Research Appraisals
Evidence-based critical appraisals of the latest medical research, systematically evaluated using Oxford CEBM methodology.
Showing 6 appraisals
European journal of hospital pharmacy : science and practice
Unit dose drug dispensing systems in hospitals: a systematic review of medication error reduction and cost-effectiveness
BACKGROUND: Medical errors pose significant risks to patient safety and public health. Automated unit dose drug dispensing systems (UDDSs) have emerged as valuable tools to reduce medication errors while optimising economic and logistical resources. OBJECTIVES: This systematic review aims to evaluate studies specifically focused on the impact of automated UDDSs in reducing medication errors and streamlining processes. METHODS: A literature search was performed on PubMed, Scopus, and Web of Science, focusing on peer-reviewed articles published between 2019 and 2024. The search, concluded on 24 September 2024, included studies conducted in inpatient hospital settings that assessed automated UDDS effects on medication errors, therapy management and inventory control. Outcomes examined included effects on patient safety, cost-effectiveness and inventory management. Results were synthesised qualitatively. RESULTS: From 3346 references, four studies met the inclusion criteria: a cost-effectiveness analysis, an uncontrolled before-and-after study, and two observational studies. UDDS improved medication processes, reducing drug-related problems, medication handling and dispensing time by 50% per patient per day. Integrated with barcode scanning, UDDS lowered medication administration errors (MAEs) from 19.5% to 15.8% and harmful MAEs from 3.0% to 0.3%. Overall, medication errors dropped by 45-70%, enhancing safety and reducing manual handling risks. UDDS demonstrated cost-effectiveness by significantly reducing MAEs. The study estimated a reduction in MAEs, with a cost-effectiveness ratio of €17.69 per avoided MAE. For potentially harmful MAEs, the cost-effectiveness ratio was estimated at €30.23 per avoided error. These findings suggest substantial long-term savings potential, though the exact magnitude may vary depending on hospital size and implementation specifics CONCLUSIONS: Automated UDDSs improve patient safety by significantly reducing medication errors and delivering cost savings through better inventory management. Challenges such as high initial costs and workflow adjustments can be mitigated through gradual implementation and staff training. Further integration with other healthcare technologies, such as barcoding, real-time tracking, artificial intelligence (AI)-driven error prevention tools and fully automated restocking systems could enhance UDDS benefits and further support hospital processes.
25 June 2026
Read appraisal →European journal of hospital pharmacy : science and practice
A collaborative telepharmacy model for dispensing and informed delivery from hospital to community pharmacies
BACKGROUND: This study examines the implementation of a collaborative telepharmacy programme in an outpatient pharmaceutical care unit (OPCU) at a tertiary hospital. The programme coordinates between the hospital pharmacy, community pharmacies, a pharmaceutical distributor, and the regional official college of pharmacists to optimise medication dispensing and delivery to outpatients. METHODS: The programme addressed challenges in operations, logistics, technology, legality, training and information. A protocol was developed defining the collaborative dispensing circuit, including criteria for patient selection and prioritisation. RESULTS: Over 39 months, 13 310 shipments were made to 1039 patients, averaging 17 daily. Each patient received about 13 deliveries. A total of 14 283 medications from 258 specialties were dispensed. The programme saved 512 534 km and 542 164 min (356 days) in travel. Each patient saved approximately 493 km and 522 min, reducing CO2 emissions by 58-116 kg per patient, or 72-145 tonnes overall. A survey of 130 patients revealed a 93% preference for this model over home or healthcare facility delivery. DISCUSSION: The implementation of telepharmacy programmes for dispensing hospital medication to community pharmacies marks a significant advancement in patient care. Initially rare, telepharmacy is now widespread, overcoming previous barriers. Programmes show similar effectiveness to home delivery, improving workflow and safety. Future improvements may include remote monitoring tools and video calls. Despite some limitations, such as economic analysis and tracking, telepharmacy has proven beneficial for patients, offering cost savings and enhanced confidentiality. CONCLUSION: The collaborative telepharmacy circuit was efficiently and safely implemented, offering an innovative approach that meets the needs and expectations of patients in the OPCU.
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 →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
Read appraisal →International journal of pharmaceutics
Beyond spectroscopy: Machine vision as the future of non-destructive testing in 3D-printed pharmaceuticals
3D Printing (3DP) has revolutionized personalized medicine by enabling the production of patient-specific dosage forms and adaptable drug delivery systems. However, despite these advances, 3DP presents significant challenges in quality control (QC), particularly as traditional methods may not be suitable for small-scale or on-demand manufacturing. To address these challenges, it is essential to develop flexible, precise, and efficient QC strategies that ensure product integrity. This review focuses on non-destructive spectroscopic- and imaging-based testing techniques that can bridge this gap. Techniques such as Near-Infrared Spectroscopy, Raman Spectroscopy, Terahertz Spectroscopy, Optical Coherence Tomography, and Hyperspectral Imaging provide robust alternatives to conventional QC approaches. Moreover, Machine Vision (MV) is emerging as a promising tool for real-time defect detection and visual inspection. When integrated with advanced algorithms and artificial intelligence, MV enables automated decision-making and defect identification in 3DP pharmaceuticals. Recent advancements in deep learning, especially through Convolutional Neural Networks (CNNs) and You Only Look Once (YOLO)-based models, have further enhanced the potential of MV in this domain, offering improved accuracy, speed, and adaptability under varying conditions. This review also includes a comparative evaluation of these techniques, highlighting the increasing importance of non-destructive tools, particularly MV, in supporting innovation and ensuring quality within an increasingly personalized pharmaceutical landscape. Furthermore, it also discusses existing gaps, regulatory challenges, and the need for standardization to ensure the safe and reliable integration of these technologies into pharmaceutical manufacturing workflows.
10 Apr 2026
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