B 2023

Data-driven decision-making in medical education and healthcare

KOMENDA, Martin

Základní údaje

Originální název

Data-driven decision-making in medical education and healthcare

Název česky

Datově orientované rozhodování v lékařském vzdělávání a zdravotní péči

Název anglicky

Data-driven decision-making in medical education and healthcare

Autoři

Vydání

1. vyd. Brno, 2023

Nakladatel

koedice Masarykova univerzita / Ústav zdravotnických informací a statistiky ČR

Další údaje

Jazyk

čeština

Typ výsledku

Odborná kniha

Obor

10201 Computer sciences, information science, bioinformatics

Stát vydavatele

Česká republika

Utajení

není předmětem státního či obchodního tajemství

Forma vydání

tištěná verze "print"

ISBN

978-80-280-0392-0

Klíčová slova česky

datově orientované rozhodování, medicínské vzdělávání, zdravotnické informace, otevřená data

Klíčová slova anglicky

data-driven decision-making, medical education, health information, open data

Štítky

Příznaky

Mezinárodní význam, Recenzováno
Změněno: 18. 12. 2023 09:25, Mgr. Martina Švaříčková Hlavatá

Anotace

V originále

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.

Anglicky

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.