AssistMe Project leaders Ankica Babic, Urban Lönn, Henrik Casimir Ahn

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?

Problem solving 2 Actively involve the physicians in design, implementation, and evaluation of our web based system. Clinical evaluation of extracted knowledge.

System overview

Start page

Homepage for patients

Questionnaires

Homepage for physicians

Add patient cases

Case based reasoning (result)

Case based reasoning (patient case)

Cluster analysis - introduction

Cluster analysis

Calculates the equality/difference between patients

kg years a a = age difference = 40 years b b = weight difference = 30kg c c = “distance” between patients The difference is: 50 Example: Calculation of difference using age and weight:

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.

Clusters (former page) Age Higgins

Outcome Age 0,87 0,67 1,0 0,5 1,0 0,63 Higgins

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 W/b=Large Not a good result!

Large within distance Small between distance Small within distance Large between distance W/b=Largew/B=Small Not a good result! The desired result! What is w and b in the summarization table? w is short for “within distance” b is short for “between distance”

Homogenization In order to be able to compare different variables which have different magnitude of values Age ,50 0,43 0,57 0,32 c Higgins Patient 1: Age 61; Higgins ,82 Patient 2: Age 72; Higgins 14

Automatic cluster

Automatic cluster - setup

Automatic cluster – results

Design of user interface

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

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?

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

Qualities in use What 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”

Design phase Sketch, evaluate, comment Create paper prototype Test paper prototype Create computerized prototype Test computerized prototype Implementation

“ The doctor’s 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)

Database design

Layered structure Application (AssistMe) Database manager / system Database interface

Layered structure Java code of AssistMe Patient cases Meta database Archive database... Database interface in Java

Patient case Old database design Flat structure (little or no relations) Data

Discharge PostOp New database design Relational database design Data Demografi PreOp PerOp Rel Data

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

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.

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.

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.

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.

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.

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).

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.

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)