Background & Aims
With the advent of the information age and technological development, the importance of digital health technologies has increased. Pain management nursing (PMN) remains a pivotal aspect of nursing practice, continually evolving with advancements in healthcare and patient-centered approaches. Despite of abundance of nursing research regarding pain management, there is lack of a comprehensive study aimed at elucidating topic theme and the current trends within nursing research focused on pain management, particularly in the advent of artificial intelligent for healthcare. The proposed research aims to identify and synthesize prevailing themes, trends, and innovative practices observed in contemporary pain management nursing research in 21st century using quantitative analysis with corpus linguistics analysis.
Methods
This study will review all available articles in databases over the past twenty years (from 2005 to 2024) using keyword searches for the PMN research from 5 databases. Initially, the study will employ a machine learning approach involving an unsupervised topic modeling design using Latent Dirichlet Allocation. The unit of analysis is words. Preprocessing and data analysis were carried out using the Orange Data Mining Toolbox. This programme is a Python GUI. Subsequently, corpus linguistic analyses will access keyless analysis and #Lancsbox 6.0 to explore the frequency of pain management use in PMN research. Additionally, OpenAI GPT 4.0 will be used to explore the presence of psychological constructs within PMN research results. Finally, the study also examined pain assessment/measurement, pain nursing intervention, and pain management. The change in the PMN research trend with the rise of the Artificial Intelligent in health care will be analysed using Tau with the time subcorpora.
Results
Expectation:
This proposed study will contribute significantly contribute to understanding current trends and future directions in pain management nursing research, particularly the recent impact of artificial intelligence on healthcare. The findings will encompass aspects such as main participant characteristics, pain distribution, intervention types, research methodologies, and the intervention’s impact within such research.
Conclusions
They will guide the development of clinical nursing practice by establishing empirical evidence as the cornerstone strategy and enhancing the professional quality of nursing management, prioritizing the health and well-being of patients.
References
Alsop, S., King, V., Giaimo, G., & Xu, X. (2021). Uses of corpus linguistics in education research: An adjustable lens. In M. Tight, & J. Huisman (Eds.), Theory and Method in Higher Education Research (Vol. 6, pp. 21-40). (Theory and Method in Higher Education Research; Vol. 6). Emerald. Advance online publication. https://doi.org/10.1108/S2056-375220200000006003
Babaie, S., Taghvimi, A., Hong, J.-H., Hamishehkar, H., An, S., & Kim, K. H. (2022). Recent advances in pain management based on nanoparticle technologies. Journal of Nanobiotechnology, 20(1), 290. https://doi.org/10.1186/s12951-022-01473-y
Berlin Helmi, P., Muhammad, M., & Hafizhan, A. (2020, 2020/10/11). Topic Modeling Using Latent Dirichlet Allocation (LDA) and Sentiment Analysis for Marketing Planning Tiket.com. Proceedings of the 2nd International Seminar on Science and Technology (ISSTEC 2019),
Breivik, H., Borchgrevink, P. C., Allen, S. M., Rosseland, L. A., Romundstad, L., Hals, E. K., Kvarstein, G., & Stubhaug, A. (2008). Assessment of pain. Br J Anaesth, 101(1), 17-24. https://doi.org/10.1093/bja/aen103
Cohen, S. P., Vase, L., Hooten, W.M. (2021). Chronic pain: an update on burden, best practices, and new advances. The Lancet, 397(10289), 2082-2097. https://doi.org/DOI:https://doi.org/10.1016/S0140-6736(21)00393-7
Conrad, S. (2002). Corpus linguistic approaches for discourse analysis. Annual Review of Applied Linguistics, 22, 75-95. https://doi.org/10.1017/S0267190502000041
Damar, H. T., Bilik, O., Ozdagoglu, G., Ozdagoglu, A., & Damar, M. (2018). Scientometric overview of nursing research on pain management. Rev Lat Am Enfermagem, 26, e3051. https://doi.org/10.1590/1518-8345.2581.3051
Davenport, T., & Kalakota, R. (2019). The potential for artificial intelligence in healthcare. Future Healthc J, 6(2), 94-98. https://doi.org/10.7861/futurehosp.6-2-94
Díaz-Rodríguez, M., Alcántara-Rubio, L., Aguilar-García, D., Pérez-Muñoz, C., Carretero-Bravo, J., & Puertas-Cristóbal, E. (2021). The Effect of Play on Pain and Anxiety in Children in the Field of Nursing: A Systematic Review. J Pediatr Nurs, 61, 15-22. https://doi.org/10.1016/j.pedn.2021.02.022
Draborg, E., Andreasen, J., Nørgaard, B., Juhl, C. B., Yost, J., Brunnhuber, K., Robinson, K. A., & Lund, H. (2022). Systematic reviews are rarely used to contextualise new results-a systematic review and meta-analysis of meta-research studies. Syst Rev, 11(1), 189. https://doi.org/10.1186/s13643-022-02062-8
Ferilli, S., Esposito, F., & Grieco, D. (2014). Automatic Learning of Linguistic Resources for Stopword Removal and Stemming from Text. Procedia Computer Science, 38, 116-123. https://doi.org/https://doi.org/10.1016/j.procs.2014.10.019
Gabrielatos, C. (2018). Keyness analysis: Nature, metrics and techniques. In (pp. 225-258).
Grant, C., & Osanloo, A. (2015). Understanding, selecting, and integrating a theoretical framework in dissertation research: Developing a ‘blueprint’ for your “house”. Administrative Issues Journal, 4. https://doi.org/10.5929/2014.4.2.9
Gurcan, F., Boztas, G. D., Dalveren, G. G. M., & Derawi, M. (2023). Digital Transformation Strategies, Practices, and Trends: A Large-Scale Retrospective Study Based on Machine Learning. Sustainability, 15(9), 7496. https://www.mdpi.com/2071-1050/15/9/7496
Hackl, V., Müller, A. E., Granitzer, M., & Sailer, M. (2023). Is GPT-4 a reliable rater? Evaluating consistency in GPT-4’s text ratings [Original Research]. Frontiers in Education, 8. https://doi.org/10.3389/feduc.2023.1272229
Helbach, J., Pieper, D., Mathes, T., Rombey, T., Zeeb, H., Allers, K., & Hoffmann, F. (2022). Restrictions and their reporting in systematic reviews of effectiveness: an observational study. BMC Med Res Methodol, 22(1), 230. https://doi.org/10.1186/s12874-022-01710-w
Jiang, F., Jiang, Y., Zhi, H., Dong, Y., Li, H., Ma, S., Wang, Y., Dong, Q., Shen, H., & Wang, Y. (2017). Artificial intelligence in healthcare: past, present and future. Stroke and Vascular Neurology, 2(4), 230-243. https://doi.org/10.1136/svn-2017-000101
Lau, J. H., Newman, D., & Baldwin, T. (2014). Machine Reading Tea Leaves: Automatically Evaluating Topic Coherence and Topic Model Quality. Conference of the European Chapter of the Association for Computational Linguistics,
Le-Wendling, L., Glick, W., & Tighe, P. (2017). Goals and Objectives to Optimize the Value of an Acute Pain Service in Perioperative Pain Management. Tech Orthop, 32(4), 200-208. https://doi.org/10.1097/bto.0000000000000245
Mankelow, J., Ryan, C., Taylor, P., Atkinson, G., & Martin, D. (2022). A Systematic Review and Meta-Analysis of the Effects of Biopsychosocial Pain Education upon Health Care Professional Pain Attitudes, Knowledge, Behavior and Patient Outcomes. The Journal of Pain, 23(1), 1-24. https://doi.org/https://doi.org/10.1016/j.jpain.2021.06.010
Mohamed Zaki, L. R., & Hairi, N. N. (2015). A Systematic Review of the Prevalence and Measurement of Chronic Pain in Asian Adults. Pain Manag Nurs, 16(3), 440-452. https://doi.org/10.1016/j.pmn.2014.08.012
Owens, J. K. (2021). Systematic reviews: Brief overview of methods, limitations, and resources. Nurse Author & Editor, 31(3-4), 69-72. https://doi.org/https://doi.org/10.1111/nae2.28
Paice, J. A., Battista, V., Drick, C. A., & Schreiner, E. (2018). Palliative Nursing Summit: Nurses Leading Change and Transforming Primary Palliative Care: Nursing’s Role in Providing Pain and Symptom Management. J Hosp Palliat Nurs, 20(1), 30-35. https://doi.org/10.1097/NJH.0000000000000405
Pailaha, A. D. (2023). The Impact and Issues of Artificial Intelligence in Nursing Science and Healthcare Settings. SAGE Open Nurs, 9, 23779608231196847. https://doi.org/10.1177/23779608231196847
Park, J., & Park, J. (2023). Identifying the Knowledge Structure and Trends of Nursing Informatics: A Text Network Analysis. CIN: Computers, Informatics, Nursing, 41(1), 8-17. https://doi.org/10.1097/cin.0000000000000919
Prent, J. M., van der Wurff, P., & Scholten-Peeters, G. G. M. (2023). Lifestyle factors and psychological factors are associated with central pain processing in service members with persistent low-back pain: A cross-sectional exploratory study. Medicine (Baltimore), 102(51), e36741. https://doi.org/10.1097/MD.0000000000036741
Rathje, S., Mirea, D., Sucholutsky, I., Marjieh, R., Robertson, C., & Van Bavel, J. J. (2023). GPT is an effective tool for multilingual psychological text analysis. https://doi.org/10.31234/osf.io/sekf5
Romanelli, R., Shah, S., Ikeda, L., Lynch, B., Craig, T., Cappelleri, J., Jukes, T., & Ishisaka, D. (2017). Patient Characteristics and Healthcare Utilization of a Chronic Pain Population Within an Integrated Healthcare System. The American journal of managed care, 23, e50-e56.
Russell, S. J., & Norvig, P. (2021). Artificial Intelligence: A Modern Approach (4 ed.). Hoboken: Pearson.
Scher, C., Meador, L., Van Cleave, J. H., & Reid, M. C. (2018). Moving Beyond Pain as the Fifth Vital Sign and Patient Satisfaction Scores to Improve Pain Care in the 21st Century. Pain Manag Nurs, 19(2), 125-129. https://doi.org/10.1016/j.pmn.2017.10.010
Stefanowitsch, A. (2018). Corpus Linguistics: A Guide to the Methodology.
Strathern, W., Schoenfeld, M., Ghawi, R., & Pfeffer, J. (2020). Against the Others! Detecting Moral Outrage in Social Media Networks. 2020 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM), 322-326.
Szeverenyi, C., Kekecs, Z., Johnson, A., Elkins, G., Csernatony, Z., & Varga, K. (2018). The Use of Adjunct Psychosocial Interventions Can Decrease Postoperative Pain and Improve the Quality of Clinical Care in Orthopedic Surgery: A Systematic Review and Meta-Analysis of Randomized Controlled Trials. J Pain, 19(11), 1231-1252. https://doi.org/10.1016/j.jpain.2018.05.006
Taylor, C., Marchi, A., Taylor, C., Taylor, & Francis. (2018). Corpus approaches to discourse : a critical review. In (Version 1st) Routledge. https://ebookcentral.proquest.com/lib/lancaster/detail.action?docID=5254458
Testing, U. (2019). Healthcare Chatbot Apps Are on the Rise but the Overall Customer Experience (cx) Falls Short According to a UserTesting Report. In: User Testing San Francisco, CA, USA.
Usmani, U., Haron, N., & Jaafar, J. (2021). A Natural Language Processing Approach to Mine Online Reviews Using Topic Modelling. In (pp. 82-98). https://doi.org/10.1007/978-3-030-76776-1_6
White, M., & Marsh, E. (2006). Content Analysis: A Flexible Methodology. Library Trends, 55. https://doi.org/10.1353/lib.2006.0053
Wrona, S. K., Quinlan-Colwell, A., Brown, L., & Jannuzzi, R. G. E. (2022). Procedural Pain Management: Clinical Practice Recommendations American Society for Pain Management Nursing. Pain Manag Nurs, 23(3), 254-258. https://doi.org/10.1016/j.pmn.2021.11.008
Wylde, V., Dennis, J., Beswick, A. D., Bruce, J., Eccleston, C., Howells, N., Peters, T. J., & Gooberman-Hill, R. (2017). Systematic review of management of chronic pain after surgery. Br J Surg, 104(10), 1293-1306. https://doi.org/10.1002/bjs.10601
Xu, Y., Yin, Y., & Yin, J. (2017). Tackling topic general words in topic modeling. Engineering Applications of Artificial Intelligence, 62, 124-133. https://doi.org/https://doi.org/10.1016/j.engappai.2017.04.009
Zhang, M., Zhu, L., Lin, S.-Y., Herr, K., Chi, C.-L., Demir, I., Dunn Lopez, K., & Chi, N.-C. (2022). Using artificial intelligence to improve pain assessment and pain management: a scoping review. Journal of the American Medical Informatics Association, 30(3), 570-587. https://doi.org/10.1093/jamia/ocac231
Zhang, S. K., Gu, M. L., Zhang, T., Xu, H., Mao, S. J., & Zhou, W. S. (2023). Effects of exercise therapy on disability, mobility, and quality of life in the elderly with chronic low back pain: a systematic review and meta-analysis of randomized controlled trials. J Orthop Surg Res, 18(1), 513. https://doi.org/10.1186/s13018-023-03988-y
Presenting Author
Nai-Huan Hsiung
Poster Authors
Naihuan Hsiung
PhD
Asia University
Lead Author
Topics
- Access to Care