Dataset Information

A probabilistic model for reducing medication errors.

ABSTRACT:

Background

Medication errors are common, life threatening, costly but preventable. Information technology and automated systems are highly efficient for preventing medication errors and therefore widely employed in hospital settings. The aim of this study was to construct a probabilistic model that can reduce medication errors by identifying uncommon or rare associations between medications and diseases.

Methods and findings

Association rules of mining techniques are utilized for 103.5 million prescriptions from 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 were being referred as Q values. The DMQs and MMQs were used to develop the AOP model to predict 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.

Conclusions

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 AOP model helps to reduce medication errors by alerting physicians, improving the patients' safety and the overall quality of care.

SUBMITTER: Nguyen PA

PROVIDER: S-EPMC3849453 | biostudies-literature | 2013

REPOSITORIES: biostudies-literature

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Publications

A probabilistic model for reducing medication errors.

Nguyen Phung Anh PA Syed-Abdul Shabbir S Iqbal Usman U Hsu Min-Huei MH Huang Chen-Ling CL Li Hsien-Chang HC Clinciu Daniel Livius DL Jian Wen-Shan WS Li Yu-Chuan Jack YC

PloS one 20131203 12

<h4>Background</h4>Medication errors are common, life threatening, costly but preventable. Information technology and automated systems are highly efficient for preventing medication errors and therefore widely employed in hospital settings. The aim of this study was to construct a probabilistic model that can reduce medication errors by identifying uncommon or rare associations between medications and diseases.<h4>Methods and findings</h4>Association rules of mining techniques are utilized for ...[more]

PMID: 24312659

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Project description:ObjectivesThis study aimed to evaluate a biometric palm vein authentication system to prevent medication administration errors in psychiatric hospitals.DesignThis is a pre-post observational study.SettingConventionally, the medication was distributed after a double check. We developed and introduced a new medication administration cart in two psychiatric hospitals in Japan, in which each patient-specific drug box had to be electronically opened only by palm vein authentication.ParticipantsA total of 3444 and 3523 patients were present 18 months before and after introducing the cart, respectively. Of the 212 nurses recruited, 28 were excluded due to a lack of experience with the conventional medication administration system and incomplete questionnaires.Primary and secondary outcome measuresThe primary outcome was the efficacy of this system by comparing the incidence of medication administration errors before and after introducing the cart. The secondary outcome was a survey regarding nurses' attitudes toward this system.ResultsAfter introduction of the new system, the number of medication errors due to misidentification of persons relative to the total number of admitted patients was significantly reduced from 6/3444 to 2/3523 (p<0.0001). Among 184 nurses, 182 responded that anxiety regarding administration errors was either reduced or unchanged using this system. Male nurses reported a greater increase in work burden than female nurses (OR=3.11, 95% CI=1.44 to 6.72). Nurses working in chronic care wards reported greater time pressure than nurses working in emergency wards (OR=3.33, 95% CI=1.16 to 9.57). Nurses working in dementia care wards reported a greater patient care burden than nurses working in emergency wards (OR=5.67, 95% CI=1.22 to 26.27).ConclusionsThis new system might have potential for reducing the patient misidentification risk during medication without increasing the anxiety experienced by nurses concerning administration errors. However, system usability and efficiency must be improved to reduce additional work burden, time pressure and patient care burden.

Project description:OBJECTIVE:Clinicians using clinical decision support (CDS) to prescribe medications have an obligation to ensure that prescriptions are safe. One option is to verify the safety of prescriptions if there is uncertainty, for example, by using drug references. Supervisory control experiments in aviation and process control have associated errors, with reduced verification arising from overreliance on decision support. However, it is unknown whether this relationship extends to clinical decision-making. Therefore, we examine whether there is a relationship between verification behaviors and prescribing errors, with and without CDS medication alerts, and whether task complexity mediates this. METHODS:A total of 120 students in the final 2 years of a medical degree prescribed medicines for patient scenarios using a simulated electronic prescribing system. CDS (correct, incorrect, and no CDS) and task complexity (low and high) were varied. Outcomes were omission (missed prescribing errors) and commission errors (accepted false-positive alerts). Verification measures were access of drug references and view time percentage of task time. RESULTS:Failure to access references for medicines with prescribing errors increased omission errors with no CDS (high-complexity: χ 2(1) = 12.716; p < 0.001) and incorrect CDS (Fisher's exact; low-complexity: p = 0.002; high-complexity: p = 0.001). Failure to access references for false-positive alerts increased commission errors (low-complexity: χ 2(1) = 16.673, p < 0.001; high-complexity: χ 2(1) = 18.690, p < 0.001). Fewer participants accessed relevant references with incorrect CDS compared with no CDS (McNemar; low-complexity: p < 0.001; high-complexity: p < 0.001). Lower view time percentages increased omission (F(3, 361.914) = 4.498; p = 0.035) and commission errors (F(1, 346.223) = 2.712; p = 0.045). View time percentages were lower in CDS-assisted conditions compared with unassisted conditions (F(2, 335.743) = 10.443; p < 0.001). DISCUSSION:The presence of CDS reduced verification of prescription safety. When CDS was incorrect, reduced verification was associated with increased prescribing errors. CONCLUSION:CDS can be incorrect, and verification provides one mechanism to detect errors. System designers need to facilitate verification without increasing workload or eliminating the benefits of correct CDS.

Dataset Information

A probabilistic model for reducing medication errors.

Background

Methods and findings

Conclusions

Publications

A probabilistic model for reducing medication errors.

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