Data-driven decision-making in medical education and healthcare

At a time when we are surrounded by the everyday use of communication and information technologies very closely linked to the Internet, it is challenging to discern the accuracy, truthfulness and objectivity of the information presented and published. This book and its chapters aim to provide an overview of selected projects and activities across the academic and governmental domains focused on data processing and visualisation. It is crucial to recognise that, given the volume of data of varying quality that we now have at our disposal, we need to focus much more on understanding, identifying, and distributing correct information and inferences directly from the data. This is the main reason why this book was written. The individual case studies focus on examples of both good and bad practices, drawing on experiences from real-life projects. Data should always serve as a basis for decision-making processes and mechanisms, but only if they are correctly processed, understood, and, above all, interpreted. There are various ways to present results over descriptive statistics and data analysis, from summary tables to static graphs to interactive web visualisations. It is only possible to say which type and presentation format is best with additional information (such as the target audience or primary purpose of use). The selected chapters in this book highlight the complete lifecycle of understanding, processing, visualising and validating data so that all of the critical components of this process are remembered.

This book is divided into three main sections:

  • The big picture (general background and methodologies)
  • Medical and healthcare education in selected case studies
  • Health information and statistics in selected case studies

Each chapter, except the big picture, has the same format describing a particular project result as a case study, which is always based on a well-proven interdisciplinary methodology (specifically CRISP-DM – Cross-Industry Process for Data Mining – the structured approach to planning and running data mining projects.

As a methodology, it includes descriptions of individual project phases, the tasks involved with each stage, and the relationships between them.

As a process model, it provides an overview of the complete data mining life cycle.

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 Data-driven decision-making in medical education and healthcare 9.10.2023 anyone Creative Commons License

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