As previously published, we confirm that our hybrid clinical decision support system can improve pharmacists efficiency in everyday practice by prioritizing high risk prescriptions [9]. In this study, we assessed the ability of this innovative tool to rule out low score prescriptions in daily practice without taking the risk of excluding prescriptions containing serious prescription errors and therefore allowing clinical pharmacists to focus their medication review activity on a reduced number of inpatients prescriptions. As expected, we found a dramatic decrease of pharmaceutical interventions needed for the low score prescriptions when compared to the high score prescriptions.
It is commonly known that pharmaceutical medication review remains complex, as it requires predefined rules that can also be adaptable to associated prescribed drugs and patient’s biological and physiological characteristics. Classic CDS systems, which are widely implemented in electronic patient records, are only intended to improve medication review by detecting potential prescription errors such as drug-drug interactions and dosage outliers.
However, designing a tool that could replicate human performance in pharmaceutical expertise remains challenging. An algorithm using configurable rule-based engines is a rational approach but remains difficult to create due to the many rules that would have to be implemented in the tool. The use of Artificial Intelligence (AI) could be a solution to that issue. Nevertheless, this approach cannot be exclusively relaying on AI, as many severe medication errors remains exceptional ones, therefore harder for an AI to integrate in their engines. To address this issue, we chose to create a hybrid tool using AI and a rule-based system to be able to detect occasional but severe medication errors. This tool can therefore classify prescriptions according to the risk of medication errors, which allows the pharmacist to prioritize the most at-risk prescriptions to review. We could argue that this prioritization could lead to incomplete medication review of the hospital prescriptions. However, it is important to note that this prioritization allows the selection of the most at-risk prescriptions and offer the possibility to better target the pharmaceutical expertise on the most at-risk prescriptions, which is particularly important when the medication review cannot be exhaustive.
This type of tools must constantly evolve and keep up to date with the recommended practice but also in regard to clinical feedbacks about the errors detected or not by the tool. Our study falls within that perspective by ensuring that low score prescriptions, removed from the daily and systematic medication review process, are risk-free of severe medication errors.
In our study, 36 prescriptions, including 6 low score prescriptions, had a PI concerning missing or modified treatments during hospitalization compared to the patient’s usual treatments or untreated indications related to the patient’s medical history that could not have been detected by the digital tool. When taking into account their clinical impact, only 3 severe medication errors were found in the low score prescriptions, including 2 medication error that could not have been detected by the tool in its current settings, versus 50 severe medication errors in the high score group (p < 0.05). These non-intercepted errors allowed us to identify missing data in the score calculation, such as anti-Xa activity and natural language processing which could have allowed the tool to intercept unadapted antibiotic dosage. In that regard and despite showing satisfactory sensitivity and precision, some improvements of the digital hybrid tool are yet to be integrated, such as the addition of biological values in the score calculation (anti-Xa activity for example) and the integration of natural language processing. When adding this last feature, the tool will also be able to detect an untreated indication for example.
In addition to the ability of this hybrid digital tool to detect potentially at-risk prescriptions, another important advantage is its capacity to better alert pharmacists about prescriptions with potential severe medication errors compared to classic CDS system. When comparing this hybrid tool to a CDS system, we found that more prescriptions with at least one severe medication errors were detected by the hybrid tool. Indeed, only drug-drug interactions and over/under dosage within the range of drug labels are detected by a CDSS. The hybrid tool has therefore the capacity to detect other type of medication errors, as for example medication errors related to patients’ clinical and biological characteristics. This major difference between these tools confirms that CDSS are not able to prioritize medication review the way the hybrid tool does.
Our goal was to improve pharmaceutical efficiency to provide a more secure approach for hospitalized patients’ care. Indeed, in France, a healthcare facility employs 2.8 pharmacists on average, and 1.7 pharmacists per 100 hospital beds [13, 14]. As pharmacists are in charge of many activities, only a low percentage is dedicated to clinical pharmacy activities.
By using this tool in daily clinical pharmacy practice, we found that 55% of all medication orders for hospitalized patients could be ruled out from medication review by clinical pharmacists with an acceptable risk of missing prescriptions with severe medication errors, as exhaustive medication review could not be performed in this hospital anyway. These findings could allow clinical pharmacists to reinforce other activities, such as medication reconciliation or patient education.