臺灣博碩士論文加值系統

CONTEXT：「腎功能不全」是顯影劑誘發腎病變(CIN)最重要的獨立危險因子，若將顯影劑注射於腎功能不全病患，可能引發腎臟病變。然而例行顯影性檢查時，可能因把關疏忽而導致此不良事件。決策支援系統可以偵測醫師排檢行為並減少醫療疏失。
PURPOSE：在先導研究中，先檢視例行顯影檢查流程，定義出潛在不良事件的風險種類。再由回溯性定量分析，測量潛在不良事件的發生頻率。並對醫師進行問卷調查，分析檢查流程的缺陷及醫師可接受的加強安全方式。在主要研究中，建立一個目的在「降低將顯影劑注射到腎功能不全患者之機率」的臨床決策支援系統，並評估其降低「潛在CIN風險」的效果及使用者評價。
INTERVENTION：建立一個「Anti-CIN」決策支援系統，架構在電腦醫令系統中，於醫師開立檢查時，對潛在CIN風險病人即時跳出警示，並提供決策支援。
MAIN OUTCOME MEASURES：「CIN高風險」病人，在醫令系統定義血清Creatinine濃度大於 1.4 mg/dL為提示臨界值，「風險不明」之可能有害事件，意指在顯影檢查前未曾檢驗腎功能。
DESIGN AND SETTINGS：2006年2至4月為介入實驗期，令「Anti-CIN系統」運作三個月，而2003年1月至2005年12月為無介入的對照期間。
RESULTS：實驗組在三個月中，共有1,308筆嘗試由電腦醫令安排的顯影檢查，49筆因Anti-CIN的警示而取消，其中30筆為CIN高風險病人－13筆是在警示時立即由醫令系統取消；另17筆原無腎功能報告，經提示，加驗腎功能後發現腎功能異常，最後取消檢查。這分別說明了系統的直接阻擋效應(13)與間接阻擋效應(17)。在醫師排檢時，由系統偵測到的高風險與風險不明病人比例，在門診6.2% vs. 52%，住院18.6% vs. 12.3%。面對門診的高風險病人，高達67.3%的醫師承認在開單時不知病人為高風險，經提示後，有38.5%的檢查被取消。對原未驗腎功能的病人，提示後22.5%決定加驗腎功能。若比較對照期間與介入期間「已完成」的顯影檢查，其總數各為10,761 vs. 1,015，系統介入後，在顯影檢查前有驗腎功能的比例在門診由49.8%上升到62%，統計極端顯著(p < 0.001)。在住院部門，介入後風險分佈的改變並不顯著(p=0.243)
CONCLUSIONS：在系統介入後，Anti-CIN對門診病人的保護作用較大。對高風險者的取消檢查效應與風險不明者的加驗腎功能效應，可減少因醫療疏失所導致的顯影劑腎毒性，進而提高病患安全。

CONTEXT: Baseline renal insufficiency has been shown to be the most important independent risk factor of contrast-induced nephropathy (CIN). The effects of renal insufficiency on CIN may be neglected in daily contrast-enhanced image study-ordering practices. A clinical decision support system(CDSS) with computerized physician order entry(CPOE) may optimize physician’s ordering behaviors and reduce medical errors.
OBJECTIVE: The purposes of 3 pilot studies are as follows: in the qualitative study, to identify and define the potential CIN adverse events by evaluating the decision flow of contrast-enhanced procedures; in the retrospective study, to measure the incidence of potential CIN adverse events; and in the physician-responded questionnaire survey, to identify an acceptable way to reduce the risk of CIN. In the main study, the aim is to determine whether a CDSS designed for reducing mis-injecting contrast-medium to patients with renal insufficiency would reduce the risk of CIN.
INTERVENTION: With a real-time Anti-CIN CDSS for monitoring physician’s contrast-enhanced image study orders to patients with potential CIN risks, online pop-up reminder with optimized decision options were displayed when patients with high or unknown CIN risk were detected.
DESIGN AND SETTINGS: The Anti-CIN system was activated on the CPOE in one medical center for a 3-month intervention period from February to April 2006; compared with a 3-year control period without intervention from January 2003 to December 2005. All orders to arrange contrast-enhanced image studies were recoreded with patients’ CIN risk.
MAIN OUTCOME MEASURES: The patients arranged to contrast-enhanced imaging with serum creatinine level greater than 1.4mg/dL are classified as high CIN risk and patients without renal function test(RFT) results are classified as unknown CIN risk.
RESULTS: A total of 1,308 orders from CPOE were included in the intervention period. 49 orders were cancelled, attributed to the interception of Anti-CIN system, of which 30 were high CIN risk patients. 13 of 30 cancelled orders were attributed to the direct order-canceling effect of the system. The other 17 were cancelled due to high risks which were identified in an indirect manner because initial reminding of no available RFT reports from the system facilitated further RFT checking, hence high risks detected subsequently. The proportion of high risk and unknown risk was 6.2% vs. 52% in outpatients, and 18.6% vs. 12.3% in inpatients, respectively. Among orders for high risk outpatients, physicians admitted that the risks were not noticed initially in 67.3% of them before alerts. 38.5% of them were cancelled after physicians were reminded. 22.5% of the orders for unknown risk patients were modified with further RFT check after physicians were reminded. Risk distribution in completed contrast-enhanced image studies was further evaluated. A total of 10,761 and 1,015 exams were included in the control and intervention group respectively. The pre-imaging RFT checking rate increased significantly from 49.8% to 62% in outpatients after intervention (p<0.001). However, the change of risk distribution in inpatients was not significant (p=0.243).
CONCLUSIONS: The Anti-CIN CDSS merged with CPOE appears to result in reducing the potential CIN adverse events both by means of the order-cancelling effect to the high risk patients and the further RFT-checking effect to the unknown risk patients. The protective effect has proven to be more prominent to the outpatients than to the inpatients.

標題 i
審定書 ii
上網授權書 iii
誌謝 iv
目錄 v
表目錄 viii
圖目錄 x
中文摘要 xi
英文摘要 xiii
第1章 緒論 1
1.1 研究動機 1
1.2 研究目的 2
第2章 文獻查證 3
2.1 顯影劑誘發腎臟病變 (CIN) 3
2.2 病患安全與電腦醫令系統(CPOE)、臨床決策支援系統(CDSS) 7
第3章 先導研究：風險分析 14
3.1 傳統流程分析 15
3.2 風險分類 18
3.3 回溯性定量研究 20
3.3.1 目的 20
3.3.2 方法 20
3.3.3 結果 21
3.4 問卷分析 22
3.4.1 方法 22
3.4.2 結果 23
3.5 風險分析的摘要與結論 27
第4章 系統設計與方法 28
4.1 系統需求分析 28
4.2 系統架構 30
4.2.1 觸發條件 30
4.2.2 風險分級 31
4.2.3 風險判斷邏輯 32
4.2.4 使用者介面 33
4.2.5 風險分級與決策選項配對 34
4.2.6 決策後的系統背景工作 36
4.3 系統建置 36
第5章 系統評估與結果 40
5.1 研究設計 40
5.2 定性研究：初期使用者問卷分析 41
5.2.1 材料與方法 41
5.2.2 結果 41
5.3 定量研究：敘述性統計 44
5.3.1 材料與方法 44
5.3.2 結果 45
5.4 定量研究：推論性統計 61
5.4.1 研究設計 61
5.4.2 材料與方法 62
5.4.3 結果 64
第6章 討論 68
6.1 ANTI-CIN系統於降低病人CIN風險的效果 68
6.2 自訂的CIN風險分級與設計原則 71
6.3 本決策支援系統的特色 73
第7章 結論與建議 75
7.1 結論 75
7.2 研究限制與建議 76
第8章 參考文獻 77
第9章 附錄 83
9.1 本文常用英文縮寫名詞對照表 83
9.2 先導研究之CIN風險分析問卷 83
9.3 ANTI-CIN系統使用者評估問卷 88

1.Erdogan A, Davidson CJ. Recent clinical trials of iodixanol. Rev Cardiovasc Med 2003;4 Suppl 5:S43-50.
2.Parfrey PS, Griffiths SM, Barrett BJ, et al. Contrast material-induced renal failure in patients with diabetes mellitus, renal insufficiency, or both. A prospective controlled study. N Engl J Med 1989;320(3):143-9.
3.McCullough PA, Sandberg KR. Epidemiology of contrast-induced nephropathy. Rev Cardiovasc Med 2003;4 Suppl 5:S3-9.
4.Guitterez NV, Diaz A, Timmis GC, et al. Determinants of serum creatinine trajectory in acute contrast nephropathy. J Interv Cardiol 2002;15(5):349-54.
5.Nikolsky E, Aymong ED, Dangas G, Mehran R. Radiocontrast nephropathy: identifying the high-risk patient and the implications of exacerbating renal function. Rev Cardiovasc Med 2003;4 Suppl 1:S7-S14.
6.Lasser EC, Lyon SG, Berry CC. Reports on contrast media reactions: analysis of data from reports to the U.S. Food and Drug Administration. Radiology 1997;203(3):605-10.
7.Conti CR. Cardiovascular happenings in Europe. Clin Cardiol 2000;23(7):471-2.
8.Rihal CS, Textor SC, Grill DE, et al. Incidence and prognostic importance of acute renal failure after percutaneous coronary intervention. Circulation 2002;105(19):2259-64.
9.Hou SH, Bushinsky DA, Wish JB, Cohen JJ, Harrington JT. Hospital-acquired renal insufficiency: a prospective study. Am J Med 1983;74(2):243-8.
10.Behrend T, Miller SB. Acute renal failure in the cardiac care unit: etiologies, outcomes, and prognostic factors. Kidney Int 1999;56(1):238-43.
11.Levy EM, Viscoli CM, Horwitz RI. The effect of acute renal failure on mortality. A cohort analysis. Jama 1996;275(19):1489-94.
12.McCullough PA, Wolyn R, Rocher LL, Levin RN, O'Neill WW. Acute renal failure after coronary intervention: incidence, risk factors, and relationship to mortality. Am J Med 1997;103(5):368-75.
13.Cantley LG, Spokes K, Clark B, McMahon EG, Carter J, Epstein FH. Role of endothelin and prostaglandins in radiocontrast-induced renal artery constriction. Kidney Int 1993;44(6):1217-23.
14.Weisberg LS, Kurnik PB, Kurnik BR. Radiocontrast-induced nephropathy in humans: role of renal vasoconstriction. Kidney Int 1992;41(5):1408-15.
15.Yoshioka K, Murakami K, Maki S. [Growth factors: a regulator of renal function]. Nippon Rinsho 1992;50(12):2931-6.
16.Bakris GL, Gaber AO, Jones JD. Oxygen free radical involvement in urinary Tamm-Horsfall protein excretion after intrarenal injection of contrast medium. Radiology 1990;175(1):57-60.
17.Solomon R, Werner C, Mann D, D'Elia J, Silva P. Effects of saline, mannitol, and furosemide to prevent acute decreases in renal function induced by radiocontrast agents. N Engl J Med 1994;331(21):1416-20.
18.Barrett BJ, Carlisle EJ. Metaanalysis of the relative nephrotoxicity of high- and low-osmolality iodinated contrast media. Radiology 1993;188(1):171-8.
19.Rudnick MR, Goldfarb S, Wexler L, et al. Nephrotoxicity of ionic and nonionic contrast media in 1196 patients: a randomized trial. The Iohexol Cooperative Study. Kidney Int 1995;47(1):254-61.
20.Aspelin P. [Reduced risk of nepropathy with isoosmolar contrast media]. Lakartidningen 2003;100(23):2067-8.
21.Briguori C, Manganelli F, Scarpato P, et al. Acetylcysteine and contrast agent-associated nephrotoxicity. J Am Coll Cardiol 2002;40(2):298-303.
22.Birck R, Krzossok S, Markowetz F, Schnulle P, van der Woude FJ, Braun C. Acetylcysteine for prevention of contrast nephropathy: meta-analysis. Lancet 2003;362(9384):598-603.
23.Marenzi G, Marana I, Lauri G, et al. The prevention of radiocontrast-agent-induced nephropathy by hemofiltration. N Engl J Med 2003;349(14):1333-40.
24.Kohn LT, J. M. Corrigan and M. S. Donaldson. To Err is Human: Building a Safer Health System. Institute of Medicine Report.: Washington, DC: National Academy Press; 2000.
25.林宏榮. 病患安全風險因素之研究—以台灣大型醫院急診部門為例: 成大; 2003.
26.Altman DE, Clancy C, Blendon RJ. Improving Patient Safety—Five Years after the IOM Report. New England Journal of Medicine 2004;351:2041-3.
27.Leape LL, Berwick DM. Five years after To Err Is Human: what have we learned. JAMA 2005;293(19):2384-90.
28.Kaushal R, Shojania KG, Bates DW. Effects of computerized physician order entry and clinical decision support systems on medication safety: a systematic review. Arch Intern Med 2003;163(12):1409-16.
29.Barker KN, Mikeal RL, Pearson RE, Illig NA, Morse ML. Medication errors in nursing homes and small hospitals. Am J Hosp Pharm 1982;39(6):987-91.
30.Bates DW, Cullen DJ, Laird N, et al. Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. Jama 1995;274(1):29-34.
31.Cullen DJ, Sweitzer BJ, Bates DW, Burdick E, Edmondson A, Leape LL. Preventable adverse drug events in hospitalized patients: a comparative study of intensive care and general care units. Crit Care Med 1997;25(8):1289-97.
32.Leape LL, Bates DW, Cullen DJ, et al. Systems analysis of adverse drug events. ADE Prevention Study Group. Jama 1995;274(1):35-43.
33.Bates DW, Teich JM, Lee J, et al. The impact of computerized physician order entry on medication error prevention. J Am Med Inform Assoc 1999;6(4):313-21.
34.Bates DW, Leape LL, Cullen DJ, et al. Effect of computerized physician order entry and a team intervention on prevention of serious medication errors. JAMA 1998;280(15):1311-6.
35.Burton ME, Ash CL, Hill Jr DP, Handy T, Shepherd MD, Vasko MR. A controlled trial of the cost benefit of computerized bayesian aminoglycoside administration. Clin Pharmacol Ther 1991;49(6):685-94.
36.Casner PR, Reilly R, Ho H. A randomized controlled trial of computerized pharmacokinetic theophylline dosing versus empiric physician dosing. Clin Pharmacol Ther 1993;53(6):684-90.
37.Evans RS, Classen DC, Pestotnik SL, Lundsgaarde HP, Burke JP. Improving empiric antibiotic selection using computer decision support. Archives of Internal Medicine 1994;154(8):878-84.
38.Evans RS, Pestotnik SL, Ph R, et al. A Computer-Assisted Management Program for Antibiotics and Other Antiinfective Agents. New England Journal of Medicine.
39.Hurley SF, Dziukas LJ, McNeil JJ, Brignell MJ. A randomized controlled clinical trial of pharmacokinetic theophylline dosing. Am Rev Respir Dis 1986;134(6):1219-24.
40.In RA. Guided Medication Dosing for Inpatients With Renal Insufficiency. JAMA 2001;286:2839-44.
41.Mungall DR, Anbe D, Forrester PL, et al. A prospective randomized comparison of the accuracy of computer-assisted versus GUSTO nomogram--directed heparin therapy. Clin Pharmacol Ther 1994;55(5):591-6.
42.Overhage JM, Tierney WM, Zhou XHA, McDonald CJ. A Randomized Trial of" Corollary Orders" to Prevent Errors of Omission. Journal of the American Medical Informatics Association 1997;4:364-75.
43.Teich JM, Merchia PR, Schmiz JL, Kuperman GJ, Spurr CD, Bates DW. Effects of computerized physician order entry on prescribing practices. Arch Intern Med 2000;160(18):2741-7.
44.White RH, Hong R, Venook AP, et al. Initiation of warfarin therapy: comparison of physician dosing with computer-assisted dosing. J Gen Intern Med 1987;2(3):141-8.
45.Garg AX, Adhikari NK, McDonald H, et al. Effects of computerized clinical decision support systems on practitioner performance and patient outcomes: a systematic review. JAMA 2005;293(10):1223-38.
46.記者:廖宗慶. 少女注射顯影劑死　疑醫療疏失. TVBS 2005/6/23.
47.記者:簡東源. 服下顯影劑不治 死因待查. 中國時報 2005/08/05.
48.Sittig DF, Krall M, Kaalaas-Sittig J, Ash JS. Emotional Aspects of Computer-based Provider Order Entry: A Qualitative Study. J Am Med Inform Assoc 2005.
49.Fitzpatrick J, Koh JS. If you build it (right), they will come: the physician-friendly CPOE. Not everything works as planned right out of the box. A Mississippi hospital customizes its electronic order entry system for maximum use by physicians. Health Manag Technol 2005;26(1):52-3.
50.Bates DW, Gawande AA. Improving safety with information technology. N Engl J Med 2003;348(25):2526-34.
51.Kremsdorf R. CPOE: not the first step toward patient safety. Health Manag Technol 2005;26(1):66, 5.