I MAMMOGRAPHY ACCEPTANCE WITH INSIGHTS FROM PATIENT AND HEALTHCARE PROFESSIONAL USING UTAUT
DOI:
10.54443/ijebas.v5i2.2601Published:
2025-04-21Downloads
Abstract
Breast cancer currently positioned as the leading cause of cancer-related mortality among women in Indonesia, highlighting the urgent need for early detection and accurate diagnosis. AI-assisted mammography is one of a potential solution to detect the cancer early and reduce radiologist workload. However, the adoption of AI mammography remains limited, due to skepticism among healthcare professionals, and a lack of patient awareness. This study aims to evaluate the acceptance of AI mammography using the Unified Theory of Acceptance and Use of Technology (UTAUT) framework, by incorporating insights from both healthcare professionals and female patients aged 30 and above. A mixed-method approach was employed, quantitative surveys from 480 women and interviews 8 healthcare professionals. The results indicate that Performance Expectancy, Effort Expectancy, Social Influence, and Facilitating Conditions significantly impact Behavioral Intention to adopt AI mammography. However, the relationship between Behavioral Intention and actual Use Behavior did not meet reliability and validity thresholds, suggesting that adoption is still in its early phase. Key barriers identified include limited AI exposure, infrastructure disparities, regulatory constraints, and concerns over job displacement among radiologists.
Keywords:
Mammography, Breast Cancer, UTAUT, Technology Adoption, Crossing The Chasm, Innovation ManagementReferences
AHRQ. (2023). 2023 National Healthcare Quality and Disparities Report. https://www.ahrq.gov/research/findings/nhqrdr/nhqdr23/index.html
Bawaneh, K., Ghazi Nezami, F., Rasheduzzaman, Md., & Deken, B. (2019). Energy Consumption Analysis and Characterization of Healthcare Facilities in the United States. Energies, 12(19), 3775. https://doi.org/10.3390/en12193775
Cobelli, N., Cassia, F., & Donvito, R. (2023). Pharmacists’ attitudes and intention to adopt telemedicine: Integrating the market-orientation paradigm and the UTAUT. Technological Forecasting and Social Change, 196, 122871. https://doi.org/10.1016/j.techfore.2023.122871
Coleman, M. P., Quaresma, M., Berrino, F., Lutz, J.-M., De Angelis, R., Capocaccia, R., Baili, P., Rachet, B., Gatta, G., Hakulinen, T., Micheli, A., Sant, M., Weir, H. K., Elwood, J. M., Tsukuma, H., Koifman, S., E Silva, G. A., Francisci, S., Santaquilani, M., … Young, J. L. (2008). Cancer survival in five continents: A worldwide population-based study (CONCORD). The Lancet Oncology, 9(8), 730–756. https://doi.org/10.1016/S1470- 2045(08)70179-7
ERIA. (2023). ACCELERATING DIGITAL TRANSFORMATION IN INDONESIA: Technology, Market, and Policy. https://www.eria.org/uploads/Accelerating-Digital-Transformation Indonesia-rev3.pdf
Fass, L. (2008). Imaging and cancer: A review. Molecular Oncology, 2(2), 115–152. https://doi.org/10.1016/j.molonc.2008.04.001
Fletcher, S. W., & Elmore, J. G. (2003). Mammographic Screening for Breast Cancer. New England Journal of Medicine, 348(17), 1672–1680. https://doi.org/10.1056/NEJMcp021804
Hosny, A., Parmar, C., Quackenbush, J., Schwartz, L. H., & Aerts, H. J. W. L. (2018). Artificial intelligence in radiology. Nature Reviews. Cancer, 18(8), 500–510. https://doi.org/10.1038/s41568-018-0016-5
Hua, D., Petrina, N., Young, N., Cho, J.-G., & Poon, S. K. (2024). Understanding the factors influencing acceptability of AI in medical imaging domains among healthcare professionals: A scoping review. Artificial Intelligence in Medicine, 147, 102698. https://doi.org/10.1016/j.artmed.2023.102698
Huang, W., Ong, W. C., Wong, M. K. F., Ng, E. Y. K., Koh, T., Chandramouli, C., Ng, C. T., Hummel, Y., Huang, F., Lam, C. S. P., & Tromp, J. (2024). Applying the UTAUT2 framework to patients’ attitudes toward healthcare task shifting with artificial intelligence. BMC Health Services Research, 24(1), 455. https://doi.org/10.1186/s12913-024-10861-z
IARC. (2022). Age-Standardized Rate (World) per 100 000, Incidence and Mortality, Both sexes, in 2022. https://gco.iarc.fr/today/en/dataviz/bars?mode=cancer&group_populations= 1&types=0_1&sort_by=value1&populations=360
Icanervilia, A., Choridah, L., Van Asselt, A., Vervoort, J., Postma, M., Rengganis, A., & Kardinah, K. (2023). Early Detection of Breast Cancer in Indonesia: Barriers Identified in a Qualitative Study. Asian Pacific Journal of Cancer Prevention, 24(8), 2749–2755. https://doi.org/10.31557/APJCP.2023.24.8.2749
IsDB. (2024). Strengthening Indonesia’s Healthcare Referral Network (Sihren)—GPN. https://www.isdb.org/projectprocurement/tenders/2024/gpn/strengthening-indonesias-healthcarereferral-network-sihren-gpn
Kataoka, M., & Uematsu, T. (2024). AI Systems for Mammography with Digital Breast Tomosynthesis: Expectations and Challenges. Radiology. Imaging Cancer, 6(4), e240171. https://doi.org/10.1148/rycan.240171
Kementrian Kesehatan Indonesia. (2024). Digital Health Transformation Strategy 2024. https://oss2.dto.kemkes.go.id/artikel-web-dto/ENGBlueprint-for-Digital-Health-Transformation-StrategyIndonesia%202024.pdf
Kementrian Kesehatan Indonesia. (2024). Rencana Kanker Nasional 2024- 2034. https://p2ptm.kemkes.go.id/uploads/cEdQdm1WVXZuRXhad3FtVXduOW 1WUT09/2024/10/NCCP_ISI_240927_Rencana%20Kanker%20Nasional% 202024-2034.pdf
Kementrian Kesehatan Indonesia. (2024). Request for Bids Goods-Specific Procurement Notice Procurement of Mammography. https://ihss.kemkes.go.id/media/events/materials/2024-08-08/Paparan_Prebid_Mammography_050824.pdf
Moore, G. A. (2006). A new adoption model for quality of experience assessed by radiologists using AI medical imaging technology. Journal of Open Innovation: Technology, Market, and Complexity, 10(3), 100369. https://doi.org/10.1016/j.joitmc.2024.100369
Parsa, P., Kandiah, M., Abdul Rahman, H., & Zulkefli, N. M. (2006). Barriers for breast cancer screening among Asian women: A mini literature review. Asian Pacific Journal of Cancer Prevention: APJCP, 7(4), 509–514.
Perwira, T. (2024). Evaluasi Penerimaan Wearable Electrocardiograph (Dubdub) Pada Masyarakat Indonesia Dengan Menggunakan Model Penerimaan Teknologi [Institut Teknologi Bandung]. https://digilib.itb.ac.id/gdl/view/79788/dubdub?rows=1&per_page=2
Rad-aid. (2023). Rad-aid Annual Report 2023. https://rad-aid.org/wpcontent/uploads/2023_RAD-AID_Annual-Report.pdf
Rouidi, M., Elouadi, A. E., Hamdoune, A., Choujtani, K., & Chati, A. (2022). TAM-UTAUT and the acceptance of remote healthcare technologies by healthcare professionals: A systematic review. Informatics in Medicine Unlocked, 32, 101008. https://doi.org/10.1016/j.imu.2022.101008
Safarpour Lima, Z., Ebadi, M. R., Amjad, G., & Younesi, L. (2019). Application of Imaging Technologies in Breast Cancer Detection: A Review Article. Open Access Macedonian Journal of Medical Sciences, 7(5), 838– 848. https://doi.org/10.3889/oamjms.2019.171
Uwimana, A., Gnecco, G., & Riccaboni, M. (2025). Artificial intelligence for breast cancer detection and its health technology assessment: A scoping review. Computers in Biology and Medicine, 184, 109391. https://doi.org/10.1016/j.compbiomed.2024.109391
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). Unified Theory of Acceptance and Use of Technology. https://doi.org/10.1037/t57185-000
WHO. (2014). WHO position paper on mammgrapy screening. https://iris.who.int/bitstream/handle/10665/137339/9789241507936_eng.pd f?sequence=1
WHO. (2022). GBCI: operational approach based on 3 pillars of action for national cancer control programmes. https://www.who.int/initiatives/globalbreast-cancer-initiative/operational-approach-based-on-3-pillars
WHO. (2023). Global Breast Cancer Initiative. https://www.who.int/initiatives/global-breast-cancer-initiative/
World Bank Group. (2024). Indonesia Health Systems Strengthening Project summary. https://projects.worldbank.org/en/projectsoperations/procurement-detail/OP00260151
Yin, Z., Yan, J., Fang, S., Wang, D., & Han, D. (2022). User acceptance of wearable intelligent medical devices through a modified unified theory of acceptance and use of technology. Annals of Translational Medicine, 10(11), 629–629. https://doi.org/10.21037/atm-21-5510
Yusuf, M. V. (2022). Identifikasi Faktor Intensi Penggunaan Dari Aplikasi ‘Running Tracker’ Bermerek Di Indonesia Menggunakan Model Utaut2 [Institut Teknologi Bandung].
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