Human Computer Interaction Applications in Healthcare: An Integrative Review

Authors

DOI:

https://doi.org/10.4108/eetpht.9.4186

Keywords:

Human Computer Interaction, HCI, Explainable-AIML, x-AIML, Electronic Health Record, EHR, Web Browser, Smartphone Technologies

Abstract

INTRODUCTION: Human computer interaction (HCI) interprets the design model and the uses of computer technology which focuses on the interface between the user and the computer. HCI is a very important factor in the design of software-oriented decision-making ideas in health-care organizations and also it assists in accurate detection of image, disease including safety of the patients.

OBJECTIVES: There are some pitfalls arises over some previous works on cloud based HCI applications. For that reason, to masafety, patient’s safety we wanted to work on explainable artificial intelligence (x-AI) and human intelligence in conjunction with HCI in various fields and algorithms to pro-vide transparency to the user. This may also use some web-based technologies and digital platforms with HCI for development of quality, safety and usability of the patients.

METHODS: The purpose of this study about the communication between the HCI design and healthcare system through client and apply that method to the information system of Healthcare department to analyse the functions, effects and outcomes.

RESULTS: The integration of explainable artificial intelligence (x-AI) and human intelligence with Human-Computer Interaction (HCI) demonstrated promising potential in enhancing patient safety and optimizing healthcare processes.    

CONCLUSION: By leveraging web-based technologies and digital platforms, this study established a framework for improving the quality, safety, and usability of healthcare services through effective communication between HCI design and healthcare systems.

Downloads

Download data is not yet available.

References

Agarwal, R. (2022). Predictive Analysis in Health Care System Using AI. Artificial Intelligence in Healthcare, 117-131. DOI: https://doi.org/10.1007/978-981-16-6265-2_8

AlZubi, A. A., Al-Maitah, M., & Alarifi, A. (2021). Cyber-attack detection in healthcare using cyber-physical system and machine learning techniques. Soft Computing, 25(18), 12319-12332. DOI: https://doi.org/10.1007/s00500-021-05926-8

Arruda leite. H. M, Carvalho. S.N.C, Sliva costa. T.B, Attux. R, Hornung. H.H, Arantes. D.S, “Analysis of user interaction with a Brain- Computer interface based on a stady state visually evoked potential: case study of a game” (2018). DOI: https://doi.org/10.1155/2018/4920132

B. Zhou, G. Yang, Z. Shi and S. Ma, "Natural Language Processing for Smart Healthcare," in IEEE Reviews in Biomedical Engineering, 2022, doi: 10.1109/RBME.2022.3210270. DOI: https://doi.org/10.1109/RBME.2022.3210270

Balacombe. L, Leo. D.D, “Review- Human Computer Interaction in digital mental health informatics”, (2022)-9, 14. DOI: https://doi.org/10.3390/informatics9010014

Ballesteros. J, Ayala. I, Rafael. J, Romerod. C, Amora. M, Fuentesa. L, “Evolving dy-namic self-adaptation policies of mHealth systems for long-term monitoring” (2020) DOI: https://doi.org/10.1016/j.jbi.2020.103494

Bologva. E.V, Prokusheva. D.I, Krikunov. A.V, Zvartau. N.E,Sergey V. Kovalchuk. S.V,” Human-Computer Interaction in Electronic Medical Records: from the Perspec-tives of Physicians and Data Scientists” (2016), Pp- 915-920. DOI: https://doi.org/10.1016/j.procs.2016.09.248

Bansal et al., International Journal of Advanced Research in Computer Science and Software Engineering 8(4) ISSN (E): 2277-128X, ISSN (P): 2277-6451, pp. 53-56. DOI: https://doi.org/10.23956/ijarcsse.v8i4.630

Cassano, C., Colantuono, A., De Simone, G., Giani, A., Liston, P. M., Marchigiani, E., & Parlangeli, O. (2019). Developments and problems in the man-machine relationship in computed tomography (CT). In Proceedings of the 20th Congress of the International Ergonomics Association (IEA 2018) Volume V: Human Simulation and Virtual Envi-ronments, Work with Computing Systems (WWCS), Process Control 20 (pp. 488-496). Springer International Publishing. DOI: https://doi.org/10.1007/978-3-319-96077-7_52

E. Bryndin, ``Development of artificial intelligence by ensembles of virtual agents with mobile interaction,'' Autom. Control Intell. Syst., vol. 8, no. 1, p. 1, 2020, doi: 10.11648/j.acis.20200801.11. DOI: https://doi.org/10.11648/j.acis.20200801.11

F. Topak, M.K. Pekericli, “towords using Human Computer Interaction research for ad-vancing intelligent build environments: a review”, in proc. 6th international projrct con-struction management. Conf. 2020, Pp- 835.

Gebru, B., Zeleke, L., Blankson, D., Nabil, M., Nateghi, S., Homaifar, A., & Tunstel, E. (2022). A review on human–machine trust evaluation: Human-centric and machine-centric perspectives. IEEE Transactions on Human-Machine Systems, 52(5), 952-962. DOI: https://doi.org/10.1109/THMS.2022.3144956

Gonzalez. O.L, “Black-box vs. White-box: understanding their advantages and weak-ness from a practical point of view”, (2019), Vol-4, Pp-1-19.

Guo, X., Hong, W., Zhao, Y., Zhu, T., Li, H., Zheng, G., & Xu, Y. (2022). Bioinspired sandwich-structured pressure sensors based on graphene oxide/hydroxyl functionalized carbon nanotubes/bovine serum albumin nanocomposites for wearable textile electronics. Composites Part A: Applied Science and Manufacturing, 163, 107240. DOI: https://doi.org/10.1016/j.compositesa.2022.107240

Gorsky, M., & Manton, J. (2022). The political economy of ‘strengthening health ser-vices’: The view from WHO AFRO, 1951-c. 1985. Social Science & Medicine, 115412. DOI: https://doi.org/10.1016/j.socscimed.2022.115412

Jin, Y., & Wei, W. (2022). Image edge enhancement detection method of human-computer interaction interface based on machine vision technology. Mobile Networks and Applications, 27(2), 775-783. DOI: https://doi.org/10.1007/s11036-021-01908-0

Kosch, T., Welsch, R., Chuang, L., & Schmidt, A. (2023). The Placebo Effect of Artifi-cial Intelligence in Human–Computer Interaction. ACM Transactions on Computer-Human Interaction, 29(6), 1-32. DOI: https://doi.org/10.1145/3529225

Kumar. R, Jayswal. V, Nishad. V, “Human Computer Interaction” (IJERT) (2021).

Li. X, Xu. Y, “Role of Human-Computer Interaction Healthcare System in the Teaching of Physiology and Medicine” Apr-2022.

Liberati, A., Altman, D.G., Tetzlaff, J., Mulrow, C., Gotzsche, P.C., Ioannidis, J.P.A., Clarke, M., Devereaux, P.J., Kleijnen, J., Moher, D., 2009. The PRISMA statement for reporting systematic reviews and meta-analyses of studies that evaluate healthcare inter-ventions: Explanation and elaboration. BMJ 339 (jul21 1), b2700–b2700. 10.1136/bmj.b2700. DOI: https://doi.org/10.1136/bmj.b2700

Liu, Q., Mkongwa, K. G., & Zhang, C. (2021). Performance issues in wireless body ar-ea networks for the healthcare application: A survey and future prospects. SN Applied Sciences, 3, 1-19. DOI: https://doi.org/10.1007/s42452-020-04058-2

M. G. Siavvas, K. C. Chatzidimitriou, and A. L. Symeonidis, ``QATCH_An adaptive framework for software product quality assessment,''Expert Syst. Appl., vol. 86, pp.350_366, Nov. 2017, doi: 10.1016/j.eswa.2017.05.060. DOI: https://doi.org/10.1016/j.eswa.2017.05.060

Mishra, S., Abbas, M., Jindal, K., Narayan, J., & Dwivedy, S. K. (2022). Artificial in-telligence-based technological advancements in clinical healthcare applications: A sys-tematic review. Revolutions in Product Design for Healthcare: Advances in Product Design and Design Methods for Healthcare, 207-227. DOI: https://doi.org/10.1007/978-981-16-9455-4_11

Nazar. M, Alam. M.M, Yafi. E, Su’ud. M.M, “a systematic review of Human computer Interaction and Explainable Artificial intelligence in healthcare with Artificial intelligence techniques”, (2021), Vol-9, Pp- 153316-153348. DOI: https://doi.org/10.1109/ACCESS.2021.3127881

P. Forbrig, ‘‘Continuous software engineering with special emphasis on continuous business-process modeling and human-centered design,’’ in Proc. 8th Int. Conf. Sub-ject-Oriented Bus. Process Manage. Apr. 2016, pp. 1–4, doi: 10.1145/2882879.2882895 DOI: https://doi.org/10.1145/2882879.2882895

Parui, S., Samanta, D., & Chakravorty, N. (2023, January). An Advanced Healthcare System Where Internet of Things meets Brain-Computer Interface using Event-Related Potential. In 24th International Conference on Distributed Computing and Network-ing (pp. 438-443). DOI: https://doi.org/10.1145/3571306.3571449

Pluye, P., Gagnon, M.-P., Griffiths, F., Johnson-Lafleur, J., 2009. A scoring system for appraising mixed methods research, and concomitantly appraising qualitative, quantita-tive and mixed methods primary studies in Mixed Studies Reviews. International Jour-nal of Nursing Studies 46 (4), 529–546, 10.1016/j. ijnurstu.2009.01.009. DOI: https://doi.org/10.1016/j.ijnurstu.2009.01.009

Rundo. L, Pirrone. R, Vitabile. S, Sala. E, Gambino. O, “recent advances of HCI in de-cision-making tasks for optimized clinical workflows and precision medicine” (2020-aug). DOI: https://doi.org/10.1016/j.jbi.2020.103479

S.Dino.M.J, M.Davidson.P, W.Dion.K, L.Szanton.S, L.Ong.I, “Nursing and human-computer interaction in healthcare robots for older people: An integrative review.” Mar-2022. DOI: https://doi.org/10.1016/j.ijnsa.2022.100072

Scibilia, A., Pedrocchi, N., & Fortuna, L. (2022). Human control model estimation in physical human–machine interaction: A survey. Sensors, 22(5), 1732. DOI: https://doi.org/10.3390/s22051732

Solari. F, Chessa. M, Chinellato. E, Bresciani. J. P, “advances in Human Computer In-teraction: Methods, Algorithms, Applications”, (2018). DOI: https://doi.org/10.1155/2018/4127475

T. Raduntz, T. Muhlhausen, N. Furstenau, E. Cheladze, and B. Meffert, Application of the Usability Metrics of the ISO 9126 Standard in the E-Commerce Domain: A Case Study, vol. 903. Cham, Switzerland: Springer, 2019.

Ter Stal, S., Kramer, L. L., Tabak, M., op den Akker, H., & Hermens, H. (2020). De-sign features of embodied conversational agents in eHealth: a literature re-view. International Journal of Human-Computer Studies, 138, 102409. DOI: https://doi.org/10.1016/j.ijhcs.2020.102409

Tyndall, James. (2010). AACODS Checklist. http://dspace.flinders.edu.au/dspace/. United Nations, Department of Economic and Social Affairs, 2017. World Population Prospects: The 2017 Revision. https://www.un.org/development/desa/ publica-tions/world-population-prospects-the-2017-revision.html.

V. Rajesh, P. Rajesh kumar and D. V. R. Koti Reddy, "SEMG based human machine interface for controlling wheel chair by using ANN," 2009 International Conference on Control, Automation, Communication and Energy Conservation, Perundurai, India, 2009, pp. 1-6.

Wang.J, Cheng. R, Liu.M, Liao. P.C, “research trends of HCI studies in construction hazard recognition: A bibliometric review”, (Sensors 2021, 21, 6172). DOI: https://doi.org/10.3390/s21186172

W. Xu, “A perspective from human computer interaction”, Tech. Rep. (2019).

X. Li, Y. Xu, “role of human computer interaction healthcare system in the teaching of physiology and medicine” (2022). DOI: https://doi.org/10.1155/2022/5849736

Xue, J., & Lai, K. W. C. (2023). Dynamic gripping force estimation and reconstruction in EMG-based human-machine interaction. Biomedical Signal Processing and Con-trol, 80, 104216. DOI: https://doi.org/10.1016/j.bspc.2022.104216

Y. Yun, D. Ma, and M. Yang, ``Human computer interaction based decision support system with applications in data mining,'' Future Gener.. Comp. Syst., vol. 114, pp. 285_289, Jan. 2021, doi:10.1016/j.future.2020.07.048. DOI: https://doi.org/10.1016/j.future.2020.07.048

Z. Zeng, P. J. Chen, and A. A. Lew, ``from high-touch to high-tech: COVID-19 drives robotics adoption,'' Tour. Geogr, vol. 22, no. 3, pp. 724_734, 2020, doi: 10.1080/14616688.2020.1762118. DOI: https://doi.org/10.1080/14616688.2020.1762118

Downloads

Published

20-10-2023

How to Cite

1.
Mishra R, Satpathy R, Pati B. Human Computer Interaction Applications in Healthcare: An Integrative Review. EAI Endorsed Trans Perv Health Tech [Internet]. 2023 Oct. 20 [cited 2024 Dec. 21];9. Available from: https://publications.eai.eu/index.php/phat/article/view/4186