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Background: Bedside clinical pharmacy prevents drug-related problems, but is not feasible in many countries due to limited resources. Hence, clinical rules using structural information in the electronic health record can help identifying potentially inappropriate prescriptions (PIPs). We aimed to develop and implement a risk-based clinical pharmacy service and evaluate its impact on prescribing at the trauma surgery ward. Methods: The proportion of residual PIPs per day, i.e. the number of PIPs that persisted up to 24 h after pharmacist intervention divided by the number of PIPs at T0, was evaluated before and after implementation of the intervention in an interrupted time series analysis. The pre-intervention cohort received usual pharmacy services, i.e. a 0.3 FTE clinical pharmacist trainee. Fifteen clinical rules, targeting antimicrobial, anticoagulant and analgesic therapy were implemented in the post-intervention period. The pre-intervention period was compared to two post-intervention scenarios: A) clinical rule alerts reviewed by a 0.3 FTE clinical pharmacist trainee; and B) clinical rule alerts reviewed daily for approximately 1 h by a clinical pharmacist trainee. Results: Pre-intervention, a median proportion of 67% (range 0%−100%) residual PIPs per day was observed. Scenario A showed an immediate relative reduction of 14% (p = 0.72) and scenario B a significant immediate relative reduction of 85% (p = 0.0015) in residual PIPs per day. In scenario A, recommendations were provided for 19% of clinical rule alerts, of which 67% was accepted by the surgeon within 24 h. In scenario B, recommendations were given for 56% of alerts, of which 84% was accepted. Conclusions: Using clinical rules is an effective approach to organize bedside clinical pharmacy services and improves prescribing at the trauma surgery ward. Advanced training and daily follow-up of the clinical rules are two requirements to be considered.

Background: As the application of Artificial Intelligence (AI) technologies increases in the healthcare sector, the industry faces a need to combine medical knowledge, often expressed as clinical rules, with advances in machine learning (ML), which offer high prediction accuracy at the expense of transparency of decision making., Purpose: This paper seeks to review the present literature, identify hybrid architecture patterns that incorporate rules and machine learning, and evaluate the rationale behind their selection to inform future development and research on the design of transparent and precise clinical decision systems., Methods: PubMed, IEEE Explore, and Google Scholar were queried in search for papers from 1992 to 2022, with the keywords: "clinical decision system", "hybrid clinical architecture", "machine learning and clinical rules". Excluded articles did not use both ML and rules or did not provide any explanation of employed architecture. A proposed taxonomy was used to organize the results, analyze them, and depict them in graphical and tabular form. Two researchers, one with expertise in rule-based systems and another in ML, reviewed identified papers and discussed the work to minimize bias, and the third one re-reviewed the work to ensure consistency of reporting., Results: The authors screened 957 papers and reviewed 71 that met their criteria. Five distinct architecture archetypes were determined: Rules are Embedded in ML architecture (REML) (most used), ML pre-processes input data for Rule-Based inference (MLRB), Rule-Based method pre-processes input data for ML prediction (RBML), Rules influence ML training (RMLT), Parallel Ensemble of Rules and ML (PERML), which was rarely observed in clinical contexts., Conclusions: Most architectures in the reviewed literature prioritize prediction accuracy over explainability and trustworthiness, which has led to more complex embedded approaches. Alternatively, parallel (PERML) architectures may be employed, allowing for a more transparent system that is easier to explain to patients and clinicians. The potential of this approach warrants further research., Other: A limitation of the study may be that it reviews scientific literature, while algorithms implemented in clinical practice may present different distributions of motivations and implementations of hybrid architectures., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Author(s). Published by Elsevier Inc. All rights reserved.)