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INTRODUCTION AND OBJECTIVES: Omission of insulin, a high-alert medication with one of the highest locally reported errors, could lead to severe hyperglycemia, which could result in coma or death if not treated timeously. This study aimed to identify, evaluate, and implement strategies to reduce the occurrence of insulin omission errors in diabetic adult patients requiring insulin. METHODS: This project followed the JBI Evidence Implementation Framework and conducted context analysis, strategy implementation, and evaluation of outcomes according to evidence-based quality indicators. The JBI PACES and JBI GRiP situational analysis tools were used to support data collection and implementation planning. There was one evidence-based criterion and five sub-criteria, with a sample size of 22 patients. RESULTS: There was increased compliance with best practices to reduce interruptions and distractions from baseline audit (50%) to follow-up audits 1 (45.4%) and 2 (31.8%), and no insulin omission incidences during the implementation period. In the post-implementation analysis, there were notable improvements in compliance with strategies related to nurses; however, reduced compliance was observed related to patients. Key barriers to implementation included patients still disturbing nurses despite the nurses wearing the medication vests and patients forgetting instructions not to disturb nurses during medication administration. Strategies to improve compliance included ensuring coverage in each cubicle during insulin preparation and administration, tending to patients' needs prior to insulin administration, and use of posters as reminders. CONCLUSIONS: There was an overall increase in compliance with best practice to reduce interruptions and distractions and no insulin omission incidences related to interruptions and distractions during the implementation phase. SPANISH ABSTRACT: http://links.lww.com/IJEBH/A219.
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Background: For therapy planning in cancer patients multidisciplinary team meetings (MDM) are mandatory. Due to the high number of cases being discussed and significant workload of clinicians, Clinical Decision Support System (CDSS) may improve the clinical workflow. Methods: This review and meta-analysis aims to provide an overview of the systems utilized and evaluate the correlation between a CDSS and MDM. Results: A total of 31 studies were identified for final analysis. Analysis of different cancers shows a concordance rate (CR) of 72.7% for stage I-II and 73.4% for III-IV. For breast carcinoma, CR for stage I-II was 72.8% and for III-IV 84.1%, P≤ 0.00001. CR for colorectal carcinoma is 63% for stage I-II and 67% for III-IV, for gastric carcinoma 55% and 45%, and for lung carcinoma 85% and 83% respectively, all P>0.05. Analysis of SCLC and NSCLC yields a CR of 94,3% and 82,7%, P=0.004 and for adenocarcinoma and squamous cell carcinoma in lung cancer a CR of 90% and 86%, P=0.02. Conclusion: CDSS has already been implemented in clinical practice, and while the findings suggest that its use is feasible for some cancers, further research is needed to fully evaluate its effectiveness.
ABSTRACT
INTRODUCTION: Multidisciplinary team meetings (MDMs), also known as tumor conferences, are a cornerstone of cancer treatments. However, barriers such as incomplete patient information or logistical challenges can postpone tumor board decisions and delay patient treatment, potentially affecting clinical outcomes. Therapeutic Assistance and Decision algorithms for hepatobiliary tumor Boards (ADBoard) aims to reduce this delay by providing automated data extraction and high-quality, evidence-based treatment recommendations. METHODS AND ANALYSIS: With the help of natural language processing, relevant patient information will be automatically extracted from electronic medical records and used to complete a classic tumor conference protocol. A machine learning model is trained on retrospective MDM data and clinical guidelines to recommend treatment options for patients in our inclusion criteria. Study participants will be randomized to either MDM with ADBoard (Arm A: MDM-AB) or conventional MDM (Arm B: MDM-C). The concordance of recommendations of both groups will be compared using interrater reliability. We hypothesize that the therapy recommendations of ADBoard would be in high agreement with those of the MDM-C, with a Cohen's kappa value of ≥ 0.75. Furthermore, our secondary hypotheses state that the completeness of patient information presented in MDM is higher when using ADBoard than without, and the explainability of tumor board protocols in MDM-AB is higher compared to MDM-C as measured by the System Causability Scale. DISCUSSION: The implementation of ADBoard aims to improve the quality and completeness of the data required for MDM decision-making and to propose therapeutic recommendations that consider current medical evidence and guidelines in a transparent and reproducible manner. ETHICS AND DISSEMINATION: The project was approved by the Ethics Committee of the Charité - Universitätsmedizin Berlin. REGISTRATION DETAILS: The study was registered on ClinicalTrials.gov (trial identifying number: NCT05681949; https://clinicaltrials.gov/study/NCT05681949 ) on 12 January 2023.
