Abstract

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.

Original languageEnglish
Pages (from-to)31-38
Number of pages8
JournalComputer Methods and Programs in Biomedicine
Volume170
DOIs
Publication statusPublished - Mar 1 2019

Fingerprint

Medication Errors
Electronic Health Records
Statistical Models
Sensitivity analysis
Prescriptions
Health
Regional Health Planning
Clinical Decision Support Systems
Knowledge Bases
Quality of Health Care
Ophthalmology
Neurology
Cardiology
Taiwan
Electronic medical equipment
Outpatients
Decision support systems
Physicians

Keywords

  • AESOP
  • EHR
  • Medication errors
  • Probabilistic model
  • Sensitivity analysis

ASJC Scopus subject areas

  • Software
  • Computer Science Applications
  • Health Informatics

Cite this

A probabilistic model for reducing medication errors : A sensitivity analysis using Electronic Health Records data. / Huang, Chu Ya; Nguyen, Phung Anh; Yang, Hsuan Chia; Islam, Md Mohaimenul; Liang, Chia Wei; Lee, Fei Peng; (Jack) Li, Yu Chuan.

In: Computer Methods and Programs in Biomedicine, Vol. 170, 01.03.2019, p. 31-38.

Research output: Contribution to journalArticle

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abstract = "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.",
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AU - Yang, Hsuan Chia

AU - Islam, Md Mohaimenul

AU - Liang, Chia Wei

AU - Lee, Fei Peng

AU - (Jack) Li, Yu Chuan

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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.

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