AssistMe

1
AssistMe
Project leaders Ankica Babic, Urban Lönn, Henrik
Casimir Ahn
2
Problem solving 1

• Start with clinical questions that should be
  supported by decision support and data mining.
• Distinguish levels of decision support from user
  driven to structured procedures for knowledge
  mining
• Cluster analysis, Case Based Reasoning (CBR),
  statistical reports
• More, specialized reports?

3
Problem solving 2

• Actively involve the physicians in design,
  implementation, and evaluation of our web based
  system.
• Clinical evaluation of extracted knowledge.

4
System overview
5
Start page
6
Homepage for patients
7
Questionnaires
8
Homepage for physicians
9
Add patient cases
10
Case based reasoning(result)
11
Case based reasoning (patient case)
12
Cluster analysis - introduction
13
Cluster analysis
14
Cluster analysis

• Calculates the equality/difference between
  patients

15
Example Calculation of difference using age and
weight
kg
The difference is 50
years
16
Cluster analysis

• Calculates the equality/difference of patients
• Places similar patients in the same groups
  (clusters) and different patients in different
  groups.
• The user can choose what variables to use for
  comparing the patients when the population is
  divided into subgroups. The number of groups must
  also be specified.
• Additional information, such as the survival
  percentage, is provided for the different groups.

17
(No Transcript)
18
Clusters (former page)
Higgins
19
(No Transcript)
20
Outcome
Higgins
21
(No Transcript)
22
What is w and b in the summarization table?

• w is short for within distance
• b is short for between distance

23
What is w and b in the summarization table?

• w is short for within distance
• b is short for between distance

Large within distance Small between distance
Small within distance Large between distance
W/bLarge
w/BSmall
Not agood result!
The desired result!
24
(No Transcript)
25
Homogenization
In order to be able to compare different
variables which have different magnitude of
values.
Patient 1 Age 61 Higgins 7
Patient 2 Age 72 Higgins 14
26
(No Transcript)
27
Automatic cluster
28
Automatic cluster - setup
29
Automatic cluster results
30
Design of user interface
31
Design for usability

• The design process is a constant shifting between
  the following three abilities
• The ability to understand and formulate the
  design problem
• The ability to create design solutions
• The ability to evaluate those solutions

32
How to create premises for the design

• Initial understanding What? Who? Where? Why?
• Studies of literature
• Fields studies
• Increased understanding of What? Who? Where? Why?

33
Field studies

• Contextual research
• Create scenarios
• Design/ Style studies
• Task analysis

34
Qualities in useWhat is good for this type of
system, these users in this context?

• Important qualities and what they are based on
• Aesthetic values the feeling of a trustworthy
  system
• Practical values easy to learn, effective use,
  possibility to abort actions
• Psychological values cognitive ease of use,
  psychological support
• Autonomic values Freedom of choice
• Social values facilitate consent, supporting
  the team mind

35
Design phase

• Sketch, evaluate, comment
• Create paper prototype
• Test paper prototype
• Create computerized prototype
• Test computerized prototype
• Implementation

36
The doctors information tool of the future
might be some sort of combination between the
patient record and the Internet, with the doctor
and the patient positioned together at the
intersection but not having to pay attention to
the technology. (Smith 1996)
37
Database design
38
Layered structure
39
Layered structure
Meta database
Archive database
40
Old database design

• Flat structure (little or no relations)

Patient case
41
New database design

• Relational database design

42
Database design

• Structured Query Language, SQL
• Standard for commercial database managers
• Easy to transfer information to and from the
  database.

43
Database design

• Dynamical structure
• Should be easy to change the type of data that is
  stored in the database
• Support for more than one database in the system
  at once
• The system can be used in parallel for different
  purposes.

44
Database interface

• Database interface specially developed for the
  system
• Easy to read and write information in the
  database.
• Easy to add new tools (Cluster, CBR, ) that
  utilizes the databases.

45
LVAD Outcomes

• Overview of the area functionality, clinical use
  (bridge or destination therapy, continued care),
  types/families of LVAD, short technical
  descriptions and pictures.
• Scenario from start to end. QoL (including cost
  consideration).
• This is focused on the aspects of morbidity and
  mortality. Literature studies.

46
Mortality

• Definitions, surgical perspective on it, heart
  transplant specific aspects and reflection over
  the follow up and waiting time prior to
  transplantation.
• Accepting the 30 days survival as standard. All
  mortality is registered including cause of death.

47
Morbidity

• Complications. Technical and clinical
  complications with reference to device related
  problems.
• Definitions of complications (clear cut and/vs.
  working definitions), motivating the definitions
  used in this research. Addressing verity and
  complexity of definitions.

48
Morbidity

• Motivation or/and pragmatic reasoning about the
  morbidity.
• Research vs. clinical thinking.
• Give better understanding of mechanisms involved
  in order to reduce the incidence (Piccione Jr. W.
  2000).

49
Risk Factors

• Overview of risk factors used within the LVAD
  domain and their usage to assess morbidity and
  mortality.
• Higgins, Euro scores, other systems for risk
  stratification.
• Outlines we have accepted in our research.

50
Patient Selection

• In terms of indications, demographic data,
  selection criteria in use, ethics around it.
• It is of paramount importance to choose patient
  that is appropriate for treatment to succeed.
• (See Left Ventricular Assist, Fraizer, 1997)