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Biostatistics, Information and Decision in Health II

Code: MI329     Acronym: BIOINFDS_II

Keywords
Classification Keyword
OFICIAL Medicine

Instance: 2019/2020 - 2S (of 10-02-2020 to 31-07-2020) Ícone do Moodle

Active? Yes
Responsible unit: Departamento Medicina da Comunidade, Informação e Decisão em Saúde
Course/CS Responsible: Master Degree in Medicine

Cycles of Study/Courses

Acronym No. of Students Study Plan Curricular Years Credits UCN Credits ECTS Contact hours Total Time
MIMED 292 Mestrado Integrado em Medicina- Plano oficial 2013 (Reforma Curricular) 3 - 3 30 81

Teaching Staff - Responsibilities

Teacher Responsibility
Luís Filipe Ribeiro de Azevedo
Pedro Pereira Rodrigues

Teaching language

Suitable for English-speaking students

Objectives

To develop knowledge on biostatistics, information and critical appraisal of medical evidence and to develop skills to integrate the best evidence in the health care decision making process.

Learning outcomes and competences

The learner should:

1) conceptualize the knowledge search process  guided by a clear research question in health care

2) learn the need to qualify available biomedical research in terms of adequacy and validity

3) enclose the concept of integration in clinical practice according to patient's values and clinician's experience

4) be familiar with evidence formalization processes, used during their own decision process in everyday clinical practice

5) understand the existence of other perspectives in decision in health care

Working method

Presencial

Program

• Decision in health: in technology evaluation, health service research and clinical research;
• Stepwise approach to decision in health – evidence based medicine:
• Critical analysis of studies for specific clinical questions:
• Synthesis of evidence – systematic reviews and meta-analysis studies;
• Results valuing methodologies – preferences, utilities and QALY's;
• Economic evaluation and measurement of costs in health;
• Basics of decision analysis:
   ◦ structuring and analysis of the decision process;
   ◦ construction and interpretation of decision trees;
  ◦ uncertainty management, threshold analysis, sensitivity analysis and modeling;
• Decisionsupportsystems:
   ◦ formalization of decision algorithms after clinical guidelines;
   ◦ decision trees and probabilistic graphical models;
   ◦ artificial intelligence, knowledge discovery from health data;
   ◦ decision support systems based on clinical guidelines and meta-analysis;
   ◦ evaluation of clinical decision support systems

Mandatory literature

M. G. Myriam Hunink, Paul P. Glasziou, Joanna E. Siegel, Jane C. Weeks, Joseph S. Pliskin, Arthur S. Elstein, Milton C. Weinstein; Decision Making in Health and Medicine: Integrating Evidence and Values, Cambridge University Press, 2001
Michael F. Drummond, Bernard J. O'Brien, Greg L. Stoddart, George W. Torrance; Methods for the Economic Evaluation of Health Care Programs, Oxford University Press, 1997
Marthe R. Gold, Joanna E. Siegel, Louise B. Russell, Milton C. Weinstein; Cost-Effectiveness in Health and Medicine, Oxford University Press, 1996
Dinis-Ribeiro M; Relatório Pedagógico relativo ao ensino de uma unidade curricular de “Evidência na Decisão”. Provas de Agregação, Faculdade de Medicina da Universidade do Porto, 2009
Sackett DL et al; Evidence-Based Medicine: how to practice and teach EBM, Churchill Livingstone, 2000
Berner E.; Clinical decision support systems: theory and practice, Springer Verlag, 2006

Teaching methods and learning activities

Overall, after theoretical exposition of each topic, groups will be formed to perform practical exercises, along with individual exercises, using an e-learning platform. Furthermore, software for decision analysis and decision support will be made available in practical lessons.

Theoretical (12 hours) and theoretical-practical (18 hours) lessons with presentation and discussion of different topics, group and individual exercises with manuscripts critical appraisal and software outputs adequate interpretation.

Evaluation will result from a theoretical exam (80%) and distributed practical evaluation (20%). Only the theoretical exam component is allowed to be resat.

The exam consists of multiple choice questions with simple answers, short answer questions and short development questions. The use of a calculator or any other electronic device is not permitted. The exam will last for a maximum of 90 minutes.

keywords

Physical sciences > Mathematics > Statistics

Evaluation Type

Distributed evaluation with final exam

Assessment Components

Designation Weight (%)
Exame 80,00
Participação presencial 10,00
Teste 10,00
Total: 100,00

Amount of time allocated to each course unit

Designation Time (hours)
Estudo autónomo 51,00
Frequência das aulas 30,00
Total: 81,00

Eligibility for exams

Presence in 75% of the classes

Calculation formula of final grade

Evaluation will result from a theoretical test (80%) and practical evaluation (20%). The practical evaluation consists of tests at the end of each session (10%) and a global evaluation based on the student's participation during classes (10%). The practical (distributed) evaluation from former editions of the course will not be considered, except for strictly exceptional cases, explicitly justified and requested to the coordinator in the beginning of the course.

Observations

Aproval is restricted to students who manage to get a positive mark (greater or equal to 9.5 out of 20) in each of the evaluation components (exam and distributed scoring)
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