肺結核用藥異常檢核決策支援系統 A Decision Support System for Screening Prescription Errors in Treatment for Pulmonary Tuberculosis

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肺結核用藥異常檢核決策支援系統

A Decision Support System for Screening Prescription Errors in Treatment for Pulmonary Tuberculosis

中文摘要

疾病管制局在幾次抽查結核病用藥處方的計畫中,發現不正確或是不依循現行結 核病治療指引處方的比例並不低,因結核病治療指引內容較為複雜,衛生所公衛 護士或是結核病承辦人在評估處方的適切性時,亦不易僅靠閱讀指引即可完成。

緣此,本研究目的在依用藥指引建立一結核病用藥處方異常之電腦決策支援系 統,以方便查核工作的進行。

在本研究中,我們採用疾管局於 2008 年出版之結核病診治指引第三版,開發了 一個針對公衛人員進行處方異常查核使用之 Web-based 的決策支援系統。透過知 識工程的方法,先將標準化的處方指引,轉化成決策表及 IF-THEN 邏輯,同時 設計一輸入介面以便取得分析所需參數,輸入完畢,使用者即可取得決策支援系 統的處方異常分析結果。系統也內含一警示模組,可將嚴重錯誤的處方即時回報 給相關人員。

我們使用 18 本特意挑選之健保抽審後疑似有問題之住院病例,共 62 個處方以評 估系統的表現,合計開立 200 種不同的藥。本系統找出 48%的藥物組合、21%的 藥物劑量及 6%的藥物頻次的異常,其中,藥物組合檢核的敏感度為 0.93,特異 度為 0.59;藥物劑量檢核的敏感度為 0.95,特異度為 0.91,藥物頻次的檢核的敏 感度為 1.00,特異度為 0.98。

因本用藥查核系統具有高敏感度,對結核病防治公衛人員在進行處方異常查核工 作將是一有用的輔助工具,而藥物組合檢核特異度偏低的情形,因所選擇抽樣病 歷之處方多於特殊情況時開立,將可在補足特殊情況使用之開藥規則後改善。本 決策系統著眼於處方異常的查核,並希望經由本系統減少非標準的處方,能達到 增加結核病的治癒率,並提昇病人安全。

英文摘要

High prevalence of prescription errors in treatment of pulmonary tuberculosis (TB) in Taiwan has been reported in previous studies. Due to the complexity of treatment guideline for TB, frontline health workers for TB control often find difficulties in determining the appropriateness of anti-TB regimens. The aim of this study was to build a guideline-based computerized decision support system (DSS) for screening prescription errors in anti-TB regimens.

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To facilitate the screening work for prescription errors, we’ve created a web-based DSS implementing the paper-based “Taiwan Guidelines for TB Diagnosis &

Treatment, 3rd edition”. By means of knowledge engineering, standardized

prescription guidelines were transformed into computerized decision table and if-then rules. An ease-to-use user interface was designed for collecting required parameters.

With a single click, users are allowed to obtain the analytic results by the DSS immediately. An alert module for reporting severe or harmful errors to relevant medical officers was also embedded.

For system performance test, we used 62 regimens with 200 different drugs

prescribed from 18 pre-selected in-patient medical charts. This DSS detected errors in 48% of drug combination, 21% in dosages and 6% in frequencies. Sensitivity and specificity were, respectively, 0.93 and 0.59 in combination check, 0.95 and 0.91 in dosage check, and 1.00 and 0.98 in frequency check.

With the characteristics of high sensitivity of this DSS, frontline health workers for TB control may find it useful as a screening tool for anti-TB prescription errors. The low specificity in drug combination check can be improved after amending rules used in special considerations. Aside from the purpose of surveillance for non-standardized prescriptions, this system may have the potential for increasing TB treatment success rate and improving patient safety by improving the compliance to TB treatment guidelines.

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