Data analysis of health information systems
| Keywords |
| Classification |
Keyword |
| OFICIAL |
Medicine |
Instance: 2019/2020 - 2S (of 10-02-2020 to 31-07-2020) 
Cycles of Study/Courses
Teaching Staff - Responsibilities
Teaching language
Suitable for English-speaking students
Objectives
To develop a conceptual framework for the reuse of data derived from health information systems (e.g. administrative database, clinical records, EHR) as well as to acquire the necessary skills for its application in health services research.
Learning outcomes and competences
Perceive the relevance of reusing health data in health services research, and to grasp concepts that are essential to the area of study;
Be familiar with the variety of existing data sources, their applications, limitations and strengths;
Acquire knowledge and practice about best practices in research based on data derived from health information systems, specifically regarding methodological aspects of data processing and visualization, statistical analysis, and scientific production;
Be able to interpret, criticize and conduct independent health services research based on secondary health data;
Understand the benefits and challenges of "Big Data" in health services research.
Working method
Presencial
Program
- Historical Introduction and guided article review
- Presentation of potential data sources
- Legal considerations on data reuse for research
- Use of secondary data: potentialities and limitations
- Overview on data quality and analysis’ methods
- Data preparation for analysis (data screening and creation of variables)
- Data analysis’ concepts and methodologies (biostatistics and machine learning)
- Techniques for presenting results
- Standards, recommendations for scientific production using re-use of health systems data (RECORD - REporting of studies Conducted using Observational Routinely-collected health Data)
- Big Data - Opportunities and Limitations
Mandatory literature
MIT Critical Data; Secondary Analysis of Electronic Health Records, Cham: Springer International Publishing, 2016
Sherman, R. E., Anderson, S. A., Dal Pan, G. J., Gray, G. W., Gross, T., Hunter, N. L., … Califf, R. M.; Real-World Evidence — What Is It and What Can It Tell Us? , New England Journal of Medicine, 375(23), 2293–2297, 2016
Teaching methods and learning activities
Theoretical-practical classes (14 hours) with presentation and discussion of topics, group and individual exercises, with interpretation of scientific articles and results of practical exercises. Tutorial guidance (14 hours) for monitoring and discussion of group assignments.
Software
IBM SPSS Statistics
Evaluation Type
Distributed evaluation without final exam
Assessment Components
| Designation |
Weight (%) |
| Trabalho escrito |
80,00 |
| Participação presencial |
20,00 |
| Total: |
100,00 |
Amount of time allocated to each course unit
| Designation |
Time (hours) |
| Apresentação/discussão de um trabalho científico |
2,00 |
| Estudo autónomo |
17,00 |
| Frequência das aulas |
12,00 |
| Trabalho de investigação |
50,00 |
| Total: |
81,00 |
Eligibility for exams
Presence in 75% of the classes
Calculation formula of final grade
Evaluation will be based on individual and group assignments (80%), with oral presentations (20%).