Impact of Artificial Intelligence–Based Technology on Nurse Management: A Systematic Review
Open accessAbstract
Aim: To describe the use of artificial intelligence (AI) by nurse managers to enhance management, leadership, and healthcare outcomes. Background: AI represents a significant transformation in healthcare management by enhancing decision‐making, communication, and resource optimization. However, the integration and strategic application of AI in nursing management are underexplored, particularly regarding its impact on leadership roles and healthcare delivery. Methods: Methodological guidelines described by PRISMA were followed, and quality was assessed using the Joanna Briggs Institute (JBI) methodology. The databases searched included the Web of Science, Scopus, CINAHLi, and PubMed. The review included quantitative, qualitative, and mixed‐method studies published between January 2015 and April 2024. Results: Fourteen studies were selected for the review. The key findings indicate that AI technologies facilitate better resource management, risk assessment, and decision‐making. AI also supports nurse managers in leading changes, enhancing communication, and optimizing administrative tasks. Conclusion: AI has been progressively integrated into nursing management, demonstrating significant benefits in operational efficiency, decision support, and leadership enhancement. However, challenges, such as resistance to technological change and ethical complexities, need to be addressed. Implications for Nursing Management: Specific training programs for nurse managers are essential to optimize the integration of AI. Such programs should focus on the management of AI applications and data analyses. In addition, creating interdisciplinary groups involving nurse managers, AI developers, and nursing staff is crucial for tailoring AI solutions to meet the unique needs of healthcare settings.
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AI-driven predictive analytics and clinical decision support systems contribute to optimized patient outcomes by enabling early intervention and personalized care strategies.
“Similarly, AI-driven predictive analytics and clinical decision support systems contribute to optimized patient outcomes by enabling early intervention and personalized care strategies [32,45].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [45]
The Introduction (page unavailable) describes AI-based patient risk prediction, while the Discussion (page unavailable) says predictive analytics supports decision-making. The Conclusions (page unavailable) also link AI in nurse management to improved patient care and outcomes. Our copy does not establish that clinical decision support systems improve outcomes specifically by enabling early intervention and personalized care strategies. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAutomating administrative tasks such as documentation, scheduling, and resource allocation reduces nurse workload.
“Automating administrative tasks such as documentation, scheduling, and resource allocation not only reduces nurse workload but also enhances workflow efficiency and patient safety [45,57].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [45]
The Introduction says AI can reduce administrative work in nursing and automate routine healthcare-administration tasks; Results §3.4 also reports reduced administrative burdens. The Conclusions discuss resource allocation and decreased workloads, but the copy does not specifically establish that automating documentation or scheduling reduces nurse workload, and it frames the workload benefit as a potential outcome. (LLM, conf=0.84)
Experimental — accuracy not yet measuredContinuous professional development is essential for equipping nurse educators with the skills needed to implement AI tools effectively.
“Furthermore, continuous professional development was identified as essential for equipping nurse educators with the skills needed to implement AI tools effectively [42,45].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [45]
The Conclusions recommend continuous education and training to equip nurse managers to use AI effectively. The claim applies that recommendation to nurse educators, a different group not addressed in the relevant passages. Page numbers were not provided in our copy. (LLM, conf=0.87)
Experimental — accuracy not yet measuredAutomating administrative tasks enhances patient safety.
“Automating administrative tasks such as documentation, scheduling, and resource allocation not only reduces nurse workload but also enhances workflow efficiency and patient safety [45,57].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [45]
The Introduction says AI can automate routine administrative tasks, reduce administrative burdens, and improve operational efficiency. Results §3.4 separately reports that technologically advanced healthcare environments enhance safety, but it does not establish that automating administrative tasks itself improves patient safety. No page numbers are provided in our copy. (LLM, conf=0.85)
Experimental — accuracy not yet measuredThis gap risks fostering overreliance on AI systems, potentially eroding essential interpersonal skills and undermining core nursing values such as empathy and patient-centered care.
“This gap risks fostering overreliance on AI systems, potentially eroding essential interpersonal skills and undermining core nursing values such as empathy and patient-centered care [45,57].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [45]
The Discussion identifies loss of essential communication skills due to technological dependence as a threat, supporting the concern about overreliance and eroding interpersonal skills. It does not say that this dependence undermines empathy or patient-centered care; its discussion of ethical risks focuses instead on privacy, autonomy, and responsibility. The indexed passages provide section labels but no page numbers. (LLM, conf=0.86)
Experimental — accuracy not yet measured
Systemic barriers such as financial constraints, infrastructure readiness, and workforce training remain inadequately addressed.
“where systemic barriers such as financial constraints, infrastructure readiness, and workforce training remain inadequately addressed [39,45].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [45]
This disconnect hinders the realization of AI's transformative potential in real-world nursing environments.
“This disconnect hinders the realization of AI’s transformative potential in real-world nursing environments, where systemic barriers such as financial constraints, infrastructure readiness, and workforce training remain inadequately addressed [39,45].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [45]
Automating administrative tasks enhances workflow efficiency.
“Automating administrative tasks such as documentation, scheduling, and resource allocation not only reduces nurse workload but also enhances workflow efficiency and patient safety [45,57].”
From The Role of AI in Nursing Education and Practice: Umbrella Review (2025) · cites this paper as [45]
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Cites
Shea, B. J., Reeves, B. C., Wells, G., Thuku, M., Hamel, C., Moran, J., Moher, D., Tugwell, P., Welch, V., Kristjansson, E., et al. (2017) · BMJ · doi:10.1136/bmj.j4008 · Cited by 10,636 · see citing works
Guidance for conducting systematic scoping reviews
Peters, M. D. J., Godfrey, C. M., Khalil, H., McInerney, P., Parker, D., & Soares, C. B. (2015) · International Journal of Evidence-Based Healthcare · doi:10.1097/xeb.0000000000000050 · Cited by 7,472 · see citing works
The global landscape of AI ethics guidelines
Jobin, A., Ienca, M., & Vayena, E. (2019) · Nature Machine Intelligence · doi:10.1038/s42256-019-0088-2 · Cited by 6,220 · cited 1× · see citing works
“Jobin et al. [60] and Mittelstadt et al. [61] argued that there is a crucial need to enhance knowledge of how to resolve ethical dilemmas related to privacy, autonomy, and responsibility.”
The PRISMA 2020 statement: An updated guideline for reporting systematic reviews
Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., et al. (2021) · PLoS Medicine · doi:10.1371/journal.pmed.1003583 · Cited by 5,148 · see citing works
Synthesis without meta-analysis (SWiM) in systematic reviews: reporting guideline
Campbell, M., McKenzie, J. E., Sowden, A., Katikireddi, S. V., Brennan, S. E., Ellis, S., Hartmann-Boyce, J., Ryan, R., Shepperd, S., Thomas, J., et al. (2020) · BMJ · doi:10.1136/bmj.l6890 · Cited by 4,628 · see citing works
Rajkomar, A., Dean, J., & Kohane, I. (2019) · New England Journal of Medicine · doi:10.1056/nejmra1814259 · Cited by 4,579 · see citing works
JBI Manual for Evidence Synthesis
(2020) · JBI eBooks · doi:10.46658/jbimes-20-01 · Cited by 4,165 · see citing works
The potential for artificial intelligence in healthcare
Davenport, T., & Kalakota, R. (2019) · Future Healthcare Journal · doi:10.7861/futurehosp.6-2-94 · Cited by 3,800 · see citing works
The ethics of algorithms: Mapping the debate
Mittelstadt, B. D., Allo, P., Taddeo, M., Wachter, S., & Floridi, L. (2016) · Big Data & Society · doi:10.1177/2053951716679679 · Cited by 3,432 · see citing works
Aromataris, E., Fernandez, R., Godfrey, C. M., Holly, C., Khalil, H., & Tungpunkom, P. (2015) · International Journal of Evidence-Based Healthcare · doi:10.1097/xeb.0000000000000055 · Cited by 2,587 · see citing works
Lockwood, C., Munn, Z., & Porritt, K. (2015) · International Journal of Evidence-Based Healthcare · doi:10.1097/xeb.0000000000000062 · Cited by 2,456 · see citing works
Artificial Intelligence in Human Resources Management: Challenges and a Path Forward
Tambe, P., Cappelli, P., & Yakubovich, V. (2019) · California Management Review · doi:10.1177/0008125619867910 · Cited by 1,609 · see citing works
Machine learning in medicine: a practical introduction
Sidey-Gibbons, J. A. M., & Sidey-Gibbons, C. J. (2019) · BMC Medical Research Methodology · doi:10.1186/s12874-019-0681-4 · Cited by 1,308 · see citing works
The role of artificial intelligence in healthcare: a structured literature review
Secinaro, S. F., Calandra, D., Secinaro, A., Muthurangu, V., & Biancone, P. (2021) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-021-01488-9 · Cited by 1,203 · see citing works
Artificial Intelligence in Precision Cardiovascular Medicine
Krittanawong, C., Zhang, H., Wang, Z., Aydar, M., & Kitai, T. (2017) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2017.03.571 · Cited by 1,126 · see citing works
Lee, D., & Yoon, S. N. (2021) · International Journal of Environmental Research and Public Health · doi:10.3390/ijerph18010271 · Cited by 1,017 · see citing works
A Review of the Role of Artificial Intelligence in Healthcare
Al Kuwaiti, A., Nazer, K., Al-Reedy, A., Al-Shehri, S., Al-Muhanna, A., Subbarayalu, A. V., Al Muhanna, D., & Al-Muhanna, F. A. (2023) · Journal of Personalized Medicine · doi:10.3390/jpm13060951 · Cited by 915 · see citing works
Wahl, B., Cossy-Gantner, A., Germann, S., & Schwalbe, N. R. (2018) · BMJ Global Health · doi:10.1136/bmjgh-2018-000798 · Cited by 796 · see citing works
Artificial intelligence: the next digital frontier?
Bughin, J., Hazan, E., Ramaswamy, S., Chui, M., Allas, T., Dahlström, P., Henke, N., & Trench, M. (2017) · Cited by 569 · see citing works
Reddy, S. (2024) · Implementation Science · doi:10.1186/s13012-024-01357-9 · Cited by 554 · cited 1× · see citing works
“In this regard, various studies suggest that knowledge management will help transform healthcare organizations by advancing AI, which, as our fndings show, points to data security as one of the main ethical barriers [67, 68].”
Role of Artificial Intelligence Applications in Real-Life Clinical Practice: Systematic Review
Yin, J., Ngiam, K. Y., & Teo, H. H. (2021) · Journal of Medical Internet Research · doi:10.2196/25759 · Cited by 419 · see citing works
Will artificial intelligence solve the human resource crisis in healthcare?
Meskó, B., Hetényi, G., & Győrffy, Z. (2018) · BMC Health Services Research · doi:10.1186/s12913-018-3359-4 · Cited by 360 · see citing works
Knowledge management and its link to artificial intelligence
Liebowitz, J. (2001) · Expert Systems with Applications · doi:10.1016/s0957-4174(00)00044-0 · Cited by 351 · see citing works
Artificial intelligence in nursing and midwifery: A systematic review
O'Connor, S., Yan, Y., Thilo, F. J. S., Felzmann, H., Dowding, D., & Lee, J. J. (2023) · Journal of Clinical Nursing · doi:10.1111/jocn.16478 · Cited by 351 · see citing works
Fairness perceptions of algorithmic decision-making: A systematic review of the empirical literature
Starke, C., Baleis, J., Keller, B., & Marcinkowski, F. (2022) · Big Data & Society · doi:10.1177/20539517221115189 · Cited by 316 · see citing works
Application Scenarios for Artificial Intelligence in Nursing Care: Rapid Review
Seibert, K., Domhoff, D., Bruch, D., Schulte-Althoff, M., Fürstenau, D., Biessmann, F., & Wolf-Ostermann, K. (2021) · Journal of Medical Internet Research · doi:10.2196/26522 · Cited by 314 · see citing works
How artificial intelligence is changing nursing
Robert, N. (2019) · Nursing Management · doi:10.1097/01.numa.0000578988.56622.21 · Cited by 311 · see citing works
Artificial intelligence in healthcare: opportunities and risk for future
Sunarti, S., Fadzlul Rahman, F., Naufal, M., Risky, M., Febriyanto, K., & Masnina, R. (2021) · Gaceta Sanitaria · doi:10.1016/j.gaceta.2020.12.019 · Cited by 274 · see citing works
What Is Artificial Intelligence?
Jeavons, A. (2017) · Research World · doi:10.1002/rwm3.20554 · Cited by 188 · see citing works
Application of artificial intelligence in surgery
Zhou, X.-Y., Guo, Y., Shen, M., & Yang, G.-Z. (2020) · Frontiers of Medicine · doi:10.1007/s11684-020-0770-0 · Cited by 181 · see citing works
Using artificial intelligence to improve hospital inpatient care
Neill, D. B. (2013) · IEEE Intelligent Systems · doi:10.1109/mis.2013.51 · Cited by 177 · see citing works
Zahlan, A., Ranjan, R. P., & Hayes, D. (2023) · Technology in Society · doi:10.1016/j.techsoc.2023.102321 · Cited by 166 · see citing works
Hajkowicz, S. A., Sanderson, C., Karimi, S., Bratanova, A., & Naughtin, C. K. (2023) · Technology in Society · doi:10.1016/j.techsoc.2023.102260 · Cited by 155 · see citing works
Applications of Artificial Intelligence in Nursing Care: A Systematic Review
Martinez‐Ortigosa, A., Martinez-Granados, A., Gil-Hernández, E., Rodríguez-Arrastia, M. J., Ropero‐Padilla, C., & Román, P. (2023) · Journal of Nursing Management · doi:10.1155/2023/3219127 · Cited by 151 · see citing works
Artificial intelligence, machine learning, and deep learning for clinical outcome prediction
Pettit, R. W., Fullem, R. L., Cheng, C., & Amos, C. I. (2021) · Emerging Topics in Life Sciences · doi:10.1042/etls20210246 · Cited by 137 · see citing works
Synthesizing Dimensions of Digital Maturity in Hospitals: Systematic Review
Duncan, R., Eden, R., Woods, L. S., Wong, I. Y., & Sullivan, C. M. (2022) · Journal of Medical Internet Research · doi:10.2196/32994 · Cited by 123 · see citing works
Artificial Intelligence and Nursing: The Future Is Now
Clancy, T. R. (2020) · JONA: The Journal of Nursing Administration · doi:10.1097/nna.0000000000000855 · Cited by 103 · see citing works
Managerial competence of first‐line nurse managers: A concept analysis
Gunawan, J., & Aungsuroch, Y. (2017) · International Journal of Nursing Practice · doi:10.1111/ijn.12502 · Cited by 97 · see citing works
Nurse managers’ competencies: A scoping review
González‐García, A., Pinto‐Carral, A., Pérez‐González, S., & Marqués‐Sánchez, P. (2021) · Journal of Nursing Management · doi:10.1111/jonm.13380 · Cited by 91 · see citing works
Self-citationThe Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings
Pailaha, A. D. (2023) · SAGE Open Nursing · doi:10.1177/23779608231196847 · Cited by 85 · see citing works
The Future of Medicine: Harnessing the Power of AI for Revolutionizing Healthcare
Harry, A. (2023) · International Journal of Multidisciplinary Sciences and Arts · doi:10.47709/ijmdsa.v2i1.2395 · Cited by 80 · see citing works
Is AI intelligent? An assessment of artificial intelligence, 70 years after Turing
Hoffmann, C. H. (2022) · Technology in Society · doi:10.1016/j.techsoc.2022.101893 · Cited by 79 · see citing works
Bhatia, R. (2020) · Technology in Society · doi:10.1016/j.techsoc.2020.101465 · Cited by 78 · see citing works
Artificial Intelligence in Psychiatry
Fakhoury, M. (2019) · Advances in experimental medicine and biology · doi:10.1007/978-981-32-9721-0_6 · Cited by 77 · see citing works
Trends in artificial intelligence in nursing: Impacts on nursing management
Chang, C., Jen, H., & Su, W. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13770 · Cited by 76 · see citing works
Ergin, E., Karaarslan, D., Şahan, S., & Çınar Yücel, Ş. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13646 · Cited by 70 · see citing works
Shelmerdine, S. C., Arthurs, O. J., Denniston, A. K., & Sebire, N. J. (2021) · BMJ Health & Care Informatics · doi:10.1136/bmjhci-2021-100385 · Cited by 67 · see citing works
Chen, Y., Moreira, P., Liu, W., Monachino, M. S., Nguyen, T. L. H., & Wang, A. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13851 · Cited by 67 · see citing works
Barriers Related to AI Implementation in Supply Chain Management
Shrivastav, M. (2022) · Journal of Global Information Management · doi:10.4018/jgim.296725 · Cited by 64 · see citing works
Transforming clinical data into wisdom: Artificial intelligence implications for nurse leaders
Cato, K. D., McGrow, K., & Rossetti, S. C. (2020) · Nursing Management · doi:10.1097/01.numa.0000719396.83518.d6 · Cited by 49 · see citing works
Laukka, E., Hammarén, M., & Kanste, O. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13769 · Cited by 44 · see citing works
Huang, K., Jiao, Z., Cai, Y., & Zhong, Z. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13787 · Cited by 38 · see citing works
An, R., Chang, G., Fan, Y., Ji, L., Wang, X., & Hong, S. L. (2021) · Journal of Nursing Management · doi:10.1111/jonm.13284 · Cited by 37 · see citing works
Li, X., Cheng, M., & Xu, J. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13749 · Cited by 36 · see citing works
Fuller, R., & Hansen, A. (2019) · Nursing Administration Quarterly · doi:10.1097/naq.0000000000000354 · Cited by 32 · see citing works
Barriers to Implementing an Artificial Intelligence Model for Unplanned Readmissions
Baxter, S. L., Bass, J. S., & Sitapati, A. M. (2020) · ACI Open · doi:10.1055/s-0040-1716748 · Cited by 30 · see citing works
Wang, Y., Xie, C., Liang, C., Zhou, P., & Lu, L. (2022) · Journal of Nursing Management · doi:10.1111/jonm.13823 · Cited by 30 · see citing works
Smith, T. G., Norasi, H., Herbst, K. M., Kendrick, M. L., Curry, T. B., Grantcharov, T., Palter, V. N., Hallbeck, M. S., & Cleary, S. P. (2022) · Mayo Clinic Proceedings Innovations Quality & Outcomes · doi:10.1016/j.mayocpiqo.2022.09.004 · Cited by 24 · see citing works
Analysis of Adverse Drug Reactions Identified in Nursing Notes Using Reinforcement Learning
Jeon, E., Kim, Y.-S., Park, R. W., Park, R. W., Shin, H., & Park, H. (2020) · Healthcare Informatics Research · doi:10.4258/hir.2020.26.2.104 · Cited by 22 · see citing works
Hwang, G., Chang, P.-Y., Tseng, W.-Y., Chou, C.-A., Wu, C.-H., & Tu, Y. (2022) · CIN Computers Informatics Nursing · doi:10.1097/cin.0000000000000897 · Cited by 22 · see citing works
Han, J., Kang, H., & Kwon, G. H. (2019) · Journal of Nursing Management · doi:10.1111/jonm.12875 · Cited by 19 · see citing works
Spotlight on Leadership: What Nurse Leaders Need to Know About Artificial Intelligence
Fontenot, J. (2024) · JONA: The Journal of Nursing Administration · doi:10.1097/nna.0000000000001384 · Cited by 15 · see citing works
Technology Solutions for Nurse Leaders
Clancy, T. R. (2020) · Nursing Administration Quarterly · doi:10.1097/naq.0000000000000439 · Cited by 10 · see citing works
Innovations in Nursing Workforce Management
Blouin, A. S. (2023) · JONA The Journal of Nursing Administration · doi:10.1097/nna.0000000000001352 · Cited by 7 · see citing works
Identification of Patient Prescribing Predicting Cancer Diagnosis Using Boosted Decision Trees
French, J., Chen, C., Henson, K. E., et al. (2019) · Lecture notes in computer science · doi:10.1007/978-3-030-21642-9_42 · Cited by 6 · see citing works
McGoldrick, T. B. (2008) · PubMed · Cited by 1 · see citing works
Flagged first, then by citations
Research works printed but not linked: 3
- [25]Elsayed W. Sleem W. Nurse Managers’ Perspectives and Attitude Toward Using Artificial Intelligence Technology in Health Settings Assiut Scientific Nursing Journal 2021 9 24 182 192
- [56]Davenport T. H. Ronanki R. Wheaton J. Nguyen A. Feature Artificial Intelligence for the Real World Harvard Business Review 2018 11 209 216
- [59]Ransbotham S. Khodabandeh S. Fehling R. Lafountain B. Kiron D. Winning With AI 2019 Cambridge, MA MIT Sloan Management Review and Boston Consulting Group
Cited by
Sorted by citations
Next-generation agentic AI for transforming healthcare
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The Role of AI in Nursing Education and Practice: Umbrella Review
El Arab, R. A., Al Moosa, O. A., Abuadas, F. H., & Somerville, J. (2025) · Journal of Medical Internet Research · doi:10.2196/69881 · Cited by 160 · see citing works
“Furthermore, continuous professional development was identified as essential for equipping nurse educators with the skills needed to implement AI tools effectively [42,45].”
InfluentialEl-Banna, M. M., Sajid, M. R., Rizvi, M. R., Sami, W., & McNelis, A. M. (2025) · Frontiers in Medicine · doi:10.3389/fmed.2025.1681784 · Cited by 62 · see citing works
- Authors
- Gonzalez-Garcia, A., Pérez-González, S., Benavides, C., Pinto-Carral, A., Quiroga-Sánchez, E., & Marqués-Sánchez, P.
- Affiliations
- Universidad de León (ES)
- ORCID
- Venue
- Journal of Nursing Management
- Publisher
- Wiley
- Published
- 2024-01
- Volume
- 2024
- Issue
- 1
- Pages
- 3537964-3537964
- ISSN
- 0966-0429 (ISSN-L)
- 1365-2834
- PMC
- PMC11919197
- OpenAlex
- W4403360708
- Cited by
- 53
- References
- 66
- Citations by year
- 2026: 26 · 2025: 27
- FWCI
- 1.21
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Nursing Management (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Buleria (Universidad de León) (submittedVersion, other-oa)
- Topics
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Health Sciences › Nursing › Research and Theory › Nursing education and management
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Keywords
- artificial intelligence, nursing management, nurse managers, leadership, healthcare decision-making, resource management, risk assessment, resistance to technology, AI ethics, nurse manager training, interdisciplinary collaboration, health care delivery
- MeSH terms
- Artificial Intelligence, Humans, Leadership, Nurse Administrators
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