Clinical Experience of Patients in Participating in Medical Research: A Phenomenological Exploration of Emotional Dimensions in the Process, Benefits, and Challenges of Clinical-Based Medical Research
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Abstract
In recent years, the integration of digital health technologies in healthcare systems has emerged as a critical area of research, particularly in understanding the experiences of both healthcare practitioners and patients. Despite significant advancements in telemedicine and digital health platforms, the subjective experiences and underlying meanings of these technologies remain underexplored. This study addresses the gap by investigating the lived experiences of healthcare practitioners and patients in using telemedicine platforms for healthcare communication. Using a phenomenological approach, we explore how these individuals interpret and engage with digital health tools in their daily practices. Data were collected through in-depth interviews with 25 participants, including healthcare professionals and patients, and analyzed thematically to identify core themes. The findings emphasize key themes such as emotional challenges faced by patients during remote consultations, concerns about the lack of personal connection, and the anxiety stemming from technological barriers. Simultaneously, positive themes such as improved accessibility and convenience were also identified. These insights offer valuable implications for improving user-centered design in telemedicine systems and inform future research on digital health adoption in various healthcare contexts.
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References
Bacigalupe, A., Cabezas, A., Bueno, M. B., & Martín, U. (2020). Gender as a determinant of mental health and its medicalization. SESPAS Report 2020. Gaceta Sanitaria, 34, 61–67. https://doi.org/10.1016/j.gaceta.2020.06.013
Monte-Rubio, C. G., Segura, B., Strafella, A. P., van Eimeren, T., Ibarretxe-Bilbao, N., Diez-Cirarda, M., Eggers, C., Lucas-Jiménez, O., Ojeda, N., Peña, J., Ruppert, M. C., Sala-Llonch, R., Theis, H., Uribe, C., & Junque, C. (2022). Parameters from site classification to harmonize MRI clinical studies: Application to a multi-site Parkinson’s disease dataset. Human Brain Mapping, 43(10), 3130–3142. https://doi.org/10.1002/hbm.25838
Ciaccio, C., Pantaleoni, C., Taroni, F., Bella, D. D., Magri, S., Lamantea, E., Ghezzi, D., Valente, E. M., Nigro, V., & D’Arrigo, S. (2021). A clinical-based diagnostic approach to cerebellar atrophy in children. Applied Sciences, 11(5), 1–12. https://doi.org/10.3390/app11052333
Concannon, J., Máirtín, E. Ó., FitzGibbon, B., Hynes, N., Sultan, S., & McGarry, J. P. (2024). On the importance of including cohesive zone models in modelling mixed-mode aneurysm rupture. Cardiovascular Engineering and Technology, 15(5), 633–646. https://doi.org/10.1007/s13239-024-00740-3
de Moura, J., Samagaio, G., Novo, J., Almuina, P., Fernández, M. I., & Ortega, M. (2020). Joint diabetic macular edema segmentation and characterization in OCT images. Journal of Digital Imaging, 33(5), 1335–1351. https://doi.org/10.1007/s10278-020-00360-y
Haeyen, S., & Dimaggio, G. (2024). Arts and psychomotor therapies in personality disorder treatment: An appropriate therapeutic entrance to personal development: A commentary. Journal of Clinical Psychology, 80(11), 2303–2314. https://doi.org/10.1002/jclp.23730
Li, H.-J., Liu, L.-Z., Huang, Y., Jin, Y.-B., Chen, X.-P., Luo, W., Su, J.-C., Chen, K., Zhang, J., & Zhang, G.-Y. (2022). Establishment and validation of a novel MRI radiomics feature-based prognostic model to predict distant metastasis in endemic nasopharyngeal carcinoma. Frontiers in Oncology, 12. https://doi.org/10.3389/fonc.2022.794975
Liang, L., Zhi, X., Sun, Y., Li, H., Wang, J., Xu, J., & Guo, J. (2021). A nomogram based on a multiparametric ultrasound radiomics model for discrimination between malignant and benign prostate lesions. Frontiers in Oncology, 11. https://doi.org/10.3389/fonc.2021.610785
Mejia, A., Nyhus, K., Burley, T., Myhre, A., Montes, M., Osiecki, K., & Randolph, A. C. (2024). “Ripping off the band-aid”: Uncovering future health care professionals’ “fractured knowledge” about sexual and reproductive health. Frontiers in Reproductive Health, 6. https://doi.org/10.3389/frph.2024.1242885
Reilly, M., Dandapani, S. V., Kumar, K. A., Constine, L., Fogh, S. E., Roberts, K. B., Small, W., & Schechter, N. R. (2023). ACR–ARS practice parameter for the performance of total body irradiation. American Journal of Clinical Oncology: Cancer Clinical Trials, 46(5), 185–192. https://doi.org/10.1097/COC.0000000000000997
Stanciu, C. N., Healey, C. J., Emeny, R. T., Aschbrenner, K., Fetter, J. C., & Friedman, M. J. (2023). The New Hampshire Hospital Screening and Referral Algorithm (NHHSRA) for substance use in people with serious mental illness. Primary Care Companion for CNS Disorders, 25(3). https://doi.org/10.4088/PCC.22m03410
Taylor, J. L., Holland, D. J., Spathis, J. G., Beetham, K. S., Wisløff, U., Keating, S. E., & Coombes, J. S. (2019). Guidelines for the delivery and monitoring of high-intensity interval training in clinical populations. Progress in Cardiovascular Diseases, 62(2), 140–146. https://doi.org/10.1016/j.pcad.2019.01.004
Tradigo, G., Vizza, P., Gabriele, G., Mazzitelli, M., Torti, C., Prosperi, M., Guzzi, P. H., & Veltri, P. (2020). On the use of clinical-based infection data for pandemic case studies. In T. Park, Y.-R. Cho, X. T. Hu, I. Yoo, H. G. Woo, J. Wang, J. Facelli, S. Nam, & M. Kang (Eds.), Proceedings of the IEEE International Conference on Bioinformatics and Biomedicine (pp. 2313–2317). IEEE. https://doi.org/10.1109/BIBM49941.2020.9313469
Yuan, Y., Huang, L., Yu, L., Yan, X., Chen, S., Bi, C., He, J., Zhao, Y., Yang, L., Ning, L., Jin, H., Yang, R., & Li, Y. (2024). Clinical metabolomics characteristics of diabetic kidney disease: A meta-analysis of 1875 cases with diabetic kidney disease and 4503 controls. Diabetes/Metabolism Research and Reviews, 40(3). https://doi.org/10.1002/dmrr.3789
Zheng, J., Xia, Y., Xu, A., Weng, X., Wang, X., Jiang, H., Li, Q., & Li, F. (2022). Combined model based on enhanced CT texture features in liver metastasis prediction of high-risk gastrointestinal stromal tumors. Abdominal Radiology, 47(1), 85–93. https://doi.org/10.1007/s00261-021-03321-3