Postoperative Pain Assessment: Emerging Technologies and Clinical Implications
Review Article
DOI:
https://doi.org/10.5281/zenodo.20845331Keywords:
Postoperative Pain, Pain, Nursing CareAbstract
Introduction: Postoperative pain is a common clinical problem that significantly affects recovery outcomes and patient safety. Despite the widespread use of conventional pain assessment tools, limitations such as subjectivity and insufficient applicability in certain patient groups necessitate more objective and comprehensive approaches.
Objective: This review aimed to examine current approaches in postoperative pain assessment, evaluate emerging technologies, and highlight their implications for clinical practice and nursing care.
Methods: A narrative literature review was conducted using PubMed and Google Scholar databases. Studies focusing on postoperative pain assessment methods, including both conventional tools and emerging technologies, were reviewed and synthesized.
Results: Conventional pain assessment tools, such as the Visual Analog Scale and Numeric Rating Scale, provide rapid and practical evaluations but are limited by their reliance on patient self-report. Emerging technologies—including mobile health applications, wearable devices, nociception indices, pupillometry, and artificial intelligence-based systems—offer more objective, continuous, and multidimensional assessment opportunities. These technologies enable real-time monitoring, improved accuracy, and enhanced individualized pain management. However, challenges related to validity, cost-effectiveness, data security, and standardization remain significant barriers to widespread clinical implementation.
Conclusion: Emerging technologies have the potential to transform postoperative pain assessment by improving objectivity and supporting personalized care. For effective integration into clinical practice, particularly in nursing care, further high-quality research, standardization, and professional training are required.
References
Yılmaz Yelvar GD, Çırak Y, Dalkılınç M, et al. Physiotherapy integrated virtual walking for chronic pain. Eur Spine J. 2017;26(2):538-545. doi:10.1007/s00586-016-4892-7
Wong A, Reddy SK. Pain assessment and management. In: Yennurajalingam S, Bruera E, eds. Oxford American Handbook of Hospice and Palliative Medicine and Supportive Care. 2nd ed. Oxford University Press; 2016:22-67.
Sgrò A, Al-Busaidi IS, Wells CI, et al. Global surgery: A 30-year bibliometric analysis (1987-2017). World J Surg. 2019;43:2682-2694. doi:10.1007/s00268-019-05112-w
Reisli R, Akkaya ÖT, Arıcan Ş, et al. Acute postoperative pain: Pharmacologic treatment—A clinical practice guideline of the Turkish Society of Algology. Ağrı. 2021;33(suppl 1):1-51. doi:10.14744/agri.2021.60243
Aydın NO. Current perspectives on pain and pain mechanisms. Adnan Menderes Univ Med J. 2002;3(2):37-48.
Sloman R, Rosen G, Rom M, Shir Y. Nurses’ assessment of pain in surgical patients. J Adv Nurs. 2005;52(2):125-132. doi:10.1111/j.1365-2648.2005.03573.x
Erden S, Tura İ. New generation pain management: Innovative solutions in orthopedic surgery. TOTBID J. 2025;24:158-167. doi:10.5578/totbid.dergisi.2025.24
Balding L. Pain management. In: Watson M, Campbell R, Vallath N, et al, eds. Oxford Handbook of Palliative Care. 3rd ed. Oxford University Press; 2019:237-316.
Eti-Aslan F. Health Assessment and Clinical Decision Making. Akademisyen Medical Bookstore; 2017.
Fink RM, Gates RA, Jeffers KD. Pain assessment. In: Ferrell BR, Paice JA, eds. Oxford Textbook of Palliative Nursing. 5th ed. Oxford University Press; 2019:98-115.
Cheng J, Rosenquist RW, eds. Fundamentals of Pain Medicine. Springer; 2018.
Melzack R. The short-form McGill Pain Questionnaire. Pain. 1987;30(2):191-197. doi:10.1016/0304-3959(87)91074-8
Tura İ, Erden S. Evidence-based recommendations in postoperative pain control. Dent Med J-R. 2022;4(1):34-47.
Premkumar A, et al. Mobile phone text messaging for postoperative pain monitoring. HSS J. 2019;15(1):37-41. doi:10.1007/s11420-018-9635-3
Shahiri TS, Richebé P, Richard-Lalonde M, Gélinas C. Validity of ANI and NOL for postoperative pain detection. J Clin Monit Comput. 2022;36(3):623-635. doi:10.1007/s10877-021-00772-3
Fuica R, Krochek C, Weissbrod R, et al. Reduced postoperative pain in patients receiving nociception monitor–guided analgesia during elective major abdominal surgery: A randomized controlled trial. J Clin Monit Comput. 2023;37(2):481-491. doi:10.1007/s10877-022-00906-1
Prvu Bettger J, et al. Virtual exercise rehabilitation for postoperative patients. J Bone Joint Surg Am. 2020;102(2):101-109. doi:10.2106/JBJS.19.00695
Çöçelli LP, Bacaksız BD, Ovayolu N. The role of nurses in pain management. Gaziantep Med J. 2008;14(2):53-58.
McGaffin L, Mitchell G, Anderson T, Gillis A, Craig S. Digital approaches to pain assessment across older adults: A scoping review. Healthcare. 2026;14(2):149. doi:10.3390/healthcare14020149
Chou R, Gordon DB, de Leon-Casasola OA, et al. Management of postoperative pain: A clinical practice guideline. J Pain. 2016;17(2):131-157. doi:10.1016/j.jpain.2015.12.008
Turan Aİ, Çoban SD. Digital life technologies and health. Nevsehir HBV Univ J Health Sci. 2023;13(1):531-551. doi:10.30783/nevsosbilen.1229121
Chen J, Abbod M, Shieh JS. Pain and stress detection using wearable sensors and devices: A review. Sensors. 2021;21(4):1030. doi:10.3390/s21041030
Kheirkhahan M, Nair S, Davoudi A, et al. A smartwatch-based framework for monitoring postoperative pain. J Biomed Inform. 2019;89:29-40. doi:10.1016/j.jbi.2018.11.003
Avila FR, McLeod CJ, Huayllani MT, et al. Wearable electronic devices for chronic pain intensity assessment: A systematic review. Pain Pract. 2021;21(8):955-965. doi:10.1111/papr.13047
Mardini MT, et al. The temporal relationship between ecological pain and function in daily life. JMIR Mhealth Uhealth. 2021;9(1):e19609. doi:10.2196/19609
Kim MK, Choi GJ, Oh KS, Lee SP, Kang H. Pain assessment using the Analgesia Nociception Index. J Pers Med. 2023;13(10):1461. doi:10.3390/jpm13101461
Öztür Ö, Feyzioğlu Ö. Virtual reality technologies and chronic pain. J Tradit Med Complement Ther. 2020;3(2):211-216. doi:10.5336/jtracom.2019-72224
Werner P, Al-Hamadi A, Limbrecht-Ecklundt K, Walter S, Gruss S, Traue HC. Automatic pain assessment using facial expression analysis. IEEE Trans Affect Comput. 2017;8(3):286-297. doi:10.1109/TAFFC.2016.2537327
Posada-Quintero HF, Kong Y, Chon KH. Objective pain stimulation intensity and pain sensation assessment using electrodermal activity and machine learning. Am J Physiol Regul Integr Comp Physiol. 2021;321(2):R186-R196. doi:10.1152/ajpregu.00094.2021
Syrowatka A, Song W, Amato MG, et al. Key use cases for artificial intelligence in pain management. Lancet Digit Health. 2022;4(2):e137-e148. doi:10.1016/S2589-7500(21)00229-6
Bifulco L, Anderson DR, Blankson ML, et al. Evaluation of a chronic pain screening program implemented in primary care. JAMA Netw Open. 2021;4(7):e2118495. doi:10.1001/jamanetworkopen.2021.18495
Akın E. Ethical responsibilities of nurses in clinical pain management. Turkiye Klinikleri J Med Ethics. 2020;28(1):128-133. doi:10.5336/mdethic.2019-66191
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2026 Acta Medica Ruha
This work is licensed under a Creative Commons Attribution 4.0 International License.
