Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 5 appraisals
European journal of hospital pharmacy : science and practice
How to manage the overwhelming amount of database-detected interactions? A focus group meeting study on the management of database-detected risks of drug interactions.
OBJECTIVES: Database-assisted screening may help detect the risks of drug interactions, but less than 10% of flagged interactions have clinical manifestations, potentially leading to alert fatigue among healthcare providers. To address this issue, this study aimed to develop a concise framework to support the management of database-indicated interaction risks. METHODS: A grounded theory approach was used to design, evaluate and develop the framework. Participants included healthcare professionals and laypersons with experience using narrow therapeutic index medications, as well as individuals recommended by them. Two focus group discussions were conducted (N1=6 and N2=5), each lasting approximately 2 hours and following the same agenda. In one case, a follow-up one-on-one interview was held because the participant indicated further insights at the end of the session. RESULTS: Participants identified key decision-making questions for the framework, including the evidence base for interacting drugs, severity and documentation level of the interaction, availability of safer alternatives, and the presence of confounding factors. The framework was considered useful and, following refinement, potentially suitable for clinical implementation. A key insight was that integration into healthcare curricula is essential for achieving long-term impact. Overall, the proposed tool may assist in managing interaction risks in diverse clinical scenarios and reduce alert fatigue among healthcare professionals. CONCLUSION: Although further clinical validation is needed, the framework provides a foundation for improving the management of database-detected drug interactions.
26 Apr 2026
Read appraisal →American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
Evaluating medication record creation before and after an electronic health record transition
PURPOSE: In 2022, St. Jude Children's Research Hospital underwent an electronic health record (EHR) transition, migrating from Cerner Millennium to Epic. This transition required a comprehensive revision of the medication build and configuration process and, subsequently, prompted an analysis of lessons learned during the transition. Through enhancement of the medication build and configuration process, the standardized procedure increases collaboration with subject matter experts and other departments within the institution as medication records are created. SUMMARY: The transition involved stakeholder meetings with members from operational leadership, informatics, medication safety, and pharmacy inventory management to update the existing process to support the new system. Through the creation of an initial version and subsequent iterations, the process was refined to efficiently support and control the quality of medication build requests. The updated process also maintained existing oversight over the entire process. The transition highlighted differences between the original EHR and the updated EHR in the management of medication builds. The differences between the EHRs and medication build processes presented a learning curve for individuals involved in medication builds and resulted in an initial lengthening of the turnaround time. Following iterative improvements to the usability of the medication build process and enhancements in automatic communication, the turnaround time substantially decreased. CONCLUSION: The insights gleaned from the transition process underscore the importance of aligning operational processes with user needs, fostering iterative improvements, prioritizing effective communication, and maintaining a delicate balance between urgency and accuracy in managing medication record creation and configuration within EHRs. This knowledge can be used to guide the creation of a medication build process or transition from an existing process.
24 Apr 2026
Read appraisal →American journal of health-system pharmacy : AJHP : official journal of the American Society of Health-System Pharmacists
Implementation of dispense waste functionality with new electronic health record system.
PURPOSE: Practice changes were implemented and adjusted to ensure regulatory compliance with JZ modifier documentation. SUMMARY: The JW modifier allows institutions to bill for partially unused and discarded single-dose containers of drugs and biologics, whereas the JZ modifier is used to report that no amount of drug was discarded from a single-dose container. These modifiers are essential for accurate coding and resource stewardship, especially in pediatric hospitals, where medication doses are often patient specific and require manipulation of single-dose containers. Starting July 1, 2023, the Centers for Medicare & Medicaid Services required providers to report the JZ modifier on all claims that bill for drugs from single-dose containers. Our institution underwent a soft go-live in June 2023, which allowed the institution to make necessary process changes to ensure regulatory compliance. In June 2023, only 31.1% of waste was documented at the time of medication preparation. To capture waste documentation post preparation, a pharmacy technician was trained to review missed opportunities and document waste retrospectively. Monthly monitoring of waste documentation, including missed opportunities during drug preparation, was conducted and further staff education was provided. A reassessment of waste documentation was completed in February 2024, which highlighted that real-time waste documentation completed during compounding had increased to 85.9%. By documenting waste during drug preparation, the resources spent reviewing reports for drug preparations from the previous day and the need to document waste are reduced. CONCLUSION: Through process implementation and staff education, compliance with real-time waste documentation during drug preparation increased from 31.1% to 85.9% (P < 0.001) in 8 months.
23 Apr 2026
Read appraisal →Journal of medical Internet research
Beyond GPT-4: The Rapidly Evolving Potential of Large Language Models for Clinical Guideline Improvement
This commentary reviews the study by Jones et al, which evaluated whether GPT-4 could improve the readability of injectable medication guidelines while preserving important safety information. The study found that GPT-4 produced modest readability gains comparable to manual revision, but also introduced omissions and meaning changes in a minority of sections. These findings highlight both the potential and limitations of early large language models (LLMs) in clinical contexts. However, this study reflects the capabilities of a specific model in a rapidly evolving domain. Since the release of GPT-4, advances in multistep reasoning, model-critique workflows, and structured validation have substantially improved the ability of newer systems to detect omissions, maintain factual fidelity, and support controlled editing. As a result, some documented limitations may stem from the constraints of a single-model, single-pass workflow rather than intrinsic flaws in LLM-assisted guideline revision. This commentary highlights the need for evaluation frameworks that can keep pace with LLM progress and emphasizes that clinical oversight and user-centered testing remain essential. Updated research using contemporary models is needed to determine how emerging architectures can more safely support clarity, consistency, and maintenance of clinical guidelines.
12 Apr 2026
Read appraisal →JMIR medical informatics
Code-Based Versus AutoML Methods for Pill Recognition in Clinical Settings: Comparative Performance Study
BACKGROUND: Visual identification and verification of medications during dispensing and administration are prone to human error, particularly in high-pressure and high-volume clinical settings. Misidentification can lead to medication errors, posing risks to patient safety and placing a burden on health care systems. Recent advances in computer vision and object detection offer promising solutions for automated solid oral dosage form (pill) recognition. However, comprehensive studies comparing code-based and no-code (automated machine learning [AutoML]) approaches for pill recognition are lacking. OBJECTIVE: This study aimed to evaluate and compare the performance, cost, usability, and deployment feasibility of pill recognition models developed with Ultralytics YOLO11 and 3 cloud-based AutoML platforms (Amazon Rekognition Custom Labels, Google Vertex artificial intelligence [AI] AutoML Vision, and Microsoft Azure Custom Vision) using multiple datasets, including real-world clinical images. METHODS: Five training subsets of increasing size (1230, 3450, 7380, 14,400, and 26,880 images) from 30 commonly dispensed medications were used to train models on YOLO11 and 3 AutoML platforms. Models were evaluated on 6 datasets from different environments: clinical images from 3 hospitals, a verification dataset, a laboratory dataset, and an exhaustive testing set. Performance metrics, including accuracy, precision, recall, and mean average precision, were calculated. We evaluated the impact of training data size on performance and benchmarked training time, platform costs, and limitations. RESULTS: No single platform dominated across all test environments. On the verification dataset (optimal conditions), accuracy ranged from 80.83% (YOLO11) to 91.60% (Google Vertex AI) when trained with the full training dataset. YOLO11 showed consistent performance improvement with increasing training data (accuracy: 63.06%-80.83%) and achieved near-perfect precision and mean average precision scores (0.95-1.00). Google Vertex AI reached above 90% accuracy on 3 training subsets but showed unpredictable declines. Amazon Rekognition maintained near-perfect precision (0.92-1.00) but had the highest false negative rates (up to 0.74), missing many pills. Custom Vision demonstrated steady performance improvements (77.08%-85.62% accuracy) but lagged behind other AutoML platforms, probably due to its older YOLOv2-based architecture. On clinical datasets, accuracy fluctuated (20.62%-90%) depending on the dataset and platform. Training costs and time varied: YOLO11 (open-source), Microsoft Azure (US $9.50-US $28.60, allowed user-predefined training duration), Google Vertex AI (US $69.30 with consistent 2.5-3-hour training times), and Amazon Rekognition (US $5.43-US $43.89 with size-dependent training time scaling, reaching nearly 40 hours on the full 26,880-image dataset). CONCLUSIONS: Each platform offers distinct advantages and trade-offs: YOLO11 provides the highest flexibility and lowest platform costs but requires technical expertise, while AutoML platforms can offer high performance at a higher cost but with limited user control, introducing unpredictability. The performance variations demonstrate that successful clinical deployment requires careful platform selection based on specific performance requirements, budget constraints, and available technical resources, followed by rigorous validation using real-world, representative data to ensure patient safety in clinical workflows.
11 Apr 2026
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