Utilizing Robotics in Health Care: Increasing the Efficiency and Accuracy of Patient Therapy
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Healthcare, Rehabilitation, Robotics
Abstract
The healthcare industry has increasingly embraced technological innovations to improve patient care and streamline medical procedures. Among these advancements, robotics has emerged as a powerful tool in enhancing the efficiency and accuracy of patient therapy. Robotics in healthcare can perform tasks such as surgery, rehabilitation, and patient monitoring with precision, leading to better outcomes and reduced human error. This study explores the integration of robotics in healthcare, focusing on its impact on patient therapy, including rehabilitation and therapeutic assistance. Using a mixed-methods approach, this research evaluates both qualitative and quantitative data from healthcare facilities implementing robotic technologies. The findings show that robotic systems have led to a 30% improvement in rehabilitation outcomes, 20% reduction in therapy time, and significant enhancement in treatment accuracy. These results highlight the potential of robotics to not only increase the efficiency of therapy but also provide more personalized and consistent care. The study concludes that the widespread adoption of robotics in healthcare can significantly improve patient therapy and overall healthcare delivery. However, challenges such as cost, integration with existing healthcare systems, and training requirements must be addressed. Future research should focus
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References
Al-Hamadani, M. N. A. (2024). Reinforcement Learning Algorithms and Applications in Healthcare and Robotics: A Comprehensive and Systematic Review. Sensors, 24(8). https://doi.org/10.3390/s24082461
Amado, G. C. (2022). Vertical integration in healthcare: What does literature say about improvements on quality, access, efficiency, and costs containment? International Journal of Health Planning and Management, 37(3), 1252–1298. https://doi.org/10.1002/hpm.3407
Ar-Reyouchi, E. M. (2022). Protocol Wireless Medical Sensor Networks in IoT for the Efficiency of Healthcare. IEEE Internet of Things Journal, 9(13), 10693–10704. https://doi.org/10.1109/JIOT.2021.3125886
Bauer, G. R. (2021). Intersectionality in quantitative research: A systematic review of its emergence and applications of theory and methods. SSM - Population Health, 14(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.ssmph.2021.100798
Boch, A. (2023). Beyond the Metal Flesh: Understanding the Intersection between Bio- and AI Ethics for Robotics in Healthcare. Robotics, 12(4). https://doi.org/10.3390/robotics12040110
Breitenbach, M. C. (2021). Global Healthcare Resource Efficiency in the Management of COVID-19 Death and Infection Prevalence Rates. Frontiers in Public Health, 9(Query date: 2025-02-03 20:10:29). https://doi.org/10.3389/fpubh.2021.638481
Breuer, S. (2023). How Engineers’ Imaginaries of Healthcare Shape Design and User Engagement: A Case Study of a Robotics Initiative for Geriatric Healthcare AI Applications. ACM Transactions on Computer-Human Interaction, 30(2). https://doi.org/10.1145/3577010
Brunete, A. (2021). Smart assistive architecture for the integration of IoT devices, robotic systems, and multimodal interfaces in healthcare environments. Sensors, 21(6), 1–25. https://doi.org/10.3390/s21062212
Chen, S. (2024). Liquid Metal Functionalization Innovations in Wearables and Soft Robotics for Smart Healthcare Applications. Advanced Functional Materials, 34(31). https://doi.org/10.1002/adfm.202309989
Dion, H. (2023). Hospitals management transformative initiatives; towards energy efficiency and environmental sustainability in healthcare facilities. Journal of Engineering, Design and Technology, 21(2), 552–584. https://doi.org/10.1108/JEDT-04-2022-0200
Elendu, C. (2023). Ethical implications of AI and robotics in healthcare: A review. Medicine (United States), 102(50). https://doi.org/10.1097/MD.0000000000036671
Gao, F. (2021). Efficiency of fine scale and spatial regression in modelling associations between healthcare service spatial accessibility and their utilization. International Journal of Health Geographics, 20(1). https://doi.org/10.1186/s12942-021-00276-y
Hu, T. (2021). Movable oil content evaluation of lacustrine organic-rich shales: Methods and a novel quantitative evaluation model. Earth-Science Reviews, 214(Query date: 2024-12-01 09:57:11). https://doi.org/10.1016/j.earscirev.2021.103545
Izadikhah, M. (2022). A fuzzy stochastic slacks-based data envelopment analysis model with application to healthcare efficiency. Healthcare Analytics, 2(Query date: 2025-02-03 20:10:29). https://doi.org/10.1016/j.health.2022.100038
Janakiraman, R. (2023). The Effects of Health Information Exchange Access on Healthcare Quality and Efficiency: An Empirical Investigation. Management Science, 69(2), 791–811. https://doi.org/10.1287/mnsc.2022.4378
Jian, C. (2020). Quantitative PCR provides a simple and accessible method for quantitative microbiota profiling. PLoS ONE, 15(1). https://doi.org/10.1371/journal.pone.0227285
Jung, S. (2023). Efficiency Measurement Using Data Envelopment Analysis (DEA) in Public Healthcare: Research Trends from 2017 to 2022. Processes, 11(3). https://doi.org/10.3390/pr11030811
Kaur, J. (2023a). How is Robotic Process Automation Revolutionising the Way Healthcare Sector Works? Lecture Notes in Networks and Systems, 757(Query date: 2025-02-03 20:09:44), 1037–1055. https://doi.org/10.1007/978-981-99-5166-6_70
Kaur, J. (2023b). Robotic Process Automation in Healthcare Sector. E3S Web of Conferences, 391(Query date: 2025-02-03 20:09:44). https://doi.org/10.1051/e3sconf/202339101008
Khang, A. (2024). Quantum-based robotics in the high-tech healthcare industry: Innovations and applications. Medical Robotics and AI-Assisted Diagnostics for a High-Tech Healthcare Industry, Query date: 2025-02-03 20:09:44, 1–27. https://doi.org/10.4018/979-8-3693-2105-8.ch001
Kolpashchikov, D. (2022). Robotics in Healthcare. Intelligent Systems Reference Library, 212(Query date: 2025-02-03 20:09:44), 281–306. https://doi.org/10.1007/978-3-030-83620-7_12
Liu, H. (2022a). A study on the efficiency of pediatric healthcare services and its influencing factors in China——Estimation of a three-stage DEA model based on provincial-level data. Socio-Economic Planning Sciences, 84(Query date: 2025-02-03 20:10:29). https://doi.org/10.1016/j.seps.2022.101315
Liu, H. (2022b). Assessing the financial efficiency of healthcare services and its influencing factors of financial development: Fresh evidences from three-stage DEA model based on Chinese provincial level data. Environmental Science and Pollution Research, 29(15), 21955–21967. https://doi.org/10.1007/s11356-021-17005-4
McFadden, D. (2021). Quantitative methods for analysing travel behaviour ofindividuals: Some recent developments. Behavioural Travel Modelling, Query date: 2024-12-01 09:57:11, 279–318.
Miszczynska, K. (2022). Measuring the efficiency of the healthcare sector in Poland – a window-DEA evaluation. International Journal of Productivity and Performance Management, 71(7), 2743–2770. https://doi.org/10.1108/IJPPM-06-2020-0276
Mokrini, A. E. (2022). A decision-support tool for policy makers in healthcare supply chains to balance between perceived risk in logistics outsourcing and cost-efficiency. Expert Systems with Applications, 201(Query date: 2025-02-03 20:10:29). https://doi.org/10.1016/j.eswa.2022.116999
Mourad, N. (2021). Appraising healthcare systems’ efficiency in facing covid-19 through data envelopment analysis. Decision Science Letters, 10(3), 301–310. https://doi.org/10.5267/j.dsl.2021.2.007
Mueller, A. V. (2020). Quantitative Method for Comparative Assessment of Particle Removal Efficiency of Fabric Masks as Alternatives to Standard Surgical Masks for PPE. Matter, 3(3), 950–962. https://doi.org/10.1016/j.matt.2020.07.006
Nepomuceno, T. C. C. (2022). The Core of Healthcare Efficiency: A Comprehensive Bibliometric Review on Frontier Analysis of Hospitals. Healthcare (Switzerland), 10(7). https://doi.org/10.3390/healthcare10071316
Osipov, V. S. (2021). Impact of Digital Technologies on the Efficiency of Healthcare Delivery. Studies in Computational Intelligence, 933(Query date: 2025-02-03 20:10:29), 243–261. https://doi.org/10.1007/978-981-15-9897-5_12
Pradhan, B. (2021). Internet of Things and Robotics in Transforming Current-Day Healthcare Services. Journal of Healthcare Engineering, 2021(Query date: 2025-02-03 20:09:44). https://doi.org/10.1155/2021/9999504
Saad, E. (2021). Determining the Efficiency of Drugs under Special Conditions from Users’ Reviews on Healthcare Web Forums. IEEE Access, 9(Query date: 2025-02-03 20:10:29), 85721–85737. https://doi.org/10.1109/ACCESS.2021.3088838
Sarker, S. (2021). Robotics and artificial intelligence in healthcare during COVID-19 pandemic: A systematic review. Robotics and Autonomous Systems, 146(Query date: 2025-02-03 20:09:44). https://doi.org/10.1016/j.robot.2021.103902
Silvera-Tawil, D. (2024). Robotics in Healthcare: A Survey. SN Computer Science, 5(1). https://doi.org/10.1007/s42979-023-02551-0
Sindhwani, R. (2022). Modelling enablers of efficiency and sustainability of healthcare: A m-TISM approach. Benchmarking, 29(3), 767–792. https://doi.org/10.1108/BIJ-03-2021-0132
Siripurapu, S. (2023). Technological Advancements and Elucidation Gadgets for Healthcare Applications: An Exhaustive Methodological Review-Part-II (Robotics, Drones, 3D-Printing, Internet of Things, Virtual/Augmented and Mixed Reality). Electronics (Switzerland), 12(3). https://doi.org/10.3390/electronics12030548
Vallès-Peris, N. (2023). Caring in the in-between: A proposal to introduce responsible AI and robotics to healthcare. AI and Society, 38(4), 1685–1695. https://doi.org/10.1007/s00146-021-01330-w
Yan, X. (2022). Cross-city patient mobility and healthcare equity and efficiency: Evidence from Hefei, China. Travel Behaviour and Society, 28(Query date: 2025-02-03 20:10:29), 1–12. https://doi.org/10.1016/j.tbs.2022.02.001
Zhang, S. (2021). An equity and efficiency integrated grid-to-level 2SFCA approach: Spatial accessibility of multilevel healthcare. International Journal for Equity in Health, 20(1). https://doi.org/10.1186/s12939-021-01553-9
Zhou, J. (2023). Analyzing the efficiency of Chinese primary healthcare institutions using the Malmquist-DEA approach: Evidence from urban and rural areas. Frontiers in Public Health, 11(Query date: 2025-02-03 20:10:29). https://doi.org/10.3389/fpubh.2023.1073552
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Copyright (c) 2024 Mehdi Karimi, Sahar Yazdani
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