Presentation done by Vicente Traver at the IEEE EMBC 2015 conference in Milano. Corresponding author of this work is Carlos Fernandez (cfllatas@itaca.upv.es)

1. Innovaciones Tecnologías para la Salud y el Bienestar
MIlano, 26th August 2015
Diabetes care related process modelling using
Process Mining techniques. Lessons learned in
the application of Interactive Pattern
Recognition:
coping with the Spaghetti Effect
Carlos Fernandez-Llatas, Antonio Martinez-Millana, Alvaro
Martinez-Romero, Jose Miguel Benedí and Vicente Traver

2. Where is the pain?
- The worldwide prevalence of Diabetes was
estimated around 8.3% among adults between
20-80*
- Self management improves quality of life of
patients
- Personalisation requires evidence (EBM)
- Evidence needs to be extracted from available
data to suggest/define new care processes/Life
Assistance Protocols for diabetes management
* L. Guariguata, D. R. Whiting, I. Hambleton, J. Beagley, U. Linnenkamp, and J. E. Shaw. Global estimates of diabetes
prevalence for 2013 and projections for 2035. Diabetes Research and Clinical Practice, 103(2):137–149, February
2014

3. Need to design new care processes /Life Assistance Protocols
Process standardization
Automation and traceability
Measure and optimization
Workflows as automation language
Complete, unambiguous formal processes
Expressivity vs understandability
Difficulties in the process design using workflows
Complete and explicit definition needed
Differences between current and perceived process
High time consuming
Deep knowledge of representation language

4. Interactive Pattern Recognition

5. Process Mining
Process Mining
Obtain a model from the execution logs
Corpus
Set of workflow execution logs used to train the model
Our Vision: Activity-Based Workflow Mining
Parallel Activity-based Log Inference Algorithm (PALIA)
…
27/05/2013 19:31:18.65 => i:9 Fin Accion: FirstTriageResult Res: ACUTE
27/05/2013 19:31:18.68 => i:9d4fcabf Nodo: Get BP -> InicioAccion: getBloodPressure
27/05/2013 19:31:18.69 => i:9d4fcabf Nodo: Get BP -> InicioReloj: clk4
27/05/2013 19:31:18.71 => i:9d4fcabf Nodo: Get Temp -> InicioAccion: getTemperature
27/05/2013 19:31:18.72 => i:9d4fcabf Nodo: Get Temp -> InicioReloj: clk3
27/05/2013 19:31:18.83 => i:8229056d Fin Accion: FirstTriageResult Res: ACUTE
27/05/2013 19:31:18.88 => i:8229056d Nodo: Get BP -> InicioAccion: getBloodPressure
27/05/2013 19:31:18.90 => i:8229056d Nodo: Get BP -> InicioReloj: clk4
27/05/2013 19:31:18.91 => i:8229056d Nodo: Get Temp -> InicioAccion: getTemperature
27/05/2013 19:31:18.93 => i:8229056d Nodo: Get Temp -> InicioReloj: clk3
27/05/2013 19:31:19.47 => i:1a5c92f6 Fin Accion: getTemperatureResult Res: FEVER
27/05/2013 19:31:20.19 => i:aa3380da Fin Accion: getTemperatureResult Res: OK
27/05/2013 19:31:20.27 => i:aa3380da Nodo: TEMP OK -> InicioAccion:
27/05/2013 19:31:20.38 => i:aa3380da Fin Accion: getBloodPressureResult Res: OK
27/05/2013 19:31:20.40 => i:aa3380da Nodo: BP OK -> InicioAccion:
27/05/2013 19:31:20.41 => i:aa3380da Nodo: Quality Test -> InicioAccion: QualityTest
…

6. Uses of Process Mining
Using workflows to describe Clinical Pathways/Care processes
Facilitate the praxis of health professionals
Improvement of quality of care
Unify criteria
Help the administrative management of clinical processes
Process Mining can infer the real deployment of processes
Computer Aided Design of Clinical Pathways
Clinical Pathways tracing
Detect bottlenecks
Cost effectiveness study of care processes
Measure adherence of Clinical Pathways instances
Processes deployment support
Individualized behavior modeling

7. Process Mining Lifecycle

8. Spaghetti Effect
This is not useful at all!

9. Spaghetti Effect
This is not useful at all!

10. Recommended practices: Using Space-Efficient Structures
Petri net
Determinate Finite
Automation DFA

11. Recommended practices: Time Abstractions
Use of discrete time variables to reduce Spaghetti effect

12. Recommended practices: Rendering Algorithms
Based on different profiles/priorities

13. Recommended practices: Clustering
Showing the different paths and frequencies to know more
about the problem and find a better solution

14. Recommended practices: Workflow layout and navigation

15. Discussion/Conclusions
Process Mining can be used to support clinical pathways
design and traceability
The Spaguetti Effect is a well known problem that affects
critically the application of process mining to health
The use of such recommended practices allows a
mitigation of the spaguetti effect making process mining
techniques usable in clinical scenarios
The application of Interactive Pattern Recognition Technologies in
the continuous following of patients with diabetes could be a very
interesting alternative to manual creation of Clinical Pathways (or
Life Assistance Protocols) offering not only a way to support the
design of those protocols but also support the deployment of
these protocols, offering a way to continuously improve them and
tracking the patient throughout all the process.

16. Innovaciones Tecnologías para la Salud y el Bienestar
Vicente Traver @vtraver
vtraver@itaca.upv.es
MIlano, 26th August 2015
Diabetes care related process modelling using Process
Mining techniques. Lessons learned in the application of
Interactive Pattern Recognition: coping with the
Spaghetti Effect
Questions ?