Patient-based real-time quality control (PBRTQC) is a laboratory tool for monitoring the performance of the testing process. It includes well-established procedures like Bull’s algorithm, average of nomals, moving median, moving average (MA) and exponentially (weighted) MAs. Following the setup and optimization processes, a key step prior to the routine implementation of PBRTQC is the verification and documentation of the performance of the PBRTQC as part of the laboratory quality system. This verification process should provide a realistic representation of the performance of the PBRTQC in the environment it is being implemented in, to allow proper risk assessment by laboratory practitioners. This document focuses on the recommendation on performance verification of PBRTQC prior to implementation.

Background: Machine learning (ML) has been applied to an increasing number of predictive problems in laboratory medicine, and published work to date suggests that it has tremendous potential for clinical applications. However, a number of groups have noted the potential pitfalls associated with this work, particularly if certain details of the development and validation pipelines are not carefully controlled. Methods: To address these pitfalls and other specific challenges when applying machine learning in a laboratory medicine setting, a working group of the International Federation for Clinical Chemistry and Laboratory Medicine was convened to provide a guidance document for this domain. Results: This manuscript represents consensus recommendations for best practices from that committee, with the goal of improving the quality of developed and published ML models designed for use in clinical laboratories. Conclusions: The committee believes that implementation of these best practices will improve the quality and reproducibility of machine learning utilized in laboratory medicine. Summary: We have provided our consensus assessment of a number of important practices that are required to ensure that valid, reproducible machine learning (ML) models can be applied to address operational and diagnostic questions in the clinical laboratory. These practices span all phases of model development, from problem formulation through predictive implementation. Although it is not possible to exhaustively discuss every potential pitfall in ML workflows, we believe that our current guidelines capture best practices for avoiding the most common and potentially dangerous errors in this important emerging field.

This study presents a web-based method and its interface ensuring alignment of all parts of a curriculum map including competencies, objectives, teaching and assessment methods, workload and patient availability. Needs, acceptance and effectiveness are shown through a nine-year study. After a comprehensive needs assessment, the curriculum map and a web-based interface "Learning Opportunities, Objectives and Outcome Platform" (LOOOP) were developed according to Harden's conceptual framework of 10-steps for curriculum mapping. The outcome was measured by surveys and results of interdisciplinary MCQ-assessments. The usage rates and functionalities were analysed. The implementation of LOOOP was significantly associated with improved perception of the curriculum structure by teachers and students, quality of defined objectives and their alignment with teaching and assessment, usage by students to prepare examinations and their scores in interdisciplinary MCQ-assessment. Additionally, LOOOP improved the curriculum coordination by faculty, and assisted departments for identifying patient availability for clinical training. LOOOP is well accepted among students and teachers, has positive effect on curriculum development, facilitates effective utilisation of educational resources and improves student's outcomes. Currently, LOOOP is used in five undergraduate medical curricula including 85,000 mapped learning opportunities (lectures, seminars), 5000 registered users (students, teachers) and 380,000 yearly page-visits.