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Open Access 01-12-2024 | Artificial Intelligence | Research

Forecasting the future of smart hospitals: findings from a real-time delphi study

Authors: Florian Jovy-Klein, Susan Stead, Torsten Oliver Salge, Jil Sander, Anke Diehl, David Antons

Published in: BMC Health Services Research | Issue 1/2024

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Abstract

Background

In concert with other digital technologies, artificial intelligence (AI) is shaping the vision of smart hospitals. The transformation into smart hospitals, however, is all but trivial due to the lack of financial and human resources, digital skills, and supporting policies. Thus, the extent to which the vision of smart hospitals will eventually become reality is uncertain. In this context, our study provides a multidimensional conceptualization of the immediate future of smart hospitals to 2042.

Methods

This study employs an iterative mixed-methods approach, including expert workshops and a Delphi study. We conducted a real-time Delphi study to forecast the evolution of smart hospitals in 5-year steps from 2027 to 2042. A total of 39 experts in healthcare, artificial intelligence, and management participated.

Results

Our understanding of a technology-enabled smart hospital in this study includes four dimensions: artificial intelligence (AI), sustainability, ecosystems, and human-centeredness. Our findings underscore the critical need to address the shortage of hospital staff and general practitioners that models predict will peak by 2032. Additionally, our results show a significant shift to individualized medicine and home care. This shift indicates that smart hospitals are expected to leverage AI and digital technologies to tailor care to each patient. Furthermore, the roles and responsibilities of hospital staff will undergo significant changes. Healthcare personnel will have to adapt to new technologies that facilitate more efficient workflows and improve patient engagement in evolving healthcare environments. The results of our study suggest a shift in care to individualized medicine and home care, with corresponding changes in the roles and responsibilities of hospital staff who will employ new technologies.

Conclusions

The findings from our real-time Delphi study suggest that the vision of smart hospitals is gradually becoming reality over the next 20 years. Advancements in artificial intelligence should enhance operational efficiency and patient-centric care, while facilitating the integration of sustainability practices and fostering collaborative ecosystems. However, addressing challenges such as staff shortages, ethical considerations, and the need for robust digital skills will be essential. A deep pool of expert healthcare practitioners, clear ethical guidelines, and robust digital skills are essential to fully realize this vision and ensure that smart hospitals can meet the evolving needs of healthcare delivery.
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Literature
1.
Magnavita N, Chirico F, Garbarino S, Bragazzi NL, Santacroce E, Zaffina S. SARS/MERS/SARS-CoV-2 Outbreaks and Burnout Syndrome among Healthcare Workers. An Umbrella Systematic Review. Int J Environ Res Public Health. 2021;18:4361.PubMedPubMedCentralCrossRef
2.
3.
Hermes S, Riasanow T, Clemons EK, Böhm M, Krcmar H. The digital transformation of the healthcare industry: exploring the rise of emerging platform ecosystems and their influence on the role of patients. Bus Res. 2020;13:1033–69.CrossRef
4.
Crain MA, Bush AL, Hayanga H, Boyle A, Unger M, Ellison M, et al. Healthcare Leadership in the COVID-19 Pandemic: From Innovative Preparation to Evolutionary Transformation. J Healthc Leadersh. 2021;13:199–207.PubMedPubMedCentralCrossRef
5.
Frick NRJ, Möllmann HL, Mirbabaie M, Stieglitz S. Driving Digital Transformation During a Pandemic: Case Study of Virtual Collaboration in a German Hospital. JMIR Med Inform. 2021;9:e25183.PubMedPubMedCentralCrossRef
6.
7.
Bohnet-Joschko S, Pilgrim K, Balling S, Köbe P, Korte L, Koß J, et al. Die digitale Zukunft der Gesundheitsversorgung. Pflegezeitschrift. 2022;75:10–3.CrossRef
8.
Jiang F, Jiang Y, Zhi H, Dong Y, Li H, Ma S, et al. Artificial intelligence in healthcare: past, present and future. Stroke Vasc Neurol. 2017;2:230–243.
9.
Rong G, Mendez A, Bou Assi E, Zhao B, Sawan M. Artificial Intelligence in Healthcare: Review and Prediction Case Studies. Engineering. 2020;6:291–301.CrossRef
10.
Yu K-H, Beam AL, Kohane IS. Artificial intelligence in healthcare. Nat Biomed Eng. 2018;2:719–31.PubMedCrossRef
11.
Dicuonzo G, Donofrio F, Fusco A, Shini M. Healthcare system: Moving forward with artificial intelligence. Technovation. 2023;120:102510.CrossRef
12.
Berente N, Gu B, Recker J, Santanam R, ZitierenNicholas B, et al. Managing Artificial Intelligence. MIS quarterly. 2021;2021(453):1433–50. Berente, N., Gu, B., Recker, J., Santhanam, R. (2021). Managing artificial intelligence. MIS quarterly, 45(3), 1433-1450. BERENTE, Nicholas, al. Managing artificial intelligence. MIS quarterly, 2021, 45. Jg., Nr. 3, S. 1433-1450.
13.
14.
Kwon H, An S, Lee H-Y, Cha WC, Kim S, Cho M, et al. Review of Smart Hospital Services in Real Healthcare Environments. Healthc Inform Res. 2022;28:3–15.PubMedPubMedCentralCrossRef
15.
Holzinger A, Röcker C, Ziefle M. From Smart Health to Smart Hospitals. In: Holzinger A, Röcker C, Ziefle M, editors. Smart Health: Open Problems and Future Challenges. Cham: Springer International Publishing; 2015. p. 1–20.CrossRef
16.
Sánchez D, Tentori M, Favela J. Activity Recognition for the Smart Hospital. IEEE Intell Syst. 2008;23:50–7.CrossRef
17.
Uslu BÇ, Okay E, Dursun E. Analysis of factors affecting IoT-based smart hospital design. J Cloud Comput. 2020;9:67.CrossRef
18.
Jamil F, Hang L, Kim K, Kim D. A Novel Medical Blockchain Model for Drug Supply Chain Integrity Management in a Smart Hospital. Electronics. 2019;8:505.CrossRef
19.
Jansson M, Ohtonen P, Alalääkkölä T, Heikkinen J, Mäkiniemi M, Lahtinen S, et al. Artificial intelligence-enhanced care pathway planning and scheduling system: content validity assessment of required functionalities. BMC Health Serv Res. 2022;22:1513.PubMedPubMedCentralCrossRef
20.
Sun TQ, Medaglia R. Mapping the challenges of Artificial Intelligence in the public sector: Evidence from public healthcare. Gov Inf Q. 2019;36:368–83.CrossRef
21.
Cesario A, D’Oria M, Bove F, Privitera G, Boškoski I, Pedicino D, et al. Personalized Clinical Phenotyping through Systems Medicine and Artificial Intelligence. J Pers Med. 2021;11:265.PubMedPubMedCentralCrossRef
22.
Lemmen C, Woopen C, Stock S. Systems medicine 2030: A Delphi study on implementation in the German healthcare system. Health Policy. 2021;125:104–14.PubMedCrossRef
23.
24.
Matheny M, Israni ST, Ahmed M, Whicher D. Artificial Intelligence in Health Care: The Hope, the Hype, the Promise, the Peril. 1st ed. Washington, DC: National Academy of Medicine; 2019.
25.
Gasser U. Interoperability in the Digital Ecosystem. SSRN Electron J. 2015;13:1–29.
26.
Hansen A, Herrmann M, Ehlers JP, Mondritzki T, Hensel KO, Truebel H, et al. Perception of the Progressing Digitization and Transformation of the German Health Care System Among Experts and the Public: Mixed Methods Study. JMIR Public Health Surveill. 2019;5:e14689.PubMedPubMedCentralCrossRef
27.
Berman T, Schallmo D, Willams CA. Digital Transformation of Business Model: The Case of Israeli HealthTech. In: Schallmo D, Baiyere A, Gertsen F, Rosenstand CAF, Williams CA, editors. Digital Disruption and Transformation. Cham: Springer International Publishing; 2024. p. 51–86.CrossRef
28.
McGain F, Naylor C. Environmental sustainability in hospitals - a systematic review and research agenda. J Health Serv Res Policy. 2014;19:245–52.PubMedCrossRef
29.
Carino S, Porter J, Malekpour S, Collins J. Environmental Sustainability of Hospital Foodservices across the Food Supply Chain: A Systematic Review. J Acad Nutr Diet. 2020;120:825–73.PubMedCrossRef
30.
Cowie J, Nicoll A, Dimova ED, Campbell P, Duncan EA. The barriers and facilitators influencing the sustainability of hospital-based interventions: a systematic review. BMC Health Serv Res. 2020;20:588.PubMedPubMedCentralCrossRef
31.
Wolf RM, Abramoff MD, Channa R, Tava C, Clarida W, Lehmann HP. Potential reduction in healthcare carbon footprint by autonomous artificial intelligence. Npj Digit Med. 2022;5:1–4.CrossRef
32.
Xiao MZX, Abbass SAA, Bahrey L, Rubinstein E, Chan VWS. A Roadmap for Environmental Sustainability of Plastic Use in Anesthesia and the Perioperative Arena. Anesthesiology. 2021;135:729–37.PubMedCrossRef
33.
Shen C, Zhao K, Ge J, Zhou Q. Analysis of Building Energy Consumption in a Hospital in the Hot Summer and Cold Winter Area. Energy Procedia. 2019;158:3735–40.CrossRef
34.
Werner JA, Kaatze T, Schmidt-Rumposch A. Green Hospital: Nachhaltigkeit und Ressourcenschonung im Krankenhaus. 1st ed. Berlin: MWV; 2022.
35.
Woll A, Tørresen J. What is a Smart Hospital? A Review of the Literature. In: Duffy VG, Lehto M, Yih Y, Proctor RW, editors. Human-Automation Interaction: Manufacturing, Services and User Experience. Cham: Springer International Publishing; 2023. p. 145–65.CrossRef
36.
Jacobides MG, Cennamo C, Gawer A. Towards a theory of ecosystems. Strateg Manag J. 2018;39:2255–76.CrossRef
37.
38.
Leone D, Schiavone F, Appio FP, Chiao B. How does artificial intelligence enable and enhance value co-creation in industrial markets? An exploratory case study in the healthcare ecosystem. J Bus Res. 2021;129:849–59.CrossRef
39.
Secundo G, Riad Shams SM, Nucci F. Digital technologies and collective intelligence for healthcare ecosystem: Optimizing Internet of Things adoption for pandemic management. J Bus Res. 2021;131:563–72.PubMedPubMedCentralCrossRef
40.
Osei-Frimpong K, Wilson A, Lemke F. Patient co-creation activities in healthcare service delivery at the micro level: The influence of online access to healthcare information. Technol Forecast Soc Change. 2018;126:14–27.CrossRef
41.
Pikkarainen M, Ervasti M, Hurmelinna-Laukkanen P, Nätti S. Orchestration Roles to Facilitate Networked Innovation in a Healthcare Ecosystem. Technol Innov Manag Rev. 2017;7:30–43.CrossRef
42.
Secundo G, Toma A, Schiuma G, Passiante G. Knowledge transfer in open innovation: A classification framework for healthcare ecosystems. Bus Process Manag J. 2018;25:144–63.CrossRef
43.
Brodie RJ, Ranjan KR, Verreynne M, Jiang Y, Previte J. Coronavirus crisis and health care: learning from a service ecosystem perspective. J Serv Theory Pract. 2021;31:225–46.CrossRef
44.
Yeh M-Y, Wu S-C, Tung T-H. The relation between patient education, patient empowerment and patient satisfaction: A cross-sectional-comparison study. Appl Nurs Res. 2018;39:11–7.PubMedCrossRef
45.
Bickmore TW, Pfeifer LM, Jack BW. Taking the time to care: empowering low health literacy hospital patients with virtual nurse agents. In: Proceedings of the SIGCHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery; 2009. p. 1265–74.
46.
Lam K, Abràmoff MD, Balibrea JM, Bishop SM, Brady RR, Callcut RA, et al. A Delphi consensus statement for digital surgery. Npj Digit Med. 2022;5:1–9.CrossRef
47.
Mosch L, Fürstenau D, Brandt J, Wagnitz J, Klopfenstein SA, Poncette A-S, et al. The medical profession transformed by artificial intelligence: Qualitative study. Digit Health. 2022;8:205520762211439.CrossRef
48.
Orkin AM, Rao S, Venugopal J, Kithulegoda N, Wegier P, Ritchie SD, et al. Conceptual framework for task shifting and task sharing: an international Delphi study. Hum Resour Health. 2021;19:61.PubMedPubMedCentralCrossRef
49.
Shinners L, Aggar C, Grace S, Smith S. Exploring healthcare professionals’ perceptions of artificial intelligence: Validating a questionnaire using the e-Delphi method. Digit Health. 2021;7:20552076211003430.PubMedPubMedCentralCrossRef
50.
Stead S. The Inscrutable New Actor: An Employee Perspective on the Flipside of AI. ICIS 2023 Proceedings. 2023;13:2027.
51.
Wiggermann N, Rempel K, Zerhusen RM, Pelo T, Mann N. Human-Centered Design Process for a Hospital Bed: Promoting Patient Safety and Ease of Use. Ergon Des Q Hum Factors Appl. 2019;27:4–12.
52.
Adus S, Macklin J, Pinto A. Exploring patient perspectives on how they can and should be engaged in the development of artificial intelligence (AI) applications in health care. BMC Health Serv Res. 2023;23:1163.PubMedPubMedCentralCrossRef
53.
Beede E, Baylor E, Hersch F, Iurchenko A, Wilcox L, Ruamviboonsuk P, et al. 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 (chi’20). New York: Assoc Computing Machinery; 2020.
54.
Couture HD, Williams LA, Geradts J, Nyante SJ, Butler EN, Marron JS, et al. Image analysis with deep learning to predict breast cancer grade, ER status, histologic subtype, and intrinsic subtype. NPJ Breast Cancer. 2018;4:30.PubMedPubMedCentralCrossRef
55.
Harte R, Glynn L, Rodríguez-Molinero A, Baker PM, Scharf T, Quinlan LR, et al. A Human-Centered Design Methodology to Enhance the Usability, Human Factors, and User Experience of Connected Health Systems: A Three-Phase Methodology. JMIR Hum Factors. 2017;4:e5443.CrossRef
56.
Carayon P, Wooldridge A, Hoonakker P, Hundt AS, Kelly MM. SEIPS 3.0: Human-centered design of the patient journey for patient safety. Appl Ergon. 2020;84:103033.PubMedPubMedCentralCrossRef
57.
Ahmad A, Wilson A, Thomas-Gibson S, Suzuki N, Humphries A, Haycock A, et al. Learning Curve Of Optical Diagnosis With A Resect And Discard Strategy For Screening Colonoscopy: Preliminary Results From The Discard3 Study. In: Endoscopy. Georg Thieme Verlag KG; 2021. p. OP184.
58.
Cruz Rivera S, Liu X, Chan A-W, Denniston AK, Calvert MJ, Ashrafian H, et al. Guidelines for clinical trial protocols for interventions involving artificial intelligence: the SPIRIT-AI extension. Lancet Digit Health. 2020;2:e549–60.PubMedCrossRef
59.
Liu X, Rivera SC, Faes L, di Ferrante Ruffano L, Yau C, Keane PA, et al. Reporting guidelines for clinical trials evaluating artificial intelligence interventions are needed. Nat Med. 2019;25:1467–8.CrossRef
60.
Vasey B, Nagendran M, Campbell B, Clifton DA, Collins GS, Denaxas S, et al. Reporting guideline for the early stage clinical evaluation of decision support systems driven by artificial intelligence: DECIDE-AI. BMJ. 2022;377:e070904.PubMedPubMedCentralCrossRef
61.
Rezaei M, Jafari-Sadeghi V, Cao D, Mahdiraji HA. Key indicators of ethical challenges in digital healthcare: A combined Delphi exploration and confirmative factor analysis approach with evidence from Khorasan province in Iran. Technol Forecast Soc Change. 2021;167:120724.CrossRef
62.
Liaw S-T, Liyanage H, Kuziemsky C, Terry AL, Schreiber R, Jonnagaddala J, et al. Ethical Use of Electronic Health Record Data and Artificial Intelligence: Recommendations of the Primary Care Informatics Working Group of the International Medical Informatics Association. Yearb Med Inform. 2020;29:51–7.PubMedPubMedCentralCrossRef
63.
Jungwirth D, Haluza D. Information and communication technology and the future of healthcare: Results of a multi-scenario Delphi survey. Health Informatics J. 2019;25:161–73.PubMedCrossRef
64.
Liyanage H, Liaw S-T, Jonnagaddala J, Schreiber R, Kuziemsky C, Terry AL, et al. Artificial Intelligence in Primary Health Care: Perceptions, Issues, and Challenges: Primary Health Care Informatics Working Group Contribution to the Yearbook of Medical Informatics 2019. Yearb Med Inform. 2019;28:041–6.CrossRef
65.
Ravensbergen WM, Drewes YM, Hilderink HBM, Verschuuren M, Gussekloo J, Vonk RAA. Combined impact of future trends on healthcare utilisation of older people: A Delphi study. Health Policy. 2019;123:947–54.PubMedCrossRef
66.
Blease C, Kharko A, Locher C, DesRoches CM, Mandl KD. US primary care in 2029: A Delphi survey on the impact of machine learning. PLoS ONE. 2020;15:e0239947.PubMedPubMedCentralCrossRef
67.
Ermolina A, Tiberius V. Voice-Controlled Intelligent Personal Assistants in Health Care: International Delphi Study. J Med Internet Res. 2021;23:e25312.PubMedPubMedCentralCrossRef
68.
Deng J, Huang S, Wang L, Deng W, Yang T. Conceptual Framework for Smart Health: A Multi-Dimensional Model Using IPO Logic to Link Drivers and Outcomes. Int J Environ Res Public Health. 2022;19:16742.PubMedPubMedCentralCrossRef
69.
Koebe P, Bohnet-Joschko S. What’s next in hospital digitization? A Delphi-based scenario study. Eur J Futur Res. 2023;11:10.CrossRef
70.
Porter AL, Ashton WB, Clar G, Coates JF, Cuhls K, Cunningham SW, et al. Technology futures analysis: Toward integration of the field and new methods. Technol Forecast Soc Change. 2004;71:287–303.CrossRef
71.
Peterson GD, Cumming GS, Carpenter SR. Scenario Planning: a Tool for Conservation in an Uncertain World. Conserv Biol. 2003;17:358–66.CrossRef
72.
Loveridge D. Experts and foresight: review and experience. Int J Foresight Innov Policy. 2004;1:33.CrossRef
73.
Cunningham SW, van der Lei TE. Decision-making for new technology: A multi-actor, multi-objective method. Technol Forecast Soc Change. 2009;76:26–38.CrossRef
74.
75.
76.
Rowe G, Wright G. Expert Opinions in Forecasting: The Role of the Delphi Technique. In: Armstrong JS, editor. Principles of Forecasting: A Handbook for Researchers and Practitioners. Boston, MA: Springer, US; 2001. p. 125–44.CrossRef
77.
Dalkey N, Helmer O. An Experimental Application of the DELPHI Method to the Use of Experts. Manag Sci. 1963;9:458–67.CrossRef
78.
Aengenheyster S, Cuhls K, Gerhold L, Heiskanen-Schüttler M, Huck J, Muszynska M. Real-Time Delphi in practice — A comparative analysis of existing software-based tools. Technol Forecast Soc Change. 2017;118:15–27.CrossRef
79.
Gordon T, Pease A. RT Delphi: An efficient, “round-less” almost real time Delphi method. Technol Forecast Soc Change. 2006;73:321–33.CrossRef
80.
Beiderbeck D, Frevel N, von der Gracht HA, Schmidt SL, Schweitzer VM. Preparing, conducting, and analyzing Delphi surveys: Cross-disciplinary practices, new directions, and advancements. MethodsX. 2021;8:101401.PubMedPubMedCentralCrossRef
81.
Schmalz U, Spinler S, Ringbeck J. Lessons Learned from a Two-Round Delphi-based Scenario Study. MethodsX. 2021;8:101179.PubMedCrossRef
82.
Wehrle M, Lechler S, von der Gracht HA, Hartmann E. Digitalization and its Impact on the Future Role of SCM Executives in Talent Management – An International Cross-Industry Delphi Study. J Bus Logist. 2020;41:356–83.CrossRef
83.
Beiderbeck D, Frevel N, von der Gracht HA, Schmidt SL, Schweitzer VM. The impact of COVID-19 on the European football ecosystem – A Delphi-based scenario analysis. Technol Forecast Soc Change. 2021;165:120577.CrossRef
84.
Saritas O, Oner MA. Systemic analysis of UK foresight results: Joint application of integrated management model and roadmapping. Technol Forecast Soc Change. 2004;71:27–65.CrossRef
85.
Porter AL, Jin X-Y, Gilmour JE, Cunningham S. Technology opportunities analysis: Integrating technology mo. SRA J. 1994;26:21.
86.
Dunne A, Raby F. Speculative Everything: Design, Fiction, and Social Dreaming. MIT Press. 2013. 1st ed. JSTOR.
87.
Dunphy SM, Herbig PR, Howes ME. The innovation funnel. Technol Forecast Soc Change. 1996;53:279–92.CrossRef
88.
Ecken P, Gnatzy T, von der Gracht H. Desirability bias in foresight: Consequences for decision quality based on Delphi results. Technol Forecast Soc Change. 2011;78:1654–70.CrossRef
89.
Niederberger M, Spranger J. Delphi Technique in Health Sciences: A Map. Front Public Health. 2020;8:457.
90.
Belton I, MacDonald A, Wright G, Hamlin I. Improving the practical application of the Delphi method in group-based judgment: A six-step prescription for a well-founded and defensible process. Technol Forecast Soc Change. 2019;147:72–82.CrossRef
91.
Devaney L, Henchion M. Who is a Delphi ‘expert’? Reflections on a bioeconomy expert selection procedure from Ireland. Futures. 2018;99:45–55.CrossRef
92.
Sadler GR, Lee H-C, Lim RS-H, Fullerton J. Research Article: Recruitment of hard-to-reach population subgroups via adaptations of the snowball sampling strategy. Nurs Health Sci. 2010;12:369–74.PubMedPubMedCentralCrossRef
93.
Taylor E. We Agree, Don’t We? The Delphi Method for Health Environments Research. HERD Health Environ Res Des J. 2020;13:11–23.
94.
Warth J, von der Gracht HA, Darkow I-L. A dissent-based approach for multi-stakeholder scenario development — The future of electric drive vehicles. Technol Forecast Soc Change. 2013;80:566–83.CrossRef
Metadata
Title
Forecasting the future of smart hospitals: findings from a real-time delphi study
Authors
Florian Jovy-Klein
Susan Stead
Torsten Oliver Salge
Jil Sander
Anke Diehl
David Antons
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Health Services Research / Issue 1/2024
Electronic ISSN: 1472-6963
DOI
https://doi.org/10.1186/s12913-024-11895-z

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