Hiệu quả y tế số trong quản lý loãng xương: Nghiên cứu tổng quan luận điểm
Nội dung chính của bài viết
Tóm tắt
Loãng xương là một bệnh rối loạn chuyển hóa xương tiến triển thầm lặng. Tỷ lệ mắc và tử vong do gãy xương do loãng xương tăng lên kéo theo tăng gánh nặng cho người bệnh, gia đình, xã hội. Mục tiêu nghiên cứu này nhằm tìm hiểu ứng dụng của y tế số trong sàng lọc, theo dõi điều trị loãng xương. Nghiên cứu được tiến hành theo quy trình nghiên cứu tổng quan, tìm kiếm trên cơ sở dữ liệu Pubmed. Nội dung tìm kiếm tập trung vào ba phần chính: bệnh loãng xương, y tế số, và hiệu quả. Các bài báo gốc được lựa chọn xuất bản từ tháng 1/2014 đến tháng 9/2024 trên các tạp chí quốc tế có bình duyệt. Tổng cộng 18 bài báo được đưa vào phân tích. Y tế số được ứng dụng chủ yếu trong: sàng lọc loãng xương (38,9%) theo dõi điều trị loãng xương (62,1%). Phần lớn nghiên cứu cho thấy kết quả tích cực trong cải thiện sức khỏe cho người bệnh.
Chi tiết bài viết
Từ khóa
Y tế số, chuyển đổi số, hiệu quả, loãng xương, gãy xương
Tài liệu tham khảo
1. Conley R B, Adib G, Adler R A, et al. Secondary Fracture Prevention: Consensus Clinical Recommendations from a Multistakeholder Coalition. Journal of bone and mineral research: the official journal of the American Society for Bone and Mineral Research. 2020; 35(1): 36-52.
2. Odén A, McCloskey E V, Kanis J A, et al. Burden of high fracture probability worldwide: secular increases 2010-2040. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2015; 26(9): 2243-2248.
3. Chandran M, Ganesan G, Tan K B, et al. Cost-effectiveness of FRAX®-based intervention thresholds for management of osteoporosis in Singaporean women. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2021; 32(1): 133-144.
4. Kim S W, Won Y J, Chae D S, et al. A New Fracture Liaison Service Using the Mobile Application and IoT Sensor. Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference. 2019; 2019: 3486-3489.
5. Kwon Y, Lee J, Park J H, et al. Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women. Healthcare (Basel, Switzerland). 2022; 10(6).
6. Yang Q, Cheng H, Qin J, et al. A Machine Learning-Based Preclinical Osteoporosis Screening Tool (POST): Model Development and Validation Study. JMIR aging. 2023; 6:e46791.
7. Bui H M, Ha M H, Pham H G, et al. Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches. Scientific reports. 2022; 12(1): 20160.
8. Chang Ching-Yao, Peng Chung-Hsin, Chen Fang-Yu, et al. The risk factors determined by four machine learning methods for the change of difference of bone mineral density in post-menopausal women after three years follow-up. Sci Rep, 2023; 14(1): 23234.
9. Ou Yang W Y, Lai C C, Tsou M T, et al. Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data. International journal of environmental research and public health. 2021; 18(14).
10. E Erjiang, Wang Tingyan, Yang Lan, et al. Machine Learning Can Improve Clinical Detection of Low BMD: The DXA-HIP Study. Journal of Clinical Densitometry. 2021; 24(4): 527-537.
11. Lee Chaewon, Joo Gihun, Shin Seunghun, et al. Prediction of osteoporosis in patients with rheumatoid arthritis using machine learning. Scientific reports. 2023; 13(1): 21800.
12. Ravn Jakobsen P, Hermann A P, Søndergaard J, et al. Help at hand: Women’s experiences of using a mobile health application upon diagnosis of asymptomatic osteoporosis. SAGE open medicine. 2018; 6:2050312118807617.
13. Ryan P, Brown R L, Csuka M E, et al. Efficacy of Osteoporosis Prevention Smartphone App. Nursing research. 2020; 69(1): 31-41.
14. Li C T, Hung G K, Fong K N, et al. Effects of home-based occupational therapy telerehabilitation via smartphone for outpatients after hip fracture surgery: A feasibility randomised controlled study. Journal of telemedicine and telecare. 2022; 28(4): 239-247.
15. Park Y J, Lee S J, Shin N M, et al. Application and Effect of Mobiletype-Bone Health Intervention in Korean Young Adult Women with Low Bone Mass: A Randomized Control Trial. Asian nursing research. 2017; 11(1): 56-64.
16. Nguyen D T, Ho-Le TP, Pham L, et al. BONEcheck: A digital tool for personalized bone health assessment. Osteoporosis and sarcopenia. 2023; 9(3): 79-87.
17. Papaioannou A, McCloskey E, Bell A, et al. Use of an electronic medical record dashboard to identify gaps in osteoporosis care. Archives of osteoporosis. 2021; 16(1): 76.
18. Lu K, Wu Y M, Shi Q, et al. A novel fracture liaison service using digital health: impact on mortality in hospitalized elderly osteoporotic fracture patients. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2024; 35(1): 53-67.
19. Lindsay J R, Lawrenson G, English S, et al. A service evaluation of e-triage in the osteoporosis outpatient clinic-an effective tool to improve patient access? Archives of osteoporosis. 2020; 15(1): 53.
20. Ye C, Li J, Hao S, et al. Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. International journal of medical informatics. 2020; 137: 104105.
2. Odén A, McCloskey E V, Kanis J A, et al. Burden of high fracture probability worldwide: secular increases 2010-2040. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2015; 26(9): 2243-2248.
3. Chandran M, Ganesan G, Tan K B, et al. Cost-effectiveness of FRAX®-based intervention thresholds for management of osteoporosis in Singaporean women. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2021; 32(1): 133-144.
4. Kim S W, Won Y J, Chae D S, et al. A New Fracture Liaison Service Using the Mobile Application and IoT Sensor. Annual International Conference of the IEEE Engineering in Medicine and Biology Society IEEE Engineering in Medicine and Biology Society Annual International Conference. 2019; 2019: 3486-3489.
5. Kwon Y, Lee J, Park J H, et al. Osteoporosis Pre-Screening Using Ensemble Machine Learning in Postmenopausal Korean Women. Healthcare (Basel, Switzerland). 2022; 10(6).
6. Yang Q, Cheng H, Qin J, et al. A Machine Learning-Based Preclinical Osteoporosis Screening Tool (POST): Model Development and Validation Study. JMIR aging. 2023; 6:e46791.
7. Bui H M, Ha M H, Pham H G, et al. Predicting the risk of osteoporosis in older Vietnamese women using machine learning approaches. Scientific reports. 2022; 12(1): 20160.
8. Chang Ching-Yao, Peng Chung-Hsin, Chen Fang-Yu, et al. The risk factors determined by four machine learning methods for the change of difference of bone mineral density in post-menopausal women after three years follow-up. Sci Rep, 2023; 14(1): 23234.
9. Ou Yang W Y, Lai C C, Tsou M T, et al. Development of Machine Learning Models for Prediction of Osteoporosis from Clinical Health Examination Data. International journal of environmental research and public health. 2021; 18(14).
10. E Erjiang, Wang Tingyan, Yang Lan, et al. Machine Learning Can Improve Clinical Detection of Low BMD: The DXA-HIP Study. Journal of Clinical Densitometry. 2021; 24(4): 527-537.
11. Lee Chaewon, Joo Gihun, Shin Seunghun, et al. Prediction of osteoporosis in patients with rheumatoid arthritis using machine learning. Scientific reports. 2023; 13(1): 21800.
12. Ravn Jakobsen P, Hermann A P, Søndergaard J, et al. Help at hand: Women’s experiences of using a mobile health application upon diagnosis of asymptomatic osteoporosis. SAGE open medicine. 2018; 6:2050312118807617.
13. Ryan P, Brown R L, Csuka M E, et al. Efficacy of Osteoporosis Prevention Smartphone App. Nursing research. 2020; 69(1): 31-41.
14. Li C T, Hung G K, Fong K N, et al. Effects of home-based occupational therapy telerehabilitation via smartphone for outpatients after hip fracture surgery: A feasibility randomised controlled study. Journal of telemedicine and telecare. 2022; 28(4): 239-247.
15. Park Y J, Lee S J, Shin N M, et al. Application and Effect of Mobiletype-Bone Health Intervention in Korean Young Adult Women with Low Bone Mass: A Randomized Control Trial. Asian nursing research. 2017; 11(1): 56-64.
16. Nguyen D T, Ho-Le TP, Pham L, et al. BONEcheck: A digital tool for personalized bone health assessment. Osteoporosis and sarcopenia. 2023; 9(3): 79-87.
17. Papaioannou A, McCloskey E, Bell A, et al. Use of an electronic medical record dashboard to identify gaps in osteoporosis care. Archives of osteoporosis. 2021; 16(1): 76.
18. Lu K, Wu Y M, Shi Q, et al. A novel fracture liaison service using digital health: impact on mortality in hospitalized elderly osteoporotic fracture patients. Osteoporosis international: a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA. 2024; 35(1): 53-67.
19. Lindsay J R, Lawrenson G, English S, et al. A service evaluation of e-triage in the osteoporosis outpatient clinic-an effective tool to improve patient access? Archives of osteoporosis. 2020; 15(1): 53.
20. Ye C, Li J, Hao S, et al. Identification of elders at higher risk for fall with statewide electronic health records and a machine learning algorithm. International journal of medical informatics. 2020; 137: 104105.