First cycle
degree courses
Second cycle
degree courses
Single cycle
degree courses
School of Engineering
BIOENGINEERING
Course unit
CLINICAL ENGINEERING AND HEALTH TECHNOLOGY ASSESSMENT
INP7080277, A.A. 2018/19

Information concerning the students who enrolled in A.Y. 2017/18

Information on the course unit
Degree course Second cycle degree in
BIOENGINEERING
IN0532, Degree course structure A.Y. 2011/12, A.Y. 2018/19
N0
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Number of ECTS credits allocated 6.0
Type of assessment Mark
Course unit English denomination CLINICAL ENGINEERING AND HEALTH TECHNOLOGY ASSESSMENT
Department of reference Department of Information Engineering
E-Learning website https://elearning.dei.unipd.it/course/view.php?idnumber=2018-IN0532-000ZZ-2017-INP7080277-N0
Mandatory attendance No
Language of instruction English
Branch PADOVA
Single Course unit The Course unit can be attended under the option Single Course unit attendance
Optional Course unit The Course unit can be chosen as Optional Course unit

Lecturers
Teacher in charge ANDREA FACCHINETTI ING-INF/06

Mutuated
Course unit code Course unit name Teacher in charge Degree course code
INP7080277 CLINICAL ENGINEERING AND HEALTH TECHNOLOGY ASSESSMENT ANDREA FACCHINETTI IN2371
INP7080277 CLINICAL ENGINEERING AND HEALTH TECHNOLOGY ASSESSMENT ANDREA FACCHINETTI IN2371

ECTS: details
Type Scientific-Disciplinary Sector Credits allocated
Core courses ING-INF/06 Electronic and Information Bioengineering 6.0

Course unit organization
Period Second semester
Year 2nd Year
Teaching method frontal

Type of hours Credits Teaching
hours
Hours of
Individual study
Shifts
Lecture 6.0 48 102.0 No turn

Calendar
Start of activities 25/02/2019
End of activities 14/06/2019

Examination board
Board From To Members of the board
1 A.A. 2017/2018 01/10/2017 15/03/2019 FACCHINETTI ANDREA (Presidente)
SPARACINO GIOVANNI (Membro Effettivo)
BERTOLDO ALESSANDRA (Supplente)
DALLA MAN CHIARA (Supplente)
PEDERSEN MORTEN GRAM (Supplente)
RUGGERI ALFREDO (Supplente)
SACCOMANI MARIAPIA (Supplente)
TOFFOLO GIANNA MARIA (Supplente)

Syllabus
Prerequisites: Fundamentals of probability, statistics, and matrix algebra
Target skills and knowledge: Expected knowledge and skills:
1. To know how hospital clinical engineering services are structured
2. To know the current legislation regarding the classification and management of medical devices
3. To know the basics of economic evaluations in health care, and how these are performed
4. To be able to create decision models for economic evaluations in health care
5. To know the techniques to perform sensitivity analysis in decision models in the presence of data uncertainty
6. To know the methodologies for individualizing the variables of decision models
7. To be able to model the survival time or, more generally, the time to event
8. To know the basics of health technology assessment
9. To be able to understand and analyze health technology assessment reports
Examination methods: The verification of knowledge and skills will be through a written exam, which includes open questions on the topics of the course and exercises on the creation / solution of decision models
Assessment criteria: Completeness of the knowledge acquired on the topics of the course
Understanding of the arguments treated in the course, ability to rework the concepts and ability to correctly solve the proposed problems of health economic evaluation
Course unit contents: 1. Medical devices: general aspects, evolution of legislation on medical devices, acquisition, management, maintenance and disposal
2. Clinical engineering: definition of the professional figure and clinical engineering services for the management of biomedical equipment, tasks of the clinical engineer
3. Economic evaluations in healthcare: definition of costs and benefits in the health sector, cost-effectiveness analysis, cost-utility analysis, cost-benefit analysis
4. Methods for preference (utility) elicitation in health care
5. Creation and analysis of decision trees for health economic evaluations
6. Markov models: definition and statistical properties (irreducibility, ergodicity, properties of the transition matrix, residence time, steady state probability) and their application for health economic evaluations
7. Methods for sensitivity analysis in the presence of parameter uncertainty: definitions of variability, heterogeneity and uncertainty, deterministic and probabilistic sensitivity analysis and tornado diagrams
8. Linear regression and logistic regression for the individualization of the parameters of decision models, and basic elements of feature selection
9. Survival analysis: censoring, risk and hazard function, survival function, methods for the creation of survival functions (Kaplan-Meier method, parametric models, parametric regression methods, Cox regression)
10. Health technology assessment: origins and evolution, objectives, bodies responsible for issuing evaluation reports, types of reports (fundamentals on key features for their analysis and creation)
Planned learning activities and teaching methods: The teaching activity includes classroom lessons where the theoretical contents of the course are dealt with (through powerpoint files), always coupled with practical examples for a better contextualization of the topic. The course includes a series of laboratories in the computer room (Matlab environment) that aims to teach how to program methodologies for decision analysis through the resolution of some representative problems
Additional notes about suggested reading: All the teaching material presented during the lectures is made available on the moodle platform (powerpoint file)
Textbooks (and optional supplementary readings)
  • Drummond, Michael F., Methods for the economic evaluation of health care programmesMichael F. Drummond ... [et al.]. Oxford: Oxford university press, 2015. Cerca nel catalogo
  • Briggs, Andrew; Sculpher, Mark, Decision modelling for health economic evaluationAndrew Briggs, Karl Claxton, Mark Sculpher. Oxford: Oxford University, 2007. Cerca nel catalogo

Innovative teaching methods: Teaching and learning strategies
  • Lecturing
  • Laboratory
  • Case study
  • Working in group
  • Problem solving
  • Loading of files and pages (web pages, Moodle, ...)

Innovative teaching methods: Software or applications used
  • Moodle (files, quizzes, workshops, ...)
  • Matlab

Sustainable Development Goals (SDGs)
Quality Education