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ISLA Santarém 26690

Health Data Representation and Visualization

Data and Technology Management in Health
  • ApresentaçãoPresentation
      
  • ProgramaProgramme
    1. Introduction to Data Representation in Health 1.1. Basic concepts of data representation 1.2. Importance of accurate data representation in healthcare 2. Visualization Tools and Dashboard Construction 2.1. Overview of visualization tools 2.2. Practical dashboard construction for health data analysis 3. Design Principles for Healthcare Interfaces 3.1. Usability in interfaces for healthcare professionals 3.2 User-centered design in healthcare settings 4. Methods of Analysis and Interpretation of Health Data 4.1 Statistical techniques for data analysis 4.2 Interpretation of results for decision-making 5. Practical Applications in Health Data Management 5.1. Case studies on data usage in healthcare 5.2. Development of practical projects in data management 6. Ethics and Privacy in Health Data Management 6.1. Ethical considerations in data usage 6.2. Privacy protection and compliance with regulations  
  • ObjectivosObjectives
    The students will develop a comprehensive understanding of the fundamental principles of data representation and visualization in health, as well as acquire knowledge of visualization techniques and tools applicable to health sciences. Additionally, they will become familiar with user-centered interface design concepts and understand their relevance to healthcare professionals. The following learning objectives are proposed: O1. Develop competencies in effective data representation in health.  O2. Introduce visualization tools and dashboard construction. 03. Discuss design principles for user-friendly healthcare interfaces. 04. Highlight the importance of usability in promoting adoption by healthcare professionals.
  • BibliografiaBibliography
    Davis, N. A. (2023). Foundations of Health Information Management - E-Book: Foundations of Health Information Management - E-Book. Elsevier Health Sciences. Deckler, G., Powell, B., & Gordon, L. (2022). Mastering Microsoft Power BI: Expert techniques to create interactive insights for effective data analytics and business intelligence. Packt Publishing Ltd. Khan, A. A., & Alam, M. (2019). Data Science in Healthcare: Concepts, Methodologies, Tools, and Applications. IGI Global.  Steele, R., & Iliinsky, N. (2019). Designing Data Visualizations: Representing Informational Relationships. O'Reilly Media
  • MetodologiaMethodology
    The classes will be developed using the following methodologies: Synchronous (distance): MET 1. - Expository, interrogative, and interactive methods: Presentation/explanation of concepts using expository, interrogative, and interactive methods. All pedagogical support materials are made available through the Moodle platform. In-person: MET 2. - Active methodologies: Practical application through exercises and assignments in a classroom setting. Autonomous: MET 3. - Reading of the recommended bibliography. Completion of practical exercises not completed during practical classes and others proposed by the instructor. These materials and exercises are made available on the Moodle platform. MET 4. - The instructor provides feedback (Tutorial Guidance - OT) on the results obtained by the student in solving these proposed problems, through the Moodle platform. 
  • LínguaLanguage
    Português
  • TipoType
    Semestral
  • ECTS
    6
  • NaturezaNature
    Mandatory
  • EstágioInternship
    Não
  • AvaliaçãoEvaluation

    Avaliação Curricular (contínua (presencial):
    Esta modalidade de avaliação é constituída por:
    AVAL 1. Portfólio de trabalhos/exercícios de aula com recurso ao Power BI. AVAL 2. Trabalho prático (relatório e projeto).
    AVAL 3. Teste final teórico/prático.
    A classificação final é calculada através da fórmula Classificação Final = 0,2*A1+0,4*A2 + 0,4 *A3. O estudante é aprovado se obtiver classificação igual ou superior a 9,5 valores.
    Avaliação Final (presencial) - A: O estudante realiza o exame teórico-prático (100%) e é aprovado se obtiver uma classificação igual ou superior a 9,5 valores em 20.
    Época de Recurso e Época Especial (presencial) - A: O estudante realiza o exame teórico-prático (100%) e fica aprovado se obtiver uma classificação igual ou superior a 9,5 valores em 20.