{"title":"信息学教育特刊:通过与临床相关案例研究的互动式动手研讨会教授数据科学。","authors":"Alvin Dean Jeffery, Patricia Sengstack","doi":"10.1055/a-2407-1272","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians.</p><p><strong>Objective: </strong>Addressing the limited exposure of healthcare providers to leverage and critique data science methods, this interactive workshop aims to provide clinicians with foundational knowledge in data science, enabling them to contribute effectively to teams focused on improving care quality.</p><p><strong>Methods: </strong>The workshop focuses on meaningful topics for clinicians, such as model performance evaluation and introduces machine learning through hands-on exercises using free, interactive python notebooks. Clinical case studies on sepsis recognition and opioid overdose death provide relatable contexts for applying data science concepts.</p><p><strong>Results: </strong>Positive feedback from over 300 participants across various settings highlights the workshop's effectiveness in making complex topics accessible to clinicians.</p><p><strong>Conclusions: </strong>Our approach prioritizes engaging content delivery and practical application over extensive programming instruction, aligning with adult learning principles. This initiative underscores the importance of equipping clinicians with data science knowledge to navigate today's data-driven healthcare landscape, offering a template for integrating data science education into healthcare informatics programs or continuing professional development.</p>","PeriodicalId":48956,"journal":{"name":"Applied Clinical Informatics","volume":" ","pages":""},"PeriodicalIF":2.1000,"publicationDate":"2024-08-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Special Issue on Informatics Education: Teaching Data Science through an Interactive, Hands-On Workshop with Clinically-Relevant Case Studies.\",\"authors\":\"Alvin Dean Jeffery, Patricia Sengstack\",\"doi\":\"10.1055/a-2407-1272\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians.</p><p><strong>Objective: </strong>Addressing the limited exposure of healthcare providers to leverage and critique data science methods, this interactive workshop aims to provide clinicians with foundational knowledge in data science, enabling them to contribute effectively to teams focused on improving care quality.</p><p><strong>Methods: </strong>The workshop focuses on meaningful topics for clinicians, such as model performance evaluation and introduces machine learning through hands-on exercises using free, interactive python notebooks. Clinical case studies on sepsis recognition and opioid overdose death provide relatable contexts for applying data science concepts.</p><p><strong>Results: </strong>Positive feedback from over 300 participants across various settings highlights the workshop's effectiveness in making complex topics accessible to clinicians.</p><p><strong>Conclusions: </strong>Our approach prioritizes engaging content delivery and practical application over extensive programming instruction, aligning with adult learning principles. This initiative underscores the importance of equipping clinicians with data science knowledge to navigate today's data-driven healthcare landscape, offering a template for integrating data science education into healthcare informatics programs or continuing professional development.</p>\",\"PeriodicalId\":48956,\"journal\":{\"name\":\"Applied Clinical Informatics\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":2.1000,\"publicationDate\":\"2024-08-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Applied Clinical Informatics\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1055/a-2407-1272\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MEDICAL INFORMATICS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Clinical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1055/a-2407-1272","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
Special Issue on Informatics Education: Teaching Data Science through an Interactive, Hands-On Workshop with Clinically-Relevant Case Studies.
Background: In this case report, we describe the development of an innovative workshop to bridge the gap in data science education for practicing clinicians (and particularly nurses). In the workshop, we emphasize the core concepts of machine learning and predictive modeling to increase understanding among clinicians.
Objective: Addressing the limited exposure of healthcare providers to leverage and critique data science methods, this interactive workshop aims to provide clinicians with foundational knowledge in data science, enabling them to contribute effectively to teams focused on improving care quality.
Methods: The workshop focuses on meaningful topics for clinicians, such as model performance evaluation and introduces machine learning through hands-on exercises using free, interactive python notebooks. Clinical case studies on sepsis recognition and opioid overdose death provide relatable contexts for applying data science concepts.
Results: Positive feedback from over 300 participants across various settings highlights the workshop's effectiveness in making complex topics accessible to clinicians.
Conclusions: Our approach prioritizes engaging content delivery and practical application over extensive programming instruction, aligning with adult learning principles. This initiative underscores the importance of equipping clinicians with data science knowledge to navigate today's data-driven healthcare landscape, offering a template for integrating data science education into healthcare informatics programs or continuing professional development.
期刊介绍:
ACI is the third Schattauer journal dealing with biomedical and health informatics. It perfectly complements our other journals Öffnet internen Link im aktuellen FensterMethods of Information in Medicine and the Öffnet internen Link im aktuellen FensterYearbook of Medical Informatics. The Yearbook of Medical Informatics being the “Milestone” or state-of-the-art journal and Methods of Information in Medicine being the “Science and Research” journal of IMIA, ACI intends to be the “Practical” journal of IMIA.