臺灣博碩士論文加值系統

研究背景：
開方失誤為用藥疏失的主要原因，而這一類的疏失是最常可避免的。有文獻建議以鑲嵌於電腦醫囑開方系統（CPOE）的臨床決策支援系統（CDSS），可達到減少開方失誤之可能。台大醫院於2007年住院醫療資訊系統轉型，至今已開發許多CDSS功能輔助醫師開方。
研究目的：
建置一鑲嵌於本院現有之電腦醫囑開方系統之臨床決策支援系統，評估重複用藥檢核CDSS之成效，並分析CDSS對於藥師通報之開方跡近錯誤的影響。
研究方法：
本研究分兩階段，第一階段為建立一鑲嵌於CPOE可檢核同類藥併用之CDSS，CDSS於開方者開立同類藥（定義為同類藥併用）或同樣藥品（定義為重複開方）時，會出現警示視窗，開方者如未接受警示視窗建議仍需併用同類藥或同樣藥，需提供併用原因，此併用原因會出現於調劑單以及藥師線上覆核畫面供藥師參考。
利用下列指標評估CDSS成效：攔截率、開方者接受率、多少病人就有一人受益、以及可能節省藥費。藉由CDSS所記錄之併用資料，分析併用處方型態。第二階段利用台大醫院院內的開方跡近錯誤通報系統，分析CDSS及藥師線上覆核對於不同時期的開方跡近錯誤（尤其是同類藥併用及重複開方）的影響。
研究結果：
2009年7月至10月期間所有住院新開處方數有727,322筆，同期間處方開立人次共27,156人，CDSS檢核到住院病房之開方為重複用藥資料共25,048筆，扣除因開方者不熟悉警示視窗操作，使警示視窗重複跳出的資料後，共22,654筆納入分析。平均每開立100筆處方會發生3次重複用藥，開方者接受率為79.7％，每1.5位病人就有一人因此系統而受益。估計四個月節省藥費共台幣32,759,661元，平均每日可節省台幣266,338元，相當於每年可節省約台幣97,213,370元，相當於可節省1206.4元/人。分析未接受電腦攔截指示之處方，發現許多是開方者不熟悉CPOE及CDSS所致，包括開方者不以「特殊劑量開方」開立相同藥品不同劑量之處方、不以「refill」開立多次劑量包裝的藥品、不熟悉可直接於警示視窗停用欲停用藥品。
檢核出重複開方的醫師接受率稍高於檢核出同類藥併用的接受率（82.4％ vs. 74.9％）。
少數類別開方者容易忽視CDSS建議，堅持併用之類別：維生素B群藥品（B.C. cap、Beesix、Methycobal、Alinamin-F50），同時以兩種α-antagonist治療BPH以及高血壓，同時以Smecta 與Pecolin susp治療病人的腹瀉，同時使用Tinten（acetaminophen）與Depain X（acetaminophen,propoxyphen）治療疼痛。此類同類藥併用仍需藥師的介入。
開方者接受率較低的類別如抗憂鬱劑（64.7％）、精神穩定劑（64.3％）、帕金森氏症用藥（70.8％）以及眩暈症用藥（62.7％），接受率較低之科部為精神科（49.6％），應與該科別醫師合作，思考如何改進CDSS。
比較2008年7~10月與2009年7~10月兩時期之開方跡近錯誤通報處方，整體開方跡近錯誤通報率由0.84％增為1.6％，醫師接受率由91.6％減少為88.5％。依科別來看，以急診醫學部之開方跡近錯誤通報率增加最多（由0.9％增為8.9％），其次為內科部（由0.9％增為1.7％）、外科部（由1.1％增為2.2％）、小兒部（由1.2％增為2.2％）。由調劑單位來看，以總院住院UDD之通報率量增加最多，平均每月921件增加至2461件。開方跡近錯誤原因為「藥師建議監測」之通報量增加最多（增加5倍），原因為「藥師建議修改處方」之通報量也增加1倍，其中在藥師端有相關提示功能的原因別：「劑量/頻次問題」、「藥品輸注/速率問題」、「併用問題」、「藥品交互作用」等開方跡近錯誤通報量也有明顯的增加。其中在藥師端無相關提示功能的原因別：藥師建議修改處方的「用藥途徑或劑型問題」、「用藥禁忌問題」、「藥品相容性問題」、「更適當用藥/配方」以及藥師建議監測等開方跡近錯誤通報量也有明顯增加。
開方跡近錯誤通報處方為「CDSS可檢核之同類藥併用」略為減少，對照組研究期間總通報處方筆數為395筆，實驗組期間為304筆，醫師接受率由對照組期間的82.8％降至實驗組期間72％。而藥師通報處方為「CDSS可檢核之重複開方」佔全部新開處方數之百分比則增加10倍，（0.01％ vs. 0.1％，由93件增加至812件），醫師接受率則略為減少，由96.8％降至92.4％。
開方者對於電腦警示為重複開方之接受率高於同類藥併用之警示（82.4％ vs. 74.9％），然而藥師對於重複開方之通報量卻增加許多。對照組及實驗組兩時期皆有針對重複開方檢核之功能，且開方者未接受電腦警示仍開立兩筆藥品之處方，會於調劑單上及藥師線上覆核畫面提醒藥師，推估是因藥師線上覆核之功能增加了藥師發現此類開方跡近錯誤的能力。
結論：
本研究所建立之CDSS成效良好，攔截率3％、整體接受率79.7％、每1.5位病人就有一位因此系統而受益，且藥師通報重複用藥之比例增加。可再加強開方者對於CPOE及CDSS熟悉度，以增加接受率。

Backgrounds:
Prescribing errors are the most common type of medication error and are often preventable. Integrate clinical decision support systems (CDSSs) into the computerized physician order entry (CPOE) may reduce medication error rates. Different CDSSs have been developed and integrated into CPOE successively in National Taiwan University Hospital since a new CPOE was implemented in 2007.
Objective:
The first part of the study is to integrate a novel clinical decision support system into a computerized physician order entry. The second part of the study is to know the effect of a clinical decision support system which checks therapeutic duplication and repeated prescription in inpatient department. The third part of the study, we use the medication error report system of National Taiwan University Hospital to evaluate the influence of CDSS on prescribing errors.
Method:
The first step is to integrate a novel clinical decision support system into a computerized physician order entry, which checks therapeutic duplication (the category which was developed by pharmacists in National Taiwan University Hospital) and repeated prescription, the CDSS will show a pop-out window to alert physicians when it detect there are therapeutic duplicated or repeated prescription. If the physicians decided to override the alert, he/she needs to provide a reason, the reason will show on the dispensing list also on the pharmacy on-line verification.
To evaluate the effect of clinical decision support system, we use interception rate, acceptance rate, what’s the rate of the patients can benefit from the CDSS in the population. We also estimate how much money can be saved by the CDSS. We also evaluate the influence of prescription errors reported by the pharmacists, especially the therapeutic duplication and repeated prescription.
Result:
During the study period July-Oct in 2009, there were total 727,322 new prescriptions, 27,156 patients had prescriptions, and 25,048 alerts were generated. Exclude the data which were generated by physician’s unfamiliar to the CDSS pop-out window, total 22,654 alerts were analyzed. Our study found interception rate is 3％, which means every 100 prescriptions may have 3 duplicated medications. Acceptance rate is 79.7％. One patient can benefit from the CDSS in every 1.5 patients. The CDSS can save NT＄32,759,661 in the study period, which equals to NT＄266,338 per day, NT＄97,213,370 per year. Divided by 27,156 patients who had prescriptions, which means it could save NT＄1206.4 per patient.
The acceptance rate which the CDSS detected as repeated prescription is a little higher than the CDSS detected as therapeutic duplication (82.4％ vs. 74.9％).
From the prescriptions physicians override from the CDSS, we found most of the alerts were generated because of physicians’ unfamiliar to use「specific dose order」to prescribe same drug used in different dose and different repeat pattern. The other reason
the alerts were generated because of physicians’ unfamiliar to use「refill」to order drugs which has multiple doses per package. The final reason is that physicians don’t know they can discontinue the drug directly on the pop-out alert window.
Physicians often override some of the therapeutic category, especially, physicians use different drugs both contain vitamin B, or use tow kind of α-receptor antagonist to treat BPH and hypertension in the same time, use Smecta and Pecolin suspension to treat diarrhea in the same time, use Tinten and Depain X as pain control in the same time. These kind of medication errors still need pharmacists to intervene.
The acceptance rate in some of the therapeutic categories were below average, like anti-depressant(64.7％), anti-psychotics(64.3％), drugs used in parkinsonism(70.8％), anti-vertigo(62.7％). Also the acceptance rate of psychotic department was below average(49.6％). We need to discuss with the physicians to find a solution to make our CDSS be more relevant to clinical.
Compare the prescribing errors in different period, total prescribing error rate was 0.84％ in Jul-Oct,2008, 1.6％ in Jul-Oct,2009. Physicians’ acceptance rate decreased from 91.6％ to 88.5％. The largest increase prescribing error rate was found in emergency medicine department (0.9％ to 8.9％), followed by medicine department (0.9％ to 1.7％), surgery department (1.1％ to 2.2％) and pediatric department (1.2％ to 2.2％). Of all the inpatient pharmacies, the largest increase prescribing error rate was found in inpatient pharmacy in main region, which was 921 prescribing errors /month, increase to 2461 prescribing errors /month.
The reason “pharmacist advice to monitor”, increased 5-fold. The reason ” pharmacist advice to change the order” also increased 2-fold, some of the prescribing errors reported by the pharmacist has the tag in the page for pharmacists’ on-line verification, like the subgroup “dose/frequency”, “infusion rate”, “combination problem”, ”drug-drug interaction”, these prescribing errors also increased. The other prescribing errors with no tag in the page of pharmacists’ online verification also increase, like “medication route and formulation problem”, “drug contraindication”, “drug compatibility”, “advise to use better drug” and “pharmacist advice to monitor”.
In the total new prescriptions, the percentage of prescribing errors reported as “therapeutic duplication which can be detected by CDSS” decreased a little (0.06％ decrease to 0.04％), acceptance rate also decreased (82.8％ to 72％). In the total new prescriptions, the percentage of prescribing errors reported as “repeated prescriptions which can be detected by CDSS” increased 10-fold (0.01％ increase to 0.1％),
acceptance rate decreased a little (96.8％ to 92.4％).
Conclusion:
The effect of CDSS implemented in National Taiwan University Hospital is good, our study found interception rate 3％, acceptance rate 79.7％, One patient can benefit from the CDSS in every 1.5 patients. We need to guide new physicians to use「specific dose order」、「refill」 to prescribe some type of medication, and also promote physicians to value the CDSS.

致謝 i
中文摘要 ii
Abstract v
目錄 ix
圖目錄 xiii
表目錄 xv
第1章. 緒論 1
1.1. 研究背景 1
1.1.1. 電腦化醫囑開方系統（computerized physician order entry, CPOE）與臨床支援決策系統（clinical decision support system, CDSS） 1
1.1.2.臺大醫院住院開方系統 2
1.2. 研究目的 2
第2章. 文獻探討 3
2.1. 藥品相關問題（drug related problems） 3
2.1.1.用藥疏失（medication error） 5
2.1.2.藥品不良反應（adverse drug reaction, ADR） 5
2.1.3.藥品不良事件（adverse drug event, ADE） 5
2.1.4.藥品相關問題、用藥疏失、藥品不良反應與藥品不良事件彼此間的關係 5
2.2. 用藥疏失 7
2.2.1.分類 7
2.2.2.開方失誤（prescribing error） 8
2.3. 應用醫療資訊科技減少用藥疏失 9
2.3.1.臨床決策支援系統相關研究 10
2.3.2.重複用藥的警示系統相關研究 11
2.3.3.臨床決策支援系統的經濟效益 11
2.4. 國內相關文獻 12
第3章. 研究方法 13
3.1. 研究設計 13
3.2. 臨床決策支援系統（CDSS）的開發 13
3.2.1.CDSS的開發團隊 13
3.2.2.重複用藥（duplicated medication）檢核 13
3.2.3.警示畫面呈現 15
3.3. 資料擷取 16
3.4. CDSS成效評估 17
3.4.1.研究材料 17
3.4.2.研究期間 17
3.4.3.名詞定義 17
3.5. 重複用藥實際併用處方資料分析 23
3.6. CDSS及藥師線上覆核對於藥師之影響 23
3.6.1.研究期間 23
3.6.2.名詞定義 25
3.6.3.研究方法 27
3.6.4.重複用藥檢核CDSS相對應的開方跡近錯誤 27
3.7. 資料處理與分析 28
3.7.1.統計分析軟體 28
3.7.2.統計分析方法 28
第4章. 研究結果 29
4.1. 檢核資料庫 29
4.2. 攔截資料分析 29
4.2.1.CDSS處方攔截率 30
4.2.2.開方端對臨床支援系統之接受度 30
4.2.3.每多少病人有一人因此受益 36
4.2.4.可能節省藥費 37
4.3. 實際併用處方分析 37
4.4. 住院開方跡近錯誤通報系統內的處方分析 67
4.4.1.不同時期開方跡近錯誤處方數 67
4.4.2.依調劑單位別 69
4.4.3.依開方科部別 73
4.4.4.依開方跡近錯誤原因別 80
4.4.5.開方跡近錯誤原因為CDSS檢核功能相對應的開方跡近錯誤原因 89
第5章. 研究討論 95
5.1. 重複用藥檢核之CDSS成效 95
5.2. 可能節省藥價 96
5.3. 重複用藥檢核CDSS接受率 96
5.4. 不同時期下，開方跡近錯誤之變化 98
5.5. 研究限制 101
5.6. 未來研究方向 103
第6章. 結論與建議 105
第7章. 參考文獻 108
第8章. 附錄-臨床試驗計畫書 111

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1.Institute of Medicine. To Err is Human. Building a Safer Health System. , in National Academies Press. 1999: Washington, DC. p. 1-8.
2.Leape LL.Brennan TA, L.N., et al, The nature of adverse events in hospitalized patients.Results of the Harvard Medical Practice Study Ⅱ. N Eng J Med, 1991. 324: p. 388-84.
3.Brennan TA, L.L., Laird NM,et al, Incidence of adverse events and negligence in hospitalized patients.Results of the Harvard Medical Practice StudyⅠ. N Eng J Med, 1991. 324(370-6).
4.Bates, D., Boyle DL,Vander Vliet MB,Schneider J,Leape L., Relationship between medication errors and adverse drug events. J Gen Intern Med 1995. 10: p. 199-205.
5.Bates, D.W., et al., Incidence of adverse drug events and potential adverse drug events. Implications for prevention. ADE Prevention Study Group. JAMA, 1995. 274(1): p. 29-34.
6.Sellers, J.A., Too many medication errors, not enough pharmacists. Am J Health Syst Pharm, 2000. 57(4): p. 337.
7.Bobb, A., et al., The epidemiology of prescribing errors: the potential impact of computerized prescriber order entry. Arch Intern Med, 2004. 164(7): p. 785-92.
8.Igboechi CA, N.C., Yang CS, Buckner AN., Impact of computerized prescriber order entry on medication errors at an acute tertiary care hospital. Hosp Pharm, 2003. 38: p. 227-31.
9.R.Shah, N., Improving Acceptance of Computerized Prescribing Alerts in Ambulatory Care. Journal of the American Medical Informatics Association, 2006. 13(1).
10.Hepler CD, S.L., Opportunities and responsibilities in pharmaceutical care. Am J Hosp Pharm, 1990. 47: p. 533-43.
11.van der Bemt PM, E.T., de Jong-van den Berg LT, Brouwers JR, Drug-related problem in hospitalized patients. Drug saf, 2000. 22: p. 321-33.
12.WORKSHOP, P.W.S.O.D. DEFINING DRP AND MEDICATION ERRORS AND THEIR RELATIONSHIP. Geneva.
13.What is a medication Error? National Coordinating Council for Medication Error Reporting and Prevention.(Accessed Dec 16,2008,at http://www.nccmerp.org/aboutMedErrors.html).
14.Leape, L.L., Preventing adverse drug events. Am J Health Syst Pharm, 1995. 52(4): p. 379-82.
15.Johnson, J.A. and J.L. Bootman, Drug-related morbidity and mortality. A cost-of-illness model. Arch Intern Med, 1995. 155(18): p. 1949-56.
16.ASHP guidelines on preventing medication errors in hospitals. . Am J Hosp Pharm. 1993;50:305-14.
17.Lewis, P.J., et al., Prevalence, incidence and nature of prescribing errors in hospital inpatients: a systematic review. Drug saf, 2009. 32(5): p. 379-89.
18.Dean Franklin, B., et al., The incidence of prescribing errors in hospital inpatients: an overview of the research methods. Drug saf, 2005. 28(10): p. 891-900.
19.Krahenbuhl-Melcher, A., et al., Drug-related problems in hospitals: a review of the recent literature. Drug saf, 2007. 30(5): p. 379-407.
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