TY - JOUR
T1 - A probabilistic model for reducing medication errors
T2 - A sensitivity analysis using Electronic Health Records data
AU - Huang, Chu Ya
AU - Nguyen, Phung Anh
AU - Yang, Hsuan Chia
AU - Islam, Md Mohaimenul
AU - Liang, Chia Wei
AU - Lee, Fei Peng
AU - (Jack) Li, Yu Chuan
N1 - Funding Information:
This research was sponsored in part by Ministry of Science and Technology (MOST) 107-2634-F-038-002 -, and Ministry of Science and Technology (MOST) 106-2634-F-038 -001 -CC2 .
Funding Information:
This research was sponsored in part by Ministry of Science and Technology (MOST) 107-2634-F-038-002-, and Ministry of Science and Technology (MOST) 106-2634-F-038 -001 -CC2.
Publisher Copyright:
© 2018
PY - 2019/3
Y1 - 2019/3
N2 - Objectives: Medication-related clinical decision support systems have already been considered as a sophisticated method to improve healthcare quality, however, its importance has not been fully recognized. This paper's aim was to validate an existing probabilistic model that can automatically identify medication errors by performing a sensitivity analysis from electronic medical record data. Methods: We first built a knowledge base that consisted of 2.22 million disease-medication (DM) and 0.78 million medication-medication (MM) associations using Taiwan Health and Welfare data science claims data between January 1st, 2009 and December 31st, 2011. Further, we collected 0.6 million outpatient visit prescriptions from six departments across five different medical centers/hospitals. Afterward, we employed the data to our AESOP model and validated it using a sensitivity analysis of 11 various thresholds (α = [0.5; 1.5]) that were used to identify positive DM and MM associations. We randomly selected 2400 randomly prescriptions and compared them to the gold standard of 18 physicians’ manual review for appropriateness. Results: One hundred twenty-one results of 2400 prescriptions with various thresholds were tested by the AESOP model. Validation against the gold standard showed a high accuracy (over 80%), sensitivity (80–96%), and positive predictive value (over 85%). The negative predictive values ranged from 45 to 75% across three departments, cardiology, neurology, and ophthalmology. Conclusion: We performed a sensitivity analysis and validated the AESOP model in different hospitals. Thus, picking the optimal threshold of the model depended on balancing false negatives with false positives and depending on the specialty and the purpose of the system.
AB - Objectives: Medication-related clinical decision support systems have already been considered as a sophisticated method to improve healthcare quality, however, its importance has not been fully recognized. This paper's aim was to validate an existing probabilistic model that can automatically identify medication errors by performing a sensitivity analysis from electronic medical record data. Methods: We first built a knowledge base that consisted of 2.22 million disease-medication (DM) and 0.78 million medication-medication (MM) associations using Taiwan Health and Welfare data science claims data between January 1st, 2009 and December 31st, 2011. Further, we collected 0.6 million outpatient visit prescriptions from six departments across five different medical centers/hospitals. Afterward, we employed the data to our AESOP model and validated it using a sensitivity analysis of 11 various thresholds (α = [0.5; 1.5]) that were used to identify positive DM and MM associations. We randomly selected 2400 randomly prescriptions and compared them to the gold standard of 18 physicians’ manual review for appropriateness. Results: One hundred twenty-one results of 2400 prescriptions with various thresholds were tested by the AESOP model. Validation against the gold standard showed a high accuracy (over 80%), sensitivity (80–96%), and positive predictive value (over 85%). The negative predictive values ranged from 45 to 75% across three departments, cardiology, neurology, and ophthalmology. Conclusion: We performed a sensitivity analysis and validated the AESOP model in different hospitals. Thus, picking the optimal threshold of the model depended on balancing false negatives with false positives and depending on the specialty and the purpose of the system.
KW - AESOP
KW - EHR
KW - Medication errors
KW - Probabilistic model
KW - Sensitivity analysis
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U2 - 10.1016/j.cmpb.2018.12.033
DO - 10.1016/j.cmpb.2018.12.033
M3 - Article
C2 - 30712602
AN - SCOPUS:85059938813
SN - 0169-2607
VL - 170
SP - 31
EP - 38
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
ER -