A machine learning–based clinical decision support system to identify prescriptions with a high risk of medication error

Abstract

Objective To improve patient safety and clinical outcomes by reducing the risk of prescribing errors, we tested the accuracy of a hybrid clinical decision support system in prioritizing prescription checks. Materials and Methods Data from electronic health records were collated over a period of 18 months. Inferred scores at a patient level (probability of a patient’s set of active orders to require a pharmacist review) were calculated using a hybrid approach (machine learning and a rule-based expert system). A clinical pharmacist analyzed randomly selected prescription orders over a 2-week period to corroborate our findings. Predicted scores were compared with the pharmacist’s review using the area under the receiving-operating characteristic curve and area under the precision-recall curve. These metrics were compared with existing tools: computerized alerts generated by a clinical decision support (CDS) system and a literature-based multicriteria query prioritization technique. Data from 10 716 individual patients (133 179 prescription orders) were used to train the algorithm on the basis of 25 features in a development dataset. Results While the pharmacist analyzed 412 individual patients (3364 prescription orders) in an independent validation dataset, the areas under the receiving-operating characteristic and precision-recall curves of our digital system were 0.81 and 0.75, respectively, thus demonstrating greater accuracy than the CDS system (0.65 and 0.56, respectively) and multicriteria query techniques (0.68 and 0.56, respectively). Discussion Our innovative digital tool was notably more accurate than existing techniques (CDS system and multicriteria query) at intercepting potential prescription errors. Conclusions By primarily targeting high-risk patients, this novel hybrid decision support system improved the accuracy and reliability of prescription checks in a hospital setting.

Integration of Explicit Criteria in a Clinical Decision Support System Through Evaluation of Acute Kidney Injury Events

“…The redundancy of alerts has also led to an increase in alerts and a decrease in the rate of PI, although the pharmacist analyzed actually all these alerts. Using supervised machine learning for optimization or for creation of rules demonstrated promising results in improving specificity of alerts [7,8]. During the first period, the number of alerts changed significantly, from 7 to 40 alerts to be assessed per day (all rules included).…”

Clinical Decision Support Systems and Computerized Provider Order Entry: Contributions from 2020

“…One paper describes the use of electronic health records to support a public health response to the COVID-19 pandemic with a focus on academic centers in the United States [ 5 ]. Other papers focus on the integration of Artificial Intelligence (AI) and machine learning-based with clinical decision support system to help in different clinical situations [ 6 ].…”

Can a Hybrid Decision Support System Effectively Rule Out Prescriptions from Medication Review in Daily Practice? A Randomized Case-Control Study.

“…This hybrid model using knowledge-driven (expert system) and date-driven approaches (machine learning) has shown to be an accurate tool at intercepting potential prescription errors. The accuracy of this hybrid decision support algorithm showed a sensitivity of 0.81 (95% CI, 0.78-0.84) and a precision 0.75 (95% CI, 0.70-0.80), that outperformed classic prescription order analysis tools [9]. Currently used in practice, this tool helps pharmacists to prioritize their medication review activity by distinguishing low and high risk prescriptions.…”

“…The score was related to the probability for the prescription to require a pharmaceutical intervention (PI), 10 being the higher probability of containing a medication error leading to a PI. The cut-off score of 2.4 was used to classify the prescriptions as "low risk" if strictly inferior to 2.4, or "high risk" if higher or equal to 2.4, as the result of the original research on the tool development, which showed that this cut-off score maximized the harmonic mean of precision and recall of the tool [9].…”