Data Driven Healthcare

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BRIEF DESCRIPTION

This course is designed to provide both clinical and non-clinical staff with a comprehensive understanding of healthcare data and its role in decision-making processes.

The course will cover a range of topics to ensure a solid understanding of how data is collected, analysed, and utilised in healthcare settings.

By the end of this course, students should be able to:

  • Recognise and differentiate various types of healthcare data and understand their role in decision-making processes.
  • Understand the functionalities of Electronic Health Records (EHR) systems and the data they collect.
  • Know the CRISP-DM methodology to tackle healthcare data analysis projects.
  • Perform exploratory data analysis using descriptive statistics and visualisation techniques to understand the data.
  • Examine a dataset for potential patterns and extractable knowledge.
  • Know healthcare innovations such as precision medicine and predictive analytics for disease prevention, understanding their impact on patient care and outcomes.

SUMMARY

This course has 2 Modules, 6 Sessions (to be organized according to the specific requests of the hospitals, either on place or online). The Sessions description is:

  • M1: Introduction to Healthcare Data
    • Types of healthcare data (structured vs. unstructured)
    • Data sources (clinical, administrative, patient-generated)
    • The role of healthcare data in decision-making
  • M2: Electronic Health Records (EHR) Systems
    • Overview of EHR systems
    • Types of data collected by EHRs
    • Benefits and challenges of EHR implementation
  • M3: Exploratory Data Analysis (EDA)
    • Descriptive statistics (mean, median, mode, standard deviation)
    • Data visualization techniques (histograms, scatter plots, box plots)
    • Tools for EDA (e.g., Excel, Python, R)
  • M4: Artificial Intelligence and Machine Learning in Healthcare  
    • Introduction to CRISP-DM (Cross-Industry Standard Process for Data Mining)
    • Phases of CRISP-DM
  • M5: Data Analysis and Pattern Recognition
    • Introduction to machine learning for pattern recognition: supervised, semi-supervised, and unsupervised learning
    • Techniques for identifying patterns in healthcare data
    • Case studies on successful data analysis projects
  • M6: Healthcare innovations driven by data analytics
    • Overview of precision medicine
    • Remote patient monitoring
    • Predictive analytics for disease prevention
    • The impact of healthcare innovations on patient care and outcomes

Each session will have a short survey (continuous); the final hands-on exercise. Course final assessment.

AUTHOR AND COORDINATOR

Author

João Carlos Ferreira

Coordinator

Ana Madureira