De Fauw, J and others, ‘Clinically Applicable Deep Learning for Diagnosis and Referral in Retinal Disease’ (2018) 24 Nature Medicine 1342.Google Scholar
Deidda, M, Lupiañez, F and Maghiros, I, ‘European Hospital Survey: Benchmarking Deployment of e-Health Services (2012–2013) – Methodological Report’ IDEAS Working Paper Series from RePEc, 2013, http://publications.jrc.ec.europa.eu/repository/bitstream/JRC85854/jrc85854.pdfGoogle Scholar
De Nigris, S and others, ‘AI Watch AI Uptake in Health and Healthcare, 2020’ EUR 30478, Publications Office of the European Union, 2020, https://doi.org/10.2760/948860.Google Scholar
Doolin, B, ‘Implementing e-Health’ in Ferlie, E, Montgomery, K and Reff Pedersen, A (eds) The Oxford Handbook of Health Care Management (Oxford University Press 2016).Google Scholar
Economist Intelligence Unit, The Automation Readiness Index: Who Is Ready for the Coming Wave of Automation? (Economist Intelligence Unit, 2018).Google Scholar
Eit Health and McKinsey&Company, ‘Transforming Healthcare with AI’ (March 2020), https://eit.europa.eu/library/eit-health-mckinsey-transforming-healthcare-aiGoogle Scholar
Emanuel, EJ and others, ‘Fair Allocation of Scarce Medical Resources in the Time of Covid-19’ (2020) 382 New England Journal of Medicine 2049.Google Scholar
Esteva, A and others, ‘Deep Learning-Enabled Medical Computer Vision’ (2021) 4 npj Digital Medicine 5.CrossRefGoogle ScholarPubMed
Esteva, A and others, ‘Dermatologist-Level Classification of Skin Cancer with Deep Neural Networks’ (2017) 542 Nature 115.Google Scholar
European Commission, ‘Artificial Intelligence’ 2021, https://ec.europa.eu/digital-single-market/en/artificial-intelligence, accessed 3 February 2021.Google Scholar
European Commission, Ethics Guidelines for Trustworthy AI (Publications Office 2019).Google Scholar
European Commission, ‘White Paper on Artificial Intelligence: A European Approach to Excellence and Trust’ 19 February 2020, https://ec.europa.eu/info/sites/default/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf, accessed 5 February 2022.Google Scholar
Foley, TJ and Vale, L, ‘What Role for Learning Health Systems in Quality Improvement within Healthcare Providers?’ (2017) 1 Learning Health Systems e10025.CrossRefGoogle ScholarPubMed
Frey, CB and Osborne, MA, ‘The Future of Employment: How Susceptible Are Jobs to Computerisation?’ (2017) 114 Technological Forecasting and Social Change 254.Google Scholar
Garrety, K and others, ‘National Electronic Health Records and the Digital Disruption of Moral Orders’ (2014) 101 Social Science & Medicine 70.CrossRefGoogle ScholarPubMed
Ghafur, S and others, ‘A Retrospective Impact Analysis of the WannaCry Cyberattack on the NHS’ (2019) 2 npj Digital Medicine 98.Google Scholar
Greco, S and others, ‘On the Methodological Framework of Composite Indices: A Review of the Issues of Weighting, Aggregation, and Robustness’ (2019) 141 Social Indicators Research 61.Google Scholar
Greenhalgh, T and others, ‘Adoption, Non-adoption, and Abandonment of a Personal Electronic Health Record: Case Study of HealthSpace’ (2010) 341 BMJ c5814.Google Scholar
Greenhalgh, T and others, ‘Beyond Adoption: A New Framework for Theorizing and Evaluating Nonadoption, Abandonment, and Challenges to the Scale-Up, Spread, and Sustainability of Health and Care Technologies’ (2017) 19 Journal of Medical Internet Research e367.Google Scholar
Grossman, C, Powers, B and McGinnis, JM, Digital Infrastructure for the Learning Health System: The Foundation for Continuous Improvement in Health and Health Care, workshop series summary (National Academies Press 2011).Google Scholar
Grupp, H and Mogee, ME, ‘Indicators for National Science and Technology Policy: How Robust Are Composite Indicators?’ (2004) 33 Research Policy 1373.Google Scholar
‘Health at a Glance: Europe 2018: State of Health in the EU Cycle | en | OECD’ (2020).Google Scholar
Harmon, SA and others, ‘Artificial Intelligence for the Detection of COVID-19 Pneumonia on Chest CT Using Multinational Datasets’ (2020) 11 Nature Communications 4080.Google Scholar
Hawkes, N, ‘NHS Data Sharing Deal with Google Prompts Concern’ (2016) 353 BMJ i2573.Google Scholar
He, J and others, ‘The Practical Implementation of Artificial Intelligence Technologies in Medicine’ (2019) 25 Nature Medicine 30.CrossRefGoogle ScholarPubMed
Hertzum, M and Ellingsen, G, ‘The Implementation of an Electronic Health Record: Comparing Preparations for Epic in Norway with Experiences from the UK and Denmark’ (2019) 129 International Journal of Medical Informatics 312.CrossRefGoogle ScholarPubMed
HIMSS, HIMSS Dictionary of Health Information Technology Terms, Acronyms, and Organizations 4th ed (CRC Press 2017).Google Scholar
HIMSS, ‘Interoperability in Healthcare’ 2020, www.himss.org/resources/interoperability-healthcare, accessed 3 February 2021.Google Scholar
HIMSS Analytics, ‘eHealth Trend Barometer: AI Use in European Healthcare’ 2019, www.himssanalytics.org/europe/ehealth-barometer/ehealth-trend-barometer-ai-use-european-healthcareGoogle Scholar
HIMSS Analytics, ‘Enabling Better Health Through Information and Technology’, www.lesiss.org/offres/file_inline_src/445/445_mailing_291018_111749_2.pdf, accessed 30 June 2020.Google Scholar
Hood, C, The Blame Game: Spin, Bureaucracy, and Self-Preservation in Government, Course Book ed. (Princeton University Press 2010).Google Scholar
Iacobucci, G, ‘Patient Data Were Shared with Google on an “Inappropriate Legal Basis,” Says NHS Data Guardian’ (2017) 357 BMJ j2439.Google Scholar
Information Commissioner’s Office, ‘Royal Free – Google DeepMind Trial Failed to Comply with Data Protection Law’ 2017, https://ico.org.uk/about-the-ico/news-and-events/news-and-blogs/2017/07/royal-free-google-deepmind-trial-failed-to-comply-with-data-protection-law/, accessed 3 February 2021.Google Scholar
Jha, S and Topol, E, ‘Adapting to Artificial Intelligence: Radiologists and Pathologists as Information Specialists’ (2016) 316 JAMA 2353.Google Scholar
Karanikolos, M and others, ‘Financial Crisis, Austerity, and Health in Europe’ (2013) 381 Lancet 1323.Google Scholar
Kazley, AS and Ozcan, YA, ‘Organizational and Environmental Determinants of Hospital EMR Adoption: A National Study’ (2007) 31 Journal of Medical Systems 375.CrossRefGoogle ScholarPubMed
Komorowski, M and others, ‘The Artificial Intelligence Clinician Learns Optimal Treatment Strategies for Sepsis in Intensive Care’ (2018) 24 Nature Medicine 1716.Google Scholar
Kovarik, CL, ‘Patient Perspectives on the Use of Artificial Intelligence’ (2020) 156 JAMA Dermatology 493.Google Scholar
Krishnan, L, Ogunwole, SM and Cooper, LA, ‘Historical Insights on Coronavirus Disease 2019 (COVID-19), the 1918 Influenza Pandemic, and Racial Disparities: Illuminating a Path Forward’ (2020) 173 Annals of Internal Medicine 474.Google Scholar
Kruse, CS and Beane, A, ‘Health Information Technology Continues to Show Positive Effect on Medical Outcomes: Systematic Review’ (2018) 20 Journal of Medical Internet Research e41.Google Scholar
Kujala, S and others, ‘The Role of Frontline Leaders in Building Health Professional Support for a New Patient Portal: Survey Study’ (2019) 21 Journal of Medical Internet Research e11413.Google Scholar
Legg, S and Hutter, M, ‘A Collection of Definitions of Intelligence’ (2007) 157 Frontiers in Artificial Intelligence and Applications 17.Google Scholar
Limb, M, ‘Controversial Database of Medical Records is Scrapped over Security Concerns’ (2016) 354 BMJ i3804.Google Scholar
Lokuge, S and others, ‘Organizational Readiness for Digital Innovation: Development and Empirical Calibration of a Construct’ (2019) 56 Information and Management 445.Google Scholar
Malin, JL, ‘Envisioning Watson as a Rapid-Learning System for Oncology’ (2013) 9 Journal of Oncology Practice 155.Google Scholar
Manz, CR and others, ‘Validation of a Machine Learning Algorithm to Predict 180-Day Mortality for Outpatients with Cancer’ (2020) 6(11) JAMA Oncology 1723–30.Google Scholar
McCall, B, ‘COVID-19 and Artificial Intelligence: Protecting Health-Care Workers and Curbing the Spread’ (2020) 2 Lancet Digital Health e166.Google Scholar
Miech, EJ and others, ‘Inside Help: An Integrative Review of Champions in Healthcare-Related Implementation’ (2018) 6 SAGE Open Medicine 2050312118773261.CrossRefGoogle ScholarPubMed
Mileti, D, Gillespie, D and Haas, J, ‘Size and Structure in Complex Organizations’ (1977) 56 Social Forces 208.Google Scholar
Minor, L, ‘The Rise of the Data-Driven Physician’ Stanford Medicine 2020 Health Trends Report, https://med.stanford.edu/dean/healthtrends.html, accessed 6 February 2022.Google Scholar
Muse, ED and others, ‘Towards a Smart Medical Home’ (2017) 389 Lancet 358.Google Scholar
Nagendran, M and others, ‘Artificial Intelligence Versus Clinicians: Systematic Review of Design, Reporting Standards, and Claims of Deep Learning Studies’ (2020) 368 BMJ m689.Google Scholar
Nelson, A and others, ‘Predicting Scheduled Hospital Attendance with Artificial Intelligence’ (2019) 2 npj Digital Medicine 26.Google Scholar
Obermeyer, Z and Lee, TH, ‘Lost in Thought – The Limits of the Human Mind and the Future of Medicine’ (2017) 377 New England Journal of Medicine 1209.Google Scholar
OECD, ‘AI Strategies & Public Sector Components – Observatory of Public Sector Innovation’ 2021, https://oecd-opsi.org/projects/ai/strategies/, accessed 3 February 2021.Google Scholar
OECD, Handbook on Constructing Composite Indicators: Methodology and User Guide (OECD Publishing 2008).Google Scholar
OECD, Health at a Glance: Europe 2016: State of Health in the EU Cycle (OECD 2016).Google Scholar
OECD and European Union, Health at a Glance: Europe 2020 (OECD Publishing 2020).Google Scholar
Oxford Insights, ‘Government AI Readiness Index 2020’ 2020, www.oxfordinsights.com/government-ai-readiness-index-2020, accessed 3 February 2021.Google Scholar
Panch, T, Szolovits, P and Atun, R, ‘Artificial Intelligence, Machine Learning and Health Systems’ (2018) 8 Journal of Global Health 020303.Google Scholar
Paré, G and others, ‘Clinicians’ Perceptions of Organizational Readiness for Change in the Context of Clinical Information System Projects: Insights from Two Cross-Sectional Surveys’ (2011) 6 Implementation Science 15.Google Scholar
Patel, TA and others, ‘Correlating Mammographic and Pathologic Findings in Clinical Decision Support Using Natural Language Processing and Data Mining Methods’ (2017) 123 Cancer 114.Google Scholar
Perrault, R and others, Artificial Intelligence Index Report 2019 (Human-Centered AI Institute, Stanford University 2019).Google Scholar
Petkus, H, Hoogewerf, J and Wyatt, JC, ‘What Do Senior Physicians Think about AI and Clinical Decision Support Systems: Quantitative and Qualitative Analysis of Data from Specialty Societies’ (2020) 20 Clinical Medicine 324.Google Scholar
Pettit, L, ‘Understanding EMRAM and How It Can be Used by Policy-Makers, Hospital CIOs and Their IT Teams’ (2013) 49 World Hospitals and Health Services 7.Google Scholar
Piwek, L and others, ‘The Rise of Consumer Health Wearables: Promises and Barriers’ (2016) 13 PLoS Medicine e1001953.Google Scholar
Powles, J and Hodson, H, ‘Google DeepMind and Healthcare in an Age of Algorithms’ (2017) 7 Health Technology 351.Google Scholar
PwC, European Hospital Survey: Benchmarking Deployment of eHealth Services (2012–2013), final report (JCR Scientific and Policy, 2014).Google Scholar
Rajkomar, A and others, ‘Scalable and Accurate Deep Learning with Electronic Health Records’ (2018) 1 npj Digital Medicine 18.Google Scholar
Rawson, TM and others, ‘Artificial Intelligence Can Improve Decision-Making in Infection Management’ (2019) 3 Nature Human Behaviour 543.Google Scholar
Rossi, F, Artificial Intelligence: Potential Benefits and Ethical Considerations (2016) Briefing Paper to the European Union Parliament Policy Department C: Citizens’ Rights and Constitutional Affairs European Parliament, www.europarl.europa.eu/RegData/etudes/BRIE/2016/571380/IPOL_BRI(2016)571380_EN.pdfGoogle Scholar
Schleicher, A, The Case for 21st Century Learning, Vol. 282 (OECD Observer 2011), www.oecd.org/general/thecasefor21st-centurylearning.htmGoogle Scholar
Schmidt, C, ‘MD Anderson Breaks with IBM Watson, Raising Questions about Artificial Intelligence in Oncology’ (2017) 109 JNCI: Journal of the National Cancer Institute, doi: 10.1093/jnci/djx113Google Scholar
Sharpe, A, Literature Review of Frameworks for Macro-Indicators (Centre for the Study of Living Standards 2004).Google Scholar
Shaw, J and others, ‘Artificial Intelligence and the Implementation Challenge’ (2019) 21 Journal of Medical Internet Research e13659.Google Scholar
Shortliffe, EH and Sepulveda, MJ, ‘Clinical Decision Support in the Era of Artificial Intelligence’ (2018) 320 JAMA 2199.Google Scholar
Singer, JD and Braun, HI, ‘Testing International Education Assessments’ (2018) 360 Science 38Google Scholar
Somashekhar, SP and others, ‘Watson for Oncology and Breast Cancer Treatment Recommendations: Agreement with an Expert Multidisciplinary Tumor Board’ (2018) 29 Annals of Oncology 418.Google Scholar
Southon, FC, Sauer, C and Grant, CN, ‘Information Technology in Complex Health Services: Organizational Impediments to Successful Technology Transfer and Diffusion’ (1997) 4 Journal of the American Medical Informatics Association 112.Google Scholar
Stephani, V, Busse, R and Geissler, A, ‘Benchmarking der Krankenhaus-IT: Deutschland im internationalen Vergleich’ in Klauber, J and others (eds), Krankenhaus-Report 2019: Das digitale Krankenhaus (Springer Berlin Heidelberg 2019).Google Scholar
Strickland, E, ‘IBM Watson, Heal Thyself: How IBM Overpromised and Underdelivered on AI Health Care’ (2019) 56 IEEE Spectrum 24.Google Scholar
Thiel, R and others, #SmartHealthSystems – Focus Europe (Bertelsmann Stiftung 2019).Google Scholar
Thunea, T and Mina, A, ‘Hospitals as Innovators in the Health-Care System: A Literature Review and Research Agenda’ (2016) 45 Research Policy 1545.Google Scholar
Ting, DSW and others, ‘Digital Technology and COVID-19’ (2020) 26 Nature Medicine 459.Google Scholar
Topol, EJ, ‘High-Performance Medicine: The Convergence of Human and Artificial Intelligence’ (2019) 25 Nature Medicine 44.Google Scholar
van Limburg, M and others, ‘Why Business Modeling is Crucial in the Development of eHealth Technologies’ (2011) 13 Journal of Medical Internet Research e124.CrossRefGoogle ScholarPubMed
van Staa, TP and others, ‘Big Health Data: The Need to Earn Public Trust’ (2016) 354 BMJ i3636.Google Scholar
Vayena, E, Blasimme, A and Cohen, IG, ‘Machine Learning in Medicine: Addressing Ethical Challenges’ (2018) 15 PLoS Medicine e1002689.CrossRefGoogle Scholar
Wachter, R, Making IT Work: Harnessing the Power of Health Information Technology to Improve Care in England (Department of Health, 2016).Google Scholar
Wadmann, S and Hoeyer, K, ‘Dangers of the Digital Fit: Rethinking Seamlessness and Social Sustainability in Data-Intensive Healthcare’ (2018) 5 Big Data & Society 2053951717752964.Google Scholar
Weng, SF and others, ‘Can Machine-Learning Improve Cardiovascular Risk Prediction Using Routine Clinical Data?’ (2017) 12 PLoS One e0174944.Google Scholar
Wind, A and van Harten, WH, ‘Benchmarking Specialty Hospitals, A Scoping Review on Theory and Practice’ (2017) 17 BMC Health Services Research 245.Google Scholar
World Health Organization Regional Office for Europe, ‘Core Health Indicators in the WHO European Region 2019. Special focus: Health 2020’ 2019, www.euro.who.int/__data/assets/pdf_file/0004/413239/CHI_2019_EN_WEB.pdfGoogle Scholar
Yusif, S, Hafeez-Baig, A and Soar, J, ‘e-Health Readiness Assessment Factors and Measuring Tools: A Systematic Review’ (2017) 107 International Journal of Medical Informatics 56.Google Scholar
Zeevi, D and others, ‘Personalized Nutrition by Prediction of Glycemic Responses’ (2015) 163 Cell 1079.Google Scholar
Zhou, Y and others, ‘Artificial Intelligence in COVID-19 Drug Repurposing’ (2020) 2 Lancet Digital Health e667.Google Scholar

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