Exploring Healthcare Professionals' and Patients' Subjective Experiences with Data Analytics in Medical Decision-Making in Hospitals
Main Article Content
Abstract
Data analytics in healthcare has emerged as a crucial tool for improving diagnostic accuracy and treatment efficiency. However, while the technical advantages are well-documented, there is limited understanding of the subjective experiences of healthcare professionals and patients who engage with this technology. This research seeks to address this gap by exploring the perceptions and experiences of medical staff and patients regarding the use of data analytics in healthcare decision-making. Employing a phenomenological approach, we examine the lived experiences of these individuals to gain a deeper understanding of their feelings of empowerment, trust, and concerns about the technology. Through in-depth interviews with 8 healthcare professionals and 7 patients, we identified key themes of uncertainty, empowerment, and privacy concerns that influence the acceptance and integration of technology in medical practice. Our findings show that while technology improves clinical outcomes, its adoption is shaped by personal and social factors, such as trust in the system and fears of data misuse. These insights contribute to a more comprehensive understanding of the challenges and opportunities that data analytics presents in healthcare, offering valuable implications for training, policy development, and future research. The limitations of this study include a small sample size and the potential for bias in participant selection. Future research could expand on this study by including a larger and more diverse sample to further explore these findings.
Article Details
Section
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
Amoon, M., Altameem, T., & Altameem, A. (2020). Internet of things sensor assisted security and quality analysis for health care data sets using artificial intelligent based heuristic health management system. Measurement: Journal of the International Measurement Confederation, 161. Scopus. https://doi.org/10.1016/j.measurement.2020.107861
Baldassano, S. N., Roberson, S. W., Balu, R., Scheid, B., Bernabei, J. M., Pathmanathan, J., Oommen, B., Leri, D., Echauz, J., Gelfand, M., Bhalla, P. K., Hill, C. E., Christini, A., Wagenaar, J. B., & Litt, B. (2020). IRIS: A modular platform for continuous monitoring and caretaker notification in the intensive care unit. IEEE Journal of Biomedical and Health Informatics, 24(8), 2389–2397. Scopus. https://doi.org/10.1109/JBHI.2020.2965858
Blonigen, D., Hyde, J., McInnes, D. K., Yoon, J., Byrne, T., Ngo, T., & Smelson, D. (2023). Integrating data analytics, peer support, and whole health coaching to improve the health outcomes of homeless veterans: Study protocol for an effectiveness-implementation trial. Contemporary Clinical Trials, 125. Scopus. https://doi.org/10.1016/j.cct.2022.107065
Fahlevi, H., Irsyadillah, I., Indriani, M., & Oktari, R. S. (2022). DRG-based payment system and management accounting changes in an Indonesian public hospital: Exploring potential roles of big data analytics. Journal of Accounting and Organizational Change, 18(2), 325–345. Scopus. https://doi.org/10.1108/JAOC-10-2020-0179
Fleming, M. D., Safaeinili, N., Knox, M., & Brewster, A. L. (2024). Organizational and community resilience for COVID-19 and beyond: Leveraging a system for health and social services integration. Health Services Research, 59(S1). Scopus. https://doi.org/10.1111/1475-6773.14250
Gous, N., Nyaruhirira, A. U., Cunningham, B., & Macek, C. (2020). Driving the usage of tuberculosis diagnostic data through capacity building in low- And middle-income countries. African Journal of Laboratory Medicine, 9(2). Scopus. https://doi.org/10.4102/AJLM.V9I2.1092
Gramaje, A., Thabtah, F., Abdelhamid, N., & Ray, S. K. (2021). Patient Discharge Classification Using Machine Learning Techniques. Annals of Data Science, 8(4), 755–767. Scopus. https://doi.org/10.1007/s40745-019-00223-6
Harb, H., Mansour, A., Nasser, A., Cruz, E. M., & De La Torre Diez, I. (2021). A Sensor-Based Data Analytics for Patient Monitoring in Connected Healthcare Applications. IEEE Sensors Journal, 21(2), 974–984. Scopus. https://doi.org/10.1109/JSEN.2020.2977352
Harb, H., Mroue, H., Mansour, A., Nasser, A., & Cruz, E. M. (2020). A hadoop-based platform for patient classification and disease diagnosis in healthcare applications. Sensors (Switzerland), 20(7). Scopus. https://doi.org/10.3390/s20071931
Ho, A. F. W., To, B. Z. Y. S., Koh, J. M., & Cheong, K. H. (2019). Forecasting Hospital Emergency Department Patient Volume Using Internet Search Data. IEEE Access, 7, 93387–93395. Scopus. https://doi.org/10.1109/ACCESS.2019.2928122
Maddeh, M., Hajjej, F., Alazzam, M. B., Otaibi, S. A., Turki, N. A., & Ayouni, S. (2023). Spatio-Temporal Cluster Mapping System in Smart Beds for Patient Monitoring. Sensors, 23(10). Scopus. https://doi.org/10.3390/s23104614
Mahajan, A., Madhani, P., Chitikeshi, S., Selvaganesan, P., Russell, A., & Mahajan, P. (2019). Advanced Data Analytics for Improved Decision-Making at a Veterans Affairs Medical Center. Journal of Healthcare Management, 64(1), 54–62. Scopus. https://doi.org/10.1097/JHM-D-17-00164
Metz, M., Smith, R., Mitchell, R., Duong, Y. T., Brown, K., Kinchen, S., Lee, K., Ogollah, F. M., Dzinamarira, T., Maliwa, V., Moore, C., Patel, H., Chung, H., Mtengo, H., & Saito, S. (2021). Data Architecture to Support Real-Time Data Analytics for the Population-Based HIV Impact Assessments. Journal of Acquired Immune Deficiency Syndromes, 87, S28–S35. Scopus. https://doi.org/10.1097/QAI.0000000000002703
Minopoulos, G. M., Memos, V. A., Stergiou, C. L., Stergiou, K. D., Plageras, A. P., Koidou, M. P., & Psannis, K. E. (2022). Exploitation of Emerging Technologies and Advanced Networks for a Smart Healthcare System. Applied Sciences (Switzerland), 12(12). Scopus. https://doi.org/10.3390/app12125859
Mulrooney, M., Smith, M., Lewis, K., Vuernick, E., Anderson, D., Channamsetty, V., & Giannotti, T. (2022). Practical implementation insights from 2 population health pharmacist project approaches to improve blood pressure control. Journal of the American Pharmacists Association, 62(1), 270–280. Scopus. https://doi.org/10.1016/j.japh.2021.07.012
Role, J., Chao, H., Rosario, C., Ho, P., & Hodgkins, M. (2021). Inpatient Staffing Dashboard: A Nursing-Information Technology Collaborative Project. CIN - Computers Informatics Nursing, 39(11), 772–779. Scopus. https://doi.org/10.1097/CIN.0000000000000778
Simsekler, M. C. E., Rodrigues, C., Qazi, A., Ellahham, S., & Ozonoff, A. (2021). A comparative study of patient and staff safety evaluation using tree-based machine learning algorithms. Reliability Engineering and System Safety, 208. Scopus. https://doi.org/10.1016/j.ress.2020.107416