This document discusses decision making for purchasing large medical equipment like MRI machines. It presents data showing differences in MRI availability between countries. There are challenges in decision making due to varying supplier policies, objectives of health facilities, and heterogeneous information. A methodology is proposed to help with rational choice of equipment. Experts will be identified and their competence evaluated. The analytic hierarchy process (AHP) will be used to determine priority weights for key MRI parameters. Experts will provide pairwise comparisons to establish preferences. This will result in a ranked list of recommended MRI machines.
5. WHO: causes of inefficiency and
recommendations
CAUSES OF INEFFICIENCY
● EXCESSIVE PURCHASING and use of EQUIPMENT
● Financial drain etc.
SOLUTION
● DEVELOPMENT OF SYSTEMS SELECTION AND
PROCUREMENT
● Control government expenditure on health care
● Implementing new regulations etc.
Source: WHO “The world health report: health systems financing: the path to universal coverage 2010”
6. Development of a system for rational
choice of large medical equipment
7. Difficulties in decision making
Suppler Objectives of Specifi-
policies the application cations
more than
difference in
900
purchase price
specifications
specialization
of health
facility difficult for
heterogeneous
information understandin
about the units g
Source: XJ., Zhou, How can I Purchase My Dream MRI Scanner?
8.
9.
10. Questionnaire for evaluation of expert's
competence
Objective evaluation - ho Subjective evaluation - hs
Work experience
participation in
in the problem
Job position
the problem
Total work
experience
Education
Level of
Grades
Grades
Grades
Grades
Grades
(years)
area
Head of Expert specializes in the given
1 Ph.D. 0.6 >10 1 >10 1 1
organization issue
Expert participates in practical
Higher work on solving the issue but
Deputy head 0.8 education 0.4 10-5 0.8 10-5 0.8 the issue does not belong to 0.8
(master) expert's indicated
specialization
Higher
Head of The issue belongs to experts
0.6 education 0.2 <5 0.6 <5 0.6 0.6
department specialization
(bachelor)
Deputy head
The issue does not belong to
of 0.4 0.3
experts specialization
department
Golupkov, Е., 1998
11. Reference table of indices of
argumentation (ka)
Level of source's influence on the expert's
Sources of arguments opinion
high medium low zero
Conducted theoretical analysis 0.3 0.2 0.1 0
Work experience 0.5 0.4 0.2 0
Summarizing papers by local authors 0.05 0.05 0.05 0
Summarizing papers by foreign authors 0.05 0.05 0.05 0
First-hand experience with state of the
0.05 0.05 0.05 0
problem abroad
Expert's intuition 0.05 0.05 0.05 0
Fedoraev, S.V., 2010
12. General competence index for a
specific experts
hjo; hjs - objective and subjective indices
kа – index of argumentation
ki – index of familiarity with the problem
14. The overall competence of the experts
0.75 0.67
0.70
0.60
Main weighting
0.65 0.57
coefficient
0.60 0.54
0.55 0.49 0.49 0.49
0.50 0.45
0.45
0.40
0 2 4 6 8
Serial number of expert
22. Aim Level 1 Lvl. 2 Level 3 Level 4
Magnetic field
Strength
Мain magnet
Technical features
Uniformity
Stability
Magnetic field
Gradient coil systems gradient
Slew rate
Parallel imaging techniques
Number of independent
channels (RF coils)
choose a new MRI
The adequacy of Shimming
the selection Size of aperture
Ease of use Patient comfort
and safety
Size of tables
Active shielding
Safety
Noise level
Dimensions
Compatibility
Training
Customer
support
Operator and reference
manuals
Service contract
Remote diagnostics
Scientific and
Technical level
The efficiency of Proposed decision hierarchy
use
Cost of use for MRI
24. Results of paired comparisons
serial number of the specifications
serial number of the specifications
25. Mutual dependence of the
characteristics
serial number of the specifications
serial number of the specifications
Independent characteristics
Dependent characteristics
30. Summary
• Method for experts' competence identification.
• Defined the raked list of the experts.
• AHP method with network elements can be
applied;
• The way how to predict changes of weights.
• Web-system for identification of experts'
preferences.
• Defined a list of 16 key parameters.
• Algorithm for selection of preferences of
experts, processing and evaluation of results.