臺灣博碩士論文加值系統

近年來資訊科技及網際網路的創新和普及化，已有愈來愈多的醫療機構導入資訊科技（Health Information Technology, HIT）作為提升工作效率、改善醫療作業流程、協助醫護人員減少人為錯誤、提升照護品質、降低醫院成本支出及減少不必要之醫療資源浪費之有效的系統化輔助。最常見的資訊科技介入為電子病歷（Electronic Health Records, EHR）和醫囑資訊系統（Computerized Physician Order Entry, CPOE），其產生出來的資料量以日俱增，而有意義的統計分析這些巨量資料(big data)將更有助於醫療人員從事臨床醫學研究、促進民眾預防保健與疾病治療、加速研究者探索生物醫學領域之知識，更能提供政策制定者有用的資訊以提升全民醫療品質。

然而，用藥錯誤在醫療院所中並不罕見，其影響不僅會造成巨大的成本耗費，嚴重時更會危害病人生命安全，但事實上許多錯誤是可以防範未然的。過去資訊科技與自動化系統運用規則模式(rule-based) 與確定性模型 (deterministic models) 有效預防用藥錯誤，而現在則傾向以機率模式 (probabilistic) 代表其疾病、治療、處置等於病人就診紀錄中出現的隨機性。

因此，本研究之目的為應用台灣全民健康保險資料庫(2002年)共一億零三百五十萬次醫師開立之處方(其中包含ICD9-CM編碼的兩億零四百五十萬個診斷和使用ATC編碼的三億四千七百七十萬種藥物)，利用資料探勘之關聯規則(Association Rule)建立一個可以藉由辨識藥物和疾病之間的少見之關聯性，降低同一處方中之用藥不適當之機率模型。

疾病與藥物（Disease-Medication, DM）和藥物與藥物（Medication-Medication, MM）其共同出現的次數與關聯性的強度之吸引力數值，我們稱之為Q值。吾人利用DMQs和MMQ在同一次就診處方上的數量及組合，建立出處方適當模型 (Appropriateness of Prescription, AOP) 自動偵測醫師開立之處方是否合適。本研究模型之評估為比較執行AOP模式與醫學專家驗證之結果，其適當的處方精確度(Accuracy)為96%，不適當的處方精確度為45％，敏感度(Sensitivity)和特異性(Specificity)分別為75.9％和89.5％。本研究亦於臺北醫學大學附設醫院內科部進行一個月的先導研究，將AOP模型與該部門之CPOE系統連接，測試結果發現在1,056次處方中，僅8次觸發系統提醒，而其中37.5%被正確地偵測為不適當的處方。

本研究成功的建立了一個有效率之以機率模型自動辨識少見的疾病與藥物、藥物與藥物的AOP工具，未來能繼續應用AOP模型協助提醒醫生開立藥物不適當性、減少用藥錯誤，進而提升病人安全和整體醫療照護品質。

Health information technology (HIT) is the area where health care experts, policymakers, payers, and consumers consider health information technologies, such as electronic health records (EHR) and computerized physician order entry (CPOE), to be critical to transforming the health care industry. Due to the rapid pace of change in technology that had dramatically increased health data accumulated by the health providers. The allure of big data and healthcare analytics is that they can unlock the potential of this information by extracting, analyzing, and making it meaningful to healthcare organizations and providers in biomedical science’s field.

Furthermore, medication errors are common, life threatening, costly but preventable. Information technology and automated systems are highly efficient for preventing medication errors by applying “rule-based” methods and deterministic models. However, a probabilistic approach is preferred to represent the uncertainty of patients’ response to treatment in health care population. Therefore, this study was to construct a probabilistic model that can reduce medication errors by identifying uncommon or rare associations between medications and diseases.

The association rules of mining techniques are utilized for 103.5 million prescriptions from 2002 Taiwan’s National Health Insurance database. The dataset included 204.5 million diagnoses with ICD9-CM codes and 347.7 million medications by using ATC codes. Disease-Medication (DM) and Medication-Medication (MM) associations were computed by their co-occurrence and associations’ strength were measured by the interestingness or lift values which being referred as Q values. By considering the number and combination of DMQs and MMQs in one prescription, we developed the Appropriateness of Prescription (AOP) model that can determine the appropriateness of a given prescription. Validation of this model was done by comparing the results of evaluation performed by the AOP model and verified by human experts. The results showed 96% accuracy for appropriate and 45% accuracy for inappropriate prescriptions, with a sensitivity and specificity of 75.9% and 89.5%, respectively. Moreover, the AOP model was implemented in CPOE system at Internal Medicine Department of Taipei Medical University Hospital (TMUH) as a pilot study. Out of 1,056 prescriptions, 8 reminders were triggered, in which 37.5% of prescriptions were considered correctly classified as “inappropriate”.

Thus, we successfully developed the AOP model as an efficient tool for automatic identification of uncommon or rare associations between disease-medication and medication-medication in prescriptions. The future work consists of applying the AOP model in clinical trial and further in clinical practice to aid in improving patients safety and quality of care as well as to enable better decision support and quality measurement.

Acknowledgement ii
Chinese Abstract iii
Abstract v
Content vii
Figures Content xi
Tables Content xii
Chapter 1 Introduction 1
1.1 Background 1
Health information technology (HIT) 1
Big Data 2
The knowledge discovery in database (KDD) 3
Data interaction 4
1.2 Related work and motivation 6
1.3 The goal of this study and Research contribution 9
1.4 Structure of the dissertation 9
Chapter 2 Literature Review 12
2.1 Medication errors and clinical problems 13
2.2 Rule based methods - Deterministic models vs. Probabilistic models 14
2.3 Association rules discovery 17
Frequent item-set mining 18
An efficient algorithm 20
Measure of interestingness 22
Chapter 3 A Probabilistic Model 23
3.1 Principles 24
3.2 Phase (I): Data gathering and cleaning 25
3.3 Phase (II): Creating Disease-Medication (DM) and Medication-Medication (MM) database 25
The assumption of “Disease-Medication/Medication-Medication association” in this study 25
Quantify the Associations’ strength (Q values) 26
Creating Disease-Medication and Medication-Medication database 30
Validating the associations’ strength of Database 34
3.4 Phase (III): Developing an Appropriateness of Prescription (AOP) model 37
3.5 Phase (IV): To test and evaluate the AOP model 39
Step 1: To test AOP model (Training) 40
Step 2: The evaluation of AOP model by human experts 41
Chapter 4 Results 42
4.1 Results of Step 1 42
4.2 Results of Step 2 42
Chapter 5 Application and Implementation 48
5.1 The scope of the AOP model 48
5.2 Reminder system’s description 49
DM and MM association database (HOSDB) 49
Web-service 50
5.3 A pilot study 53
Chapter 6 Discussion 54
6.1 Bureau of National Health Insurance 54
6.2 Q values as a measure for strength of associations 55
6.3 NHI database (DM and MM database) and validation of the database 59
6.4 The Appropriateness of Prescription (AOP) model 59
6.5 Unexpected Positive Associations 60
6.6 Alert Fatigue 61
6.7 Analysis 62
6.8 Comparison to other techniques 63
6.9 Other limitations 64
6.10 Future works 66
Chapter 7 Conclusion 68
References 69
Appendix 78
S1. An example of prescriptions in raw data 78
S2. Brief Introduction before administering questionnaires 79
S3. Description of questionnaires that used to evaluate the AOP model 80
S4. Apriori algorithm 81
S5. Top 20 positive disease-medication association under Q 82
S6. Top 20 negative disease-medication association under Q 83
S7. Publication 84

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