請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91895完整後設資料紀錄
| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 郭年真 | zh_TW |
| dc.contributor.advisor | Raymond N. Kuo | en |
| dc.contributor.author | 楊如燁 | zh_TW |
| dc.contributor.author | Ju-Yeh Yang | en |
| dc.date.accessioned | 2024-02-26T16:20:15Z | - |
| dc.date.available | 2024-02-27 | - |
| dc.date.copyright | 2024-02-26 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-01-22 | - |
| dc.identifier.citation | Administration, F. a. D. (2019). Clinical Decision Support Software-Draft Guidance for Industry and Food and Drug Administration Staff. https://www.fda.gov/media/109618/download
Akbari, A., Clase, C. M., Acott, P., Battistella, M., Bello, A., Feltmate, P., Grill, A., Karsanji, M., Komenda, P., Madore, F., Manns, B. J., Mahdavi, S., Mustafa, R. A., Smyth, A., & Welcher, E. S. (2015). Canadian Society of Nephrology commentary on the KDIGO clinical practice guideline for CKD evaluation and management. Am J Kidney Dis, 65(2), 177-205. https://doi.org/10.1053/j.ajkd.2014.10.013 Akizawa, T., Okumura, H., Alexandre, A. F., Fukushima, A., Kiyabu, G., & Dorey, J. (2018). Burden of Anemia in Chronic Kidney Disease Patients in Japan: A Literature Review. Ther Apher Dial, 22(5), 444-456. https://doi.org/10.1111/1744-9987.12712 Ancker, J. S., Edwards, A., Nosal, S., Hauser, D., Mauer, E., Kaushal, R., & with the, H. I. (2017). Effects of workload, work complexity, and repeated alerts on alert fatigue in a clinical decision support system. BMC Med Inform Decis Mak, 17(1), 36. https://doi.org/10.1186/s12911-017-0430-8 Andersson Hagiwara, M., Suserud, B. O., Andersson-Gare, B., Sjoqvist, B., Henricson, M., & Jonsson, A. (2014). The effect of a Computerised Decision Support System (CDSS) on compliance with the prehospital assessment process: results of an interrupted time-series study. BMC Med Inform Decis Mak, 14, 70. https://doi.org/10.1186/1472-6947-14-70 Ash, J. S., Sittig, D. F., Campbell, E. M., Guappone, K. P., & Dykstra, R. H. (2007). Some unintended consequences of clinical decision support systems. AMIA Annu Symp Proc, 26-30. https://www.ncbi.nlm.nih.gov/pubmed/18693791 Avram, M. M., Blaustein, D., Fein, P. A., Goel, N., Chattopadhyay, J., & Mittman, N. (2003). Hemoglobin predicts long-term survival in dialysis patients: a 15-year single-center longitudinal study and a correlation trend between prealbumin and hemoglobin. Kidney Int Suppl(87), S6-11. https://doi.org/10.1046/j.1523-1755.64.s87.3.x Barbieri, C., Mari, F., Stopper, A., Gatti, E., Escandell-Montero, P., Martinez-Martinez, J. M., & Martin-Guerrero, J. D. (2015). A new machine learning approach for predicting the response to anemia treatment in a large cohort of End Stage Renal Disease patients undergoing dialysis. Comput Biol Med, 61, 56-61. https://doi.org/10.1016/j.compbiomed.2015.03.019 Barbieri, C., Molina, M., Ponce, P., Tothova, M., Cattinelli, I., Ion Titapiccolo, J., Mari, F., Amato, C., Leipold, F., Wehmeyer, W., Stuard, S., Stopper, A., & Canaud, B. (2016). An international observational study suggests that artificial intelligence for clinical decision support optimizes anemia management in hemodialysis patients. Kidney Int, 90(2), 422-429. https://doi.org/10.1016/j.kint.2016.03.036 Besarab, A., Bolton, W. K., Browne, J. K., Egrie, J. C., Nissenson, A. R., Okamoto, D. M., Schwab, S. J., & Goodkin, D. A. (1998). The effects of normal as compared with low hematocrit values in patients with cardiac disease who are receiving hemodialysis and epoetin. N Engl J Med, 339(9), 584-590. https://doi.org/10.1056/NEJM199808273390903 Birnie, K., Caskey, F., Ben-Shlomo, Y., Sterne, J. A., Gilg, J., Nitsch, D., & Tomson, C. (2017). Erythropoiesis-stimulating agent dosing, haemoglobin and ferritin levels in UK haemodialysis patients 2005-13. Nephrol Dial Transplant, 32(4), 692-698. https://doi.org/10.1093/ndt/gfw043 Brier, M. E., Gaweda, A. E., Dailey, A., Aronoff, G. R., & Jacobs, A. A. (2010). Randomized trial of model predictive control for improved anemia management. Clin J Am Soc Nephrol, 5(5), 814-820. https://doi.org/10.2215/CJN.07181009 Bright, T. J., Wong, A., Dhurjati, R., Bristow, E., Bastian, L., Coeytaux, R. R., Samsa, G., Hasselblad, V., Williams, J. W., Musty, M. D., Wing, L., Kendrick, A. S., Sanders, G. D., & Lobach, D. (2012). Effect of clinical decision-support systems: a systematic review. Ann Intern Med, 157(1), 29-43. https://doi.org/10.7326/0003-4819-157-1-201207030-00450 Brimble, K. S., Rabbat, C. G., McKenna, P., Lambert, K., & Carlisle, E. J. (2003). Protocolized anemia management with erythropoietin in hemodialysis patients: a randomized controlled trial. J Am Soc Nephrol, 14(10), 2654-2661. https://doi.org/10.1097/01.asn.0000088026.88074.20 Brunelli, S. M., Monda, K. L., Burkart, J. M., Gitlin, M., Neumann, P. J., Park, G. S., Symonian-Silver, M., Yue, S., Bradbury, B. D., & Rubin, R. J. (2013). Early trends from the Study to Evaluate the Prospective Payment System Impact on Small Dialysis Organizations (STEPPS). Am J Kidney Dis, 61(6), 947-956. https://doi.org/10.1053/j.ajkd.2012.11.040 Buising, K. L., Thursky, K. A., Black, J. F., MacGregor, L., Street, A. C., Kennedy, M. P., & Brown, G. V. (2008). Improving antibiotic prescribing for adults with community acquired pneumonia: Does a computerised decision support system achieve more than academic detailing alone?--A time series analysis. BMC Med Inform Decis Mak, 8, 35. https://doi.org/10.1186/1472-6947-8-35 Camacho, J., Zanoletti-Mannello, M., Landis-Lewis, Z., Kane-Gill, S. L., & Boyce, R. D. (2020). A Conceptual Framework to Study the Implementation of Clinical Decision Support Systems (BEAR): Literature Review and Concept Mapping. J Med Internet Res, 22(8), e18388. https://doi.org/10.2196/18388 Chait, Y., Kalim, S., Horowitz, J., Hollot, C. V., Ankers, E. D., Germain, M. J., & Thadhani, R. I. (2016). The greatly misunderstood erythropoietin resistance index and the case for a new responsiveness measure. Hemodial Int, 20(3), 392-398. https://doi.org/10.1111/hdi.12407 Collins, A. J., Li, S., St Peter, W., Ebben, J., Roberts, T., Ma, J. Z., & Manning, W. (2001). Death, hospitalization, and economic associations among incident hemodialysis patients with hematocrit values of 36 to 39%. J Am Soc Nephrol, 12(11), 2465-2473. https://www.ncbi.nlm.nih.gov/pubmed/11675424 Collins, A. J., Ma, J. Z., & Ebben, J. (2000). Impact of hematocrit on morbidity and mortality. Semin Nephrol, 20(4), 345-349. https://www.ncbi.nlm.nih.gov/pubmed/10928336 Davis, F. D. (1986). A technology acceptance model for empirically testing new end-user information systems : theory and results http://hdl.handle.net/1721.1/15192 Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-340. https://www.jstor.org/stable/249008 English, D., Ankem, K., & English, K. (2017). Acceptance of clinical decision support surveillance technology in the clinical pharmacy. Inform Health Soc Care, 42(2), 135-152. https://doi.org/10.3109/17538157.2015.1113415 Eschbach, J. W., Abdulhadi, M. H., Browne, J. K., Delano, B. G., Downing, M. R., Egrie, J. C., Evans, R. W., Friedman, E. A., Graber, S. E., Haley, N. R., & et al. (1989). Recombinant human erythropoietin in anemic patients with end-stage renal disease. Results of a phase III multicenter clinical trial. Ann Intern Med, 111(12), 992-1000. https://doi.org/10.7326/0003-4819-111-12-992 Esmaeilzadeh, P., Sambasivan, M., Kumar, N., & Nezakati, H. (2015). Adoption of clinical decision support systems in a developing country: Antecedents and outcomes of physician''s threat to perceived professional autonomy. Int J Med Inform, 84(8), 548-560. https://doi.org/10.1016/j.ijmedinf.2015.03.007 Ewusie, J. E., Soobiah, C., Blondal, E., Beyene, J., Thabane, L., & Hamid, J. S. (2020). Methods, Applications and Challenges in the Analysis of Interrupted Time Series Data: A Scoping Review. J Multidiscip Healthc, 13, 411-423. https://doi.org/10.2147/JMDH.S241085 Fishbane, S., & Berns, J. S. (2005). Hemoglobin cycling in hemodialysis patients treated with recombinant human erythropoietin. Kidney Int, 68(3), 1337-1343. https://doi.org/10.1111/j.1523-1755.2005.00532.x Fishbane, S., & Besarab, A. (2007). Mechanism of increased mortality risk with erythropoietin treatment to higher hemoglobin targets. Clin J Am Soc Nephrol, 2(6), 1274-1282. https://doi.org/10.2215/CJN.02380607 Freburger, J. K., Ng, L. J., Bradbury, B. D., Kshirsagar, A. V., & Brookhart, M. A. (2012). Changing patterns of anemia management in US hemodialysis patients. Am J Med, 125(9), 906-914 e909. https://doi.org/10.1016/j.amjmed.2012.03.011 Fuller, D. S., Bieber, B. A., Pisoni, R. L., Li, Y., Morgenstern, H., Akizawa, T., Jacobson, S. H., Locatelli, F., Port, F. K., & Robinson, B. M. (2016). International Comparisons to Assess Effects of Payment and Regulatory Changes in the United States on Anemia Practice in Patients on Hemodialysis: The Dialysis Outcomes and Practice Patterns Study. J Am Soc Nephrol, 27(7), 2205-2215. https://doi.org/10.1681/ASN.2015060673 Gaweda, A. E., Aronoff, G. R., Jacobs, A. A., Rai, S. N., & Brier, M. E. (2014). Individualized anemia management reduces hemoglobin variability in hemodialysis patients. J Am Soc Nephrol, 25(1), 159-166. https://doi.org/10.1681/ASN.2013010089 Gaweda, A. E., Jacobs, A. A., Aronoff, G. R., & Brier, M. E. (2008). Model predictive control of erythropoietin administration in the anemia of ESRD. Am J Kidney Dis, 51(1), 71-79. https://doi.org/10.1053/j.ajkd.2007.10.003 Gaweda, A. E., Jacobs, A. A., Aronoff, G. R., & Brier, M. E. (2018). Individualized anemia management in a dialysis facility - long-term utility as a single-center quality improvement experience. Clin Nephrol, 90(4), 276-285. https://doi.org/10.5414/CN109499 Hategeka, C., Ruton, H., Karamouzian, M., Lynd, L. D., & Law, M. R. (2020). Use of interrupted time series methods in the evaluation of health system quality improvement interventions: a methodological systematic review. BMJ Glob Health, 5(10). https://doi.org/10.1136/bmjgh-2020-003567 Hsiao, J. L., & Chen, R. F. (2016). Critical factors influencing physicians'' intention to use computerized clinical practice guidelines: an integrative model of activity theory and the technology acceptance model. BMC Med Inform Decis Mak, 16, 3. https://doi.org/10.1186/s12911-016-0241-3 Hung, S. C., Kuo, K. L., Tarng, D. C., Hsu, C. C., Wu, M. S., & Huang, T. P. (2014). Anaemia management in patients with chronic kidney disease: Taiwan practice guidelines. Nephrology (Carlton), 19(12), 735-739. https://doi.org/10.1111/nep.12332 Kalantar-Zadeh, K., & Aronoff, G. R. (2009). Hemoglobin variability in anemia of chronic kidney disease. J Am Soc Nephrol, 20(3), 479-487. https://doi.org/10.1681/ASN.2007070728 Kawamoto, K., Houlihan, C. A., Balas, E. A., & Lobach, D. F. (2005). Improving clinical practice using clinical decision support systems: a systematic review of trials to identify features critical to success. BMJ, 330(7494), 765. https://doi.org/10.1136/bmj.38398.500764.8F KDOQI. (2001). IV. NKF-K/DOQI Clinical Practice Guidelines for Anemia of Chronic Kidney Disease: update 2000. Am J Kidney Dis, 37(1 Suppl 1), S182-238. https://doi.org/10.1016/s0272-6386(01)70008-x KDOQI. (2006). KDOQI Clinical Practice Guidelines and Clinical Practice Recommendations for Anemia in Chronic Kidney Disease. Am J Kidney Dis, 47(5 Suppl 3), S11-145. https://doi.org/10.1053/j.ajkd.2006.03.010 Khairat, S., Marc, D., Crosby, W., & Al Sanousi, A. (2018). Reasons For Physicians Not Adopting Clinical Decision Support Systems: Critical Analysis. JMIR Med Inform, 6(2), e24. https://doi.org/10.2196/medinform.8912 Kilsdonk, E., Peute, L. W., & Jaspers, M. W. (2017). Factors influencing implementation success of guideline-based clinical decision support systems: A systematic review and gaps analysis. Int J Med Inform, 98, 56-64. https://doi.org/10.1016/j.ijmedinf.2016.12.001 Kilsdonk, E., Peute, L. W., Knijnenburg, S. L., & Jaspers, M. W. (2011). Factors known to influence acceptance of clinical decision support systems. Stud Health Technol Inform, 169, 150-154. https://www.ncbi.nlm.nih.gov/pubmed/21893732 Kimura, T., Arai, M., Masuda, H., & Kawabata, A. (2004). Impact of a pharmacist-implemented anemia management in outpatients with end-stage renal disease in Japan. Biol Pharm Bull, 27(11), 1831-1833. https://doi.org/10.1248/bpb.27.1831 Kliger, A. S., Foley, R. N., Goldfarb, D. S., Goldstein, S. L., Johansen, K., Singh, A., & Szczech, L. (2013). KDOQI US commentary on the 2012 KDIGO Clinical Practice Guideline for Anemia in CKD. Am J Kidney Dis, 62(5), 849-859. https://doi.org/10.1053/j.ajkd.2013.06.008 Kuragano, T., Matsumura, O., Matsuda, A., Hara, T., Kiyomoto, H., Murata, T., Kitamura, K., Fujimoto, S., Hase, H., Joki, N., Fukatsu, A., Inoue, T., Itakura, I., & Nakanishi, T. (2014). Association between hemoglobin variability, serum ferritin levels, and adverse events/mortality in maintenance hemodialysis patients. Kidney Int, 86(4), 845-854. https://doi.org/10.1038/ki.2014.114 Kwan, J. L., Lo, L., Ferguson, J., Goldberg, H., Diaz-Martinez, J. P., Tomlinson, G., Grimshaw, J. M., & Shojania, K. G. (2020). Computerised clinical decision support systems and absolute improvements in care: meta-analysis of controlled clinical trials. BMJ, 370, m3216. https://doi.org/10.1136/bmj.m3216 Lacson, E., Jr., Ofsthun, N., & Lazarus, J. M. (2003). Effect of variability in anemia management on hemoglobin outcomes in ESRD. Am J Kidney Dis, 41(1), 111-124. https://doi.org/10.1053/ajkd.2003.50030 Liberati, E. G., Ruggiero, F., Galuppo, L., Gorli, M., Gonzalez-Lorenzo, M., Maraldi, M., Ruggieri, P., Polo Friz, H., Scaratti, G., Kwag, K. H., Vespignani, R., & Moja, L. (2017). What hinders the uptake of computerized decision support systems in hospitals? A qualitative study and framework for implementation. Implement Sci, 12(1), 113. https://doi.org/10.1186/s13012-017-0644-2 Lines, S. W., Lindley, E. J., Tattersall, J. E., & Wright, M. J. (2012). A predictive algorithm for the management of anaemia in haemodialysis patients based on ESA pharmacodynamics: better results for less work. Nephrol Dial Transplant, 27(6), 2425-2429. https://doi.org/10.1093/ndt/gfr706 Liu, S., Reese, T. J., Kawamoto, K., Del Fiol, G., & Weir, C. (2021). A systematic review of theoretical constructs in CDS literature. BMC Med Inform Decis Mak, 21(1), 102. https://doi.org/10.1186/s12911-021-01465-2 Liu, Y., Wang, Z., Ren, J., Tian, Y., Zhou, M., Zhou, T., Ye, K., Zhao, Y., Qiu, Y., & Li, J. (2020). A COVID-19 Risk Assessment Decision Support System for General Practitioners: Design and Development Study. J Med Internet Res, 22(6), e19786. https://doi.org/10.2196/19786 Lopez Bernal, J., Soumerai, S., & Gasparrini, A. (2018). A methodological framework for model selection in interrupted time series studies. J Clin Epidemiol, 103, 82-91. https://doi.org/10.1016/j.jclinepi.2018.05.026 Maddux, F. W., Shetty, S., del Aguila, M. A., Nelson, M. A., & Murray, B. M. (2007). Effect of erythropoiesis-stimulating agents on healthcare utilization, costs, and outcomes in chronic kidney disease. Ann Pharmacother, 41(11), 1761-1769. https://doi.org/10.1345/aph.1K194 Mahadevaiah, G., Rv, P., Bermejo, I., Jaffray, D., Dekker, A., & Wee, L. (2020). Artificial intelligence-based clinical decision support in modern medical physics: Selection, acceptance, commissioning, and quality assurance. Med Phys, 47(5), e228-e235. https://doi.org/10.1002/mp.13562 Manns, B. J., White, C. T., Madore, F., Moist, L. M., Klarenbach, S. W., Barrett, B. J., Foley, R. N., Culleton, B. F., & Tonelli, M. (2008). Introduction to the Canadian Society of Nephrology clinical practice guidelines for the management of anemia associated with chronic kidney disease. Kidney Int Suppl(110), S1-3. https://doi.org/10.1038/ki.2008.267 McCarthy, J. T., Hocum, C. L., Albright, R. C., Rogers, J., Gallaher, E. J., Steensma, D. P., Gudgell, S. F., Bergstralh, E. J., Dillon, J. C., Hickson, L. J., Williams, A. W., & Dingli, D. (2014). Biomedical system dynamics to improve anemia control with darbepoetin alfa in long-term hemodialysis patients. Mayo Clin Proc, 89(1), 87-94. https://doi.org/10.1016/j.mayocp.2013.10.022 McRae, M. P., Dapkins, I. P., Sharif, I., Anderman, J., Fenyo, D., Sinokrot, O., Kang, S. K., Christodoulides, N. J., Vurmaz, D., Simmons, G. W., Alcorn, T. M., Daoura, M. J., Gisburne, S., Zar, D., & McDevitt, J. T. (2020). Managing COVID-19 With a Clinical Decision Support Tool in a Community Health Network: Algorithm Development and Validation. J Med Internet Res, 22(8), e22033. https://doi.org/10.2196/22033 Middleton, B., Sittig, D. F., & Wright, A. (2016). Clinical Decision Support: a 25 Year Retrospective and a 25 Year Vision. Yearb Med Inform, Suppl 1, S103-116. https://doi.org/10.15265/IYS-2016-s034 Minssen, T., Mimler, M., & Mak, V. (2020). When Does Stand-Alone Software Qualify as a Medical Device in the European Union?-The Cjeu''s Decision in Snitem and What it Implies for the Next Generation of Medical Devices. Med Law Rev, 28(3), 615-624. https://doi.org/10.1093/medlaw/fwaa012 Miskulin, D. C., Weiner, D. E., Tighiouart, H., Ladik, V., Servilla, K., Zager, P. G., Martin, A., Johnson, H. K., Meyer, K. B., & Medical Directors of Dialysis Clinic, I. (2009). Computerized decision support for EPO dosing in hemodialysis patients. Am J Kidney Dis, 54(6), 1081-1088. https://doi.org/10.1053/j.ajkd.2009.07.010 Miskulin, D. C., Zhou, J., Tangri, N., Bandeen-Roche, K., Cook, C., Ephraim, P. L., Crews, D. C., Scialla, J. J., Sozio, S. M., Shafi, T., Jaar, B. G., Boulware, L. E., & Investigators, D. E. N. P. O. i. E. S. R. D. S. (2013). Trends in anemia management in US hemodialysis patients 2004-2010. BMC Nephrol, 14, 264. https://doi.org/10.1186/1471-2369-14-264 Moxey, A., Robertson, J., Newby, D., Hains, I., Williamson, M., & Pearson, S. A. (2010). Computerized clinical decision support for prescribing: provision does not guarantee uptake. J Am Med Inform Assoc, 17(1), 25-33. https://doi.org/10.1197/jamia.M3170 National Health Research Institutes, T. S. o. N. (2015). 2015 Taiwan Chronic Kidney Disease Clinical Guidelines. National Health Research Institutes. National Health Research Institutes, T. S. o. N. (2017). 2016 Annual Report on Kidney Disease in Taiwan. National Health Research Institutes. https://www.tsn.org.tw/UI/L/L002_2018.aspx National Health Research Institutes, T. S. o. N. (2020). 2019 Annual Report on Kidney Disease in Taiwan. National Health Research Institutes. https://lib.nhri.edu.tw/NewWeb/nhri/ebook/39000400105446/ Nissenson, A. R., Wade, S., Goodnough, T., Knight, K., & Dubois, R. W. (2005). Economic burden of anemia in an insured population. J Manag Care Pharm, 11(7), 565-574. https://doi.org/10.18553/jmcp.2005.11.7.565 Park, E. S. P., Min S. (2020). Factors of the Technology Acceptance Model for Construction IT. Applied Sciences., 10(22), 8299. https://doi.org/10.3390/app10228299 Patterson, P., & Allon, M. (1998). Prospective evaluation of an anemia treatment algorithm in hemodialysis patients. Am J Kidney Dis, 32(4), 635-641. https://doi.org/10.1016/s0272-6386(98)70028-9 Perez-Garcia, R., Varas, J., Cives, A., Martin-Malo, A., Aljama, P., Ramos, R., Pascual, J., Stuard, S., Canaud, B., Merello, J. I., & group, O. R. D. (2018). Increased mortality in haemodialysis patients administered high doses of erythropoiesis-stimulating agents: a propensity score-matched analysis. Nephrol Dial Transplant, 33(4), 690-699. https://doi.org/10.1093/ndt/gfx269 Pisoni, R. L., Fuller, D. S., Bieber, B. A., Gillespie, B. W., & Robinson, B. M. (2012). The DOPPS Practice Monitor for US dialysis care: trends through August 2011. Am J Kidney Dis, 60(1), 160-165. https://doi.org/10.1053/j.ajkd.2012.04.001 Richardson, D., Bartlett, C., & Will, E. J. (2001). Optimizing erythropoietin therapy in hemodialysis patients. Am J Kidney Dis, 38(1), 109-117. https://doi.org/10.1053/ajkd.2001.25203 Robinson, B. M., Fuller, D. S., Bieber, B. A., Turenne, M. N., & Pisoni, R. L. (2012). The DOPPS Practice Monitor for US dialysis care: trends through April 2011. Am J Kidney Dis, 59(2), 309-312. https://doi.org/10.1053/j.ajkd.2011.11.005 Roshanov, P. S., Fernandes, N., Wilczynski, J. M., Hemens, B. J., You, J. J., Handler, S. M., Nieuwlaat, R., Souza, N. M., Beyene, J., Van Spall, H. G., Garg, A. X., & Haynes, R. B. (2013). Features of effective computerised clinical decision support systems: meta-regression of 162 randomised trials. BMJ, 346, f657. https://doi.org/10.1136/bmj.f657 Sakaguchi, Y., Hamano, T., Wada, A., & Masakane, I. (2019). Types of Erythropoietin-Stimulating Agents and Mortality among Patients Undergoing Hemodialysis. J Am Soc Nephrol, 30(6), 1037-1048. https://doi.org/10.1681/ASN.2018101007 Sheibani, R., Sheibani, M., Heidari-Bakavoli, A., Abu-Hanna, A., & Eslami, S. (2017). The Effect of a Clinical Decision Support System on Improving Adherence to Guideline in the Treatment of Atrial Fibrillation: An Interrupted Time Series Study. J Med Syst, 42(2), 26. https://doi.org/10.1007/s10916-017-0881-6 Shortliffe, E. H., & Sepulveda, M. J. (2018). Clinical Decision Support in the Era of Artificial Intelligence. JAMA, 320(21), 2199-2200. https://doi.org/10.1001/jama.2018.17163 Singh, A. K., Szczech, L., Tang, K. L., Barnhart, H., Sapp, S., Wolfson, M., Reddan, D., & Investigators, C. (2006). Correction of anemia with epoetin alfa in chronic kidney disease. N Engl J Med, 355(20), 2085-2098. https://doi.org/10.1056/NEJMoa065485 Solomon, S. D., Uno, H., Lewis, E. F., Eckardt, K. U., Lin, J., Burdmann, E. A., de Zeeuw, D., Ivanovich, P., Levey, A. S., Parfrey, P., Remuzzi, G., Singh, A. K., Toto, R., Huang, F., Rossert, J., McMurray, J. J., Pfeffer, M. A., & Trial to Reduce Cardiovascular Events with Aranesp Therapy, I. (2010). Erythropoietic response and outcomes in kidney disease and type 2 diabetes. N Engl J Med, 363(12), 1146-1155. https://doi.org/10.1056/NEJMoa1005109 Stauffer, M. E., & Fan, T. (2014). Prevalence of anemia in chronic kidney disease in the United States. PLoS One, 9(1), e84943. https://doi.org/10.1371/journal.pone.0084943 Sutton, R. T., Pincock, D., Baumgart, D. C., Sadowski, D. C., Fedorak, R. N., & Kroeker, K. I. (2020). An overview of clinical decision support systems: benefits, risks, and strategies for success. NPJ Digit Med, 3, 17. https://doi.org/10.1038/s41746-020-0221-y Thamer, M., Zhang, Y., Kaufman, J., Cotter, D., Dong, F., & Hernan, M. A. (2007). Dialysis facility ownership and epoetin dosing in patients receiving hemodialysis. JAMA, 297(15), 1667-1674. https://doi.org/10.1001/jama.297.15.1667 Thamer, M., Zhang, Y., Kaufman, J., Kshirsagar, O., Cotter, D., & Hernan, M. A. (2014). Major declines in epoetin dosing after prospective payment system based on dialysis facility organizational status. Am J Nephrol, 40(6), 554-560. https://doi.org/10.1159/000370334 Tolman, C., Richardson, D., Bartlett, C., & Will, E. (2005). Structured conversion from thrice weekly to weekly erythropoietic regimens using a computerized decision-support system: a randomized clinical study. J Am Soc Nephrol, 16(5), 1463-1470. https://doi.org/10.1681/ASN.2004080688 Trivedi, M. H., Kern, J. K., Marcee, A., Grannemann, B., Kleiber, B., Bettinger, T., Altshuler, K. Z., & McClelland, A. (2002). Development and implementation of computerized clinical guidelines: barriers and solutions. Methods Inf Med, 41(5), 435-442. https://www.ncbi.nlm.nih.gov/pubmed/12501817 Tsubakihara, Y., Nishi, S., Akiba, T., Hirakata, H., Iseki, K., Kubota, M., Kuriyama, S., Komatsu, Y., Suzuki, M., Nakai, S., Hattori, M., Babazono, T., Hiramatsu, M., Yamamoto, H., Bessho, M., & Akizawa, T. (2010). 2008 Japanese Society for Dialysis Therapy: guidelines for renal anemia in chronic kidney disease. Ther Apher Dial, 14(3), 240-275. https://doi.org/10.1111/j.1744-9987.2010.00836.x Venkatesh, V., Morris, M. G., Davis, G. B., Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478. Vitalii, P., Andrii, H., & Yevheniia, H. (2020). Stand-Alone Software as a Medical Device: Qualification and Liability Issues. Wiad Lek, 73(10), 2282-2288. https://www.ncbi.nlm.nih.gov/pubmed/33310964 Will, E. J., Richardson, D., Tolman, C., & Bartlett, C. (2007). Development and exploitation of a clinical decision support system for the management of renal anaemia. Nephrol Dial Transplant, 22 Suppl 4, iv31-iv36. https://doi.org/10.1093/ndt/gfm163 Winkelmayer, W. C. (2011). Potential effects of the new Medicare Prospective Payment System on drug prescription in end-stage renal disease care. Blood Purif, 31(1-3), 66-69. https://doi.org/10.1159/000321856 Yusof, M. M., Kuljis, J., Papazafeiropoulou, A., & Stergioulas, L. K. (2008). An evaluation framework for Health Information Systems: human, organization and technology-fit factors (HOT-fit). Int J Med Inform, 77(6), 386-398. https://doi.org/10.1016/j.ijmedinf.2007.08.011 Zhu, X., & Perazella, M. A. (2006). Nonhematologic complications of erythropoietin therapy. Semin Dial, 19(4), 279-284. https://doi.org/10.1111/j.1525-139X.2006.00173.x | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/91895 | - |
| dc.description.abstract | 研究背景: 臨床決策支援系統(Clinical Decision Support System, CDSS)是基於專家經驗或數據研發的電腦演算流程,用在臨床情境以改善照護流程或病人預後,但約一半的CDSS無法達到預期的效果,最重要的因素是臨床人員對CDSS的接受度不足。亞東紀念醫院針對血液透析病患的貧血處置,於2019年設置了個人化貧血處置的CDSS,希望能協助改善血液透析病患血色素(Hb)達標率過低的現象。過去文獻報告透析患者貧血處置CDSS介入後的效果評估,大多直接比較介入前後的差異,沒有考慮臨床醫師對CDSS的接受度。
研究目的:本研究利用量性與質性分析,評估腎臟科醫師對透析患者貧血處置CDSS的接受度,探討影響臨床醫師對CDSS接受度的因素。 研究方法: 量性研究部分,分析2016~2020年間亞東醫院血液透析資料,按年代分為CDSS介入前期(2016~2018)、過度期(2019)及介入後期(2020)三個時期,比較三個時期貧血治療相關指標的變化趨勢,並探討此變化是否受CDSS接受度的影響。質性部分,邀請17位長期在亞東醫院血液透析室工作的腎臟科醫師,進行半結構式(semi-structured)的深度訪談(in-depth interview),探討臨床醫師對CDSS的看法與顧慮。 研究結果: 量性分析納入717名患者共36,091次Hb量測。在多變數模型中,CDSS介入後的Hb升高(0.17 g/dL;95%信賴區間[CI]:CI 0.14–0.21 g/dL),造血激素(ESA)的使用量增加(264U/week; 95CI: 158-371U/week),達標率下降(勝算比為0.71倍,95% CI:0.66–0.75),超標率增加(勝算比為1.81倍,95% CI:3.1–3.6),而失敗率在校正後沒有顯著改變(勝算比為0.92倍,95% CI:0.84–1.01)。醫囑更改跟醫矚與建議的一致性的比例都增加(更改勝算比為2.55倍,95% CI:2.39–2.73;一致勝算比為3.37倍,95% CI:3.15–3.60)。路徑分析顯示,介入後血色素增加、ESA使用量增加、達標率下降、超標率增加、醫囑更改率增加,都有部分效果是經由醫囑與CDSS建議的一致性所中介。質性研究共訪談了十七名腎臟科醫師,所有腎臟科醫師一致認為CDSS對臨床工作有助益的。其中十四名醫師認為CDSS可以加速工作,節省了數據評估的時間;八名醫師稱讚了CDSS的提醒功能。十六名醫師提到了CDSS的各種限制,例如無法根據患者情況進行個性化、無法處理罕見或突發情況、參考時間過短等。沒有醫師認為CDSS會影響專業判斷或專業自主性。相反,有十一名醫師提到CDSS可以為臨床判斷提供有益的參考。值得注意的是,多達十二名醫師表達了對醫師依賴CDSS的擔憂。 結論: CDSS的介入確實會影響醫師處方行為,影響貧血控制,且醫師處方與CDSS建議的一致性,為CDSS介入效果的中介因素。臨床醫師一致認為CDSS能減輕工作負擔,且不認為會威脅醫師的專業,但擔憂有能會有依賴CDSS的風險。我們的研究強調了在設計和介入CDSS時,優化醫師對CDSS的接受度,減少醫師對CDSS的顧慮,才能達到CDSS的效果。 | zh_TW |
| dc.description.abstract | Background: Clinical decision support systems (CDSS) are developed based on algorithms in attempts to improve healthcare implementation or patient outcomes. The most important factor that hinders the successful implementation of CDSS is the acceptance of physicians. There remain gaps in the optimal ways to evaluate the performance of CDSS. One practical example resides in the CDSS-guided clinical management of anemia in patients on hemodialysis (HD).
Objective: This study evaluated the CDSS performance in anemia management in HD patients, the impact of physician compliance on CDSS efficacy and the relevant factors associated with physician acceptance. Methods: We conducted a mixed method study. We extracted the electronic health records of HD patients in Far Eastern Memorial Hospital (FEMH) between 2016 to 2020. The CDSS program was implemented in 2019, thus we divided data into two phases: Pre-CDSS phase (2016-2018) and Post-CDSS phase (2020). We compared the managements of anemia between the two phases using random intercept models. Physician compliance was defined as the concordance of erythropoietin-stimulating agent (ESA) doses between the CDSS recommendations and the actual prescriptions. For qualitative part, we invited nephrologists with more than 3 months experience of caring HD patients at FEMH to participate in a semi-structured in-depth interview. We particularly explored nephrologists’ concerns on CDSS. Results: We included 717 patients with a total of 36,091 hemoglobin (Hb) measurements. In the adjusted random intercept model, the post-CDSS phase showed an increased hemoglobin level (by 0.17 g/dL; 95% confidence interval [CI]: CI 0.14–0.21 g/dL), an increased ESA dosage (264U/week, 95CI: 158-371), a reduced on-target rate (OR 0.71, 95% CI:0.66-0.75), an increased over-target rate (OR 1.81, 95% CI:1.68-1.95), an increased prescription rate (OR 2.55, 95% CI:2.39-2.73) and an increased concordance rate (OR 3.37, 95% CI: 3.15–3.60). Path analysis revealed that the concordance rate significantly mediated the effects of CDSS. For qualitative part, a total of seventeen nephrologists were interviewed. All interveiwees concurred that CDSS was beneficial to clinical care. Fourteen (4/17) nephrologists believed CDSS could expedite the work and saved the time in interpretation of data. Eight physicians praised reminder functions of CDSS. Sixteen physicians mentioned about limitations of CDSS. No physician thought CDSS would influence professional autonomy. In contrast, eleven physicians mentioned that CDSS could provide beneficial inputs for clinical judgement. Of note, up to twelve physicians expressed concerns of physicians’ dependence on CDSS. Conclusions: Our findings confirmed that CDSS had effects on anemia management of HD patients and physician compliance was a significant intermediate factor for the CDSS efficacy. Nephrologists concurred that CDSS could lessen workload, but expressed concerns about over-dependency on CDSS. Our study highlights the importance of optimizing physician compliance while designing and implementing CDSSs to improve the healthcare outcomes. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-02-26T16:20:15Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-02-26T16:20:15Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | 口試委員會審定書 I
誌謝 II 中文摘要 III ABSTRACT V 第 壹 章 緒論 1 第一節 研究背景與動機 1 第二節 研究目的 4 第三節 研究重要性 4 第 貳 章 文獻探討 6 第一節 透析患者的貧血 6 第二節 臨床決策支援系統 10 第三節 運用臨床決策支援系統協助透析患者貧血治療 19 第四節 文獻總結與知識缺口 24 第 參 章 研究方法 33 第一節 研究設計與架構 33 第二節 研究假說 34 第三節 研究對象 34 第四節 資料來源與處理流程 35 第五節 研究變項與操作型定義 38 第六節 統計分析方法 41 第七節 質性訪談 42 第 肆 章 研究結果 47 第一節 量性分析 47 第二節 質性訪談 69 第 伍 章 討論 83 第一節 量性分析 83 第二節 質性訪談 85 第三節 綜合討論 87 第四節 研究限制 89 第五節 未來研究方向 92 參考文獻 94 附 表 105 附 件 107 | - |
| dc.language.iso | zh_TW | - |
| dc.subject | 臨床決策支援系統 | zh_TW |
| dc.subject | 血液透析 | zh_TW |
| dc.subject | 貧血 | zh_TW |
| dc.subject | 一致性 | zh_TW |
| dc.subject | 接受度 | zh_TW |
| dc.subject | 顧慮 | zh_TW |
| dc.subject | 專業自主性 | zh_TW |
| dc.subject | 倚賴 | zh_TW |
| dc.subject | Professional autonomy | en |
| dc.subject | Clinical decision support systems | en |
| dc.subject | Dependence | en |
| dc.subject | Hemodialysis | en |
| dc.subject | Anemia | en |
| dc.subject | Consistency | en |
| dc.subject | Acceptance | en |
| dc.subject | Concern | en |
| dc.title | 探討醫師對臨床決策支援系統的接受度--以透析患者貧血治療為例 | zh_TW |
| dc.title | Assessing Physicians' Acceptance of a Clinical Decision Support System: A Focus on Anemia Management for Hemodialysis Patients | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-1 | - |
| dc.description.degree | 博士 | - |
| dc.contributor.coadvisor | 鄭守夏 | zh_TW |
| dc.contributor.coadvisor | Shou-Hsia Cheng | en |
| dc.contributor.oralexamcommittee | 楊銘欽;官晨怡;劉德明;林寬佳 | zh_TW |
| dc.contributor.oralexamcommittee | Ming-Chin Yang;Chen-I Kuan;Der-Ming Liou;Kuan-Chia Lin | en |
| dc.subject.keyword | 臨床決策支援系統,血液透析,貧血,一致性,接受度,顧慮,專業自主性,倚賴, | zh_TW |
| dc.subject.keyword | Clinical decision support systems,Hemodialysis,Anemia,Consistency,Acceptance,Concern,Professional autonomy,Dependence, | en |
| dc.relation.page | 108 | - |
| dc.identifier.doi | 10.6342/NTU202400153 | - |
| dc.rights.note | 同意授權(限校園內公開) | - |
| dc.date.accepted | 2024-01-22 | - |
| dc.contributor.author-college | 公共衛生學院 | - |
| dc.contributor.author-dept | 健康政策與管理研究所 | - |
| dc.date.embargo-lift | 2024-11-20 | - |
| 顯示於系所單位: | 健康政策與管理研究所 | |
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