CONTINUOUS CLINICAL PATHWAYS EVALUATION BY USING AUTOMATIC LEARNING ALGORITHMS<br />Carlos Fernández-Llatas, Teresa Meneu,...
Introduction<br />Using Clinical Pathways for Health Care standardization<br />Workflow Technology<br />Activity-Based Pro...
Clinical pathways<br />Formal care protocols<br />Efficient and coordinated use of resources<br />From the evidence based ...
Using Clinical Pathways for Health Care standardization<br /><ul><li>Problems
Complexity
Subjectivity
Adaptation needed
Bureaucratized execution
Without adequate ICT support </li></li></ul><li>Workflow Technology<br />Workflow<br />Theautomation of a business process...
Workflow Technology<br />Workflow<br />Formal description of a process designed to be automated<br />Readable by experts<b...
Workflow Technology<br />Difficulties in the process design<br />Complete and explicit definition needed<br />Differences ...
Process Mining<br />Process Mining<br />Obtain a model from the execution logs<br />Corpus<br />Set of workflow execution ...
Workflow Technology<br />Event Based Process Mining<br />Inference from system events<br />Insufficient for clinical pathw...
Activity-Based Process Mining <br />for Clinical Pathways automatic Learning<br />Activity Based Process Mining<br />Infer...
Activity-Based Process Mining <br />for Clinical Pathways automatic Learning<br /><ul><li>PALIA (Parallel Activity-based L...
Input: Activity based log samples
Output: TPA (Timed Parallel Automaton)
 4 Subalgorithms
Parallel prefix aceptor tree
Parallel Merge
Onward Merge
Elimination of repeated transitions and not useful states</li></ul>Algorithmdesignedwithin Carlos Fernandez’ thesis<br />
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CONTINUOUS CLINICAL PATHWAYS EVALUATION BY USING AUTOMATIC LEARNING ALGORITHMS

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Presentation carried out in Rome the 26th January, 2011 during HEALTHINF-BIOSTEC 2011 about CONTINUOUS CLINICAL PATHWAYS EVALUATION BY USING AUTOMATIC LEARNING ALGORITHMS
Authors: Carlos Fernández-Llatas, Teresa Meneu, Jose Miguel Benedí and Vicente Traver

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CONTINUOUS CLINICAL PATHWAYS EVALUATION BY USING AUTOMATIC LEARNING ALGORITHMS

  1. 1. CONTINUOUS CLINICAL PATHWAYS EVALUATION BY USING AUTOMATIC LEARNING ALGORITHMS<br />Carlos Fernández-Llatas, Teresa Meneu, Jose Miguel Benedí andVicente Traver<br />vtraver@itaca.upv.es<br />
  2. 2. Introduction<br />Using Clinical Pathways for Health Care standardization<br />Workflow Technology<br />Activity-Based Process Mining for Clinical Pathways automatic Learning<br />Clinical Pathways Process Miner<br />Conclusions and Future Work<br />Index<br />
  3. 3. Clinical pathways<br />Formal care protocols<br />Efficient and coordinated use of resources<br />From the evidence based medicine approach<br />Advantages<br />Facilitate the praxis of health professionals<br />Improvement of quality of care<br />Unify criteria<br />Help the administrative management of clinical processes<br />Using Clinical Pathways for Health Care standardization<br />
  4. 4. Using Clinical Pathways for Health Care standardization<br /><ul><li>Problems
  5. 5. Complexity
  6. 6. Subjectivity
  7. 7. Adaptation needed
  8. 8. Bureaucratized execution
  9. 9. Without adequate ICT support </li></li></ul><li>Workflow Technology<br />Workflow<br />Theautomation of a business process, in whole or part, during which documents, information or tasks are passed from one participant to another for action, according to a set of procedural rules<br />Workflow Management Coalition Glossary<br />
  10. 10. Workflow Technology<br />Workflow<br />Formal description of a process designed to be automated<br />Readable by experts<br />Can be automatically executed by using workflow engines<br />Workflows can help Clinical Pathways Standardization <br />
  11. 11. Workflow Technology<br />Difficulties in the process design<br />Complete and explicit definition needed<br />Differences between actual and perceived process<br />Large time process design<br />High knowledge of representation language needed<br />
  12. 12. Process Mining<br />Process Mining<br />Obtain a model from the execution logs<br />Corpus<br />Set of workflow execution logs used to train the model<br />Can be used in an iterative way to refine continuously clinical pathways<br />Evaluation of deployed Clinical Guidelines<br />Human readable and modifiable inferred model<br />
  13. 13. Workflow Technology<br />Event Based Process Mining<br />Inference from system events<br />Insufficient for clinical pathways inference<br />Gettemperature<br />Medicate<br />Yes<br />Fever<br />No<br />Do nothing<br />
  14. 14. Activity-Based Process Mining <br />for Clinical Pathways automatic Learning<br />Activity Based Process Mining<br />Inference from Activities <br /> Corpus with information of start, duration and result of actions<br />07/05/2010 10:22:59 => i:1 BeginAction: Admission<br />07/05/2010 10:34:01 => i:1 EndAction: Admission Res: OK<br />07/05/2010 10:35:03 => i:1 BeginAction: Triage<br />07/05/2010 10:46:06 => i:1 EndAction: Triage Res: InHospital<br />07/05/2010 10:56:07 => i:1 BeginAction: TNS<br />07/05/2010 10:56:08 => i:1 BeginAction: TMP<br />07/05/2010 11:23:10 => i:1 EndAction: TNS Res: OK<br />07/05/2010 11:33:12 => i:1 EndAction: TMP Res: Fever<br />07/05/2010 11:43:19 => i:1 BeginAction: TMP<br />07/05/2010 11:50:19 => i:1 EndAction: TMP Res: OK<br />07/05/2010 11:51:24 => i:1 BeginAction: QualityTest<br />07/05/2010 12:00:28 => i:1 EndAction: QualityTestRes: OK<br />07/05/2010 12:15:29 => i:1 BeginAction: Discharge<br />07/05/2010 12:20:30 => i:1 EndAction: DischargeRes: OK<br />
  15. 15. Activity-Based Process Mining <br />for Clinical Pathways automatic Learning<br /><ul><li>PALIA (Parallel Activity-based Log Inference Algorithm)
  16. 16. Input: Activity based log samples
  17. 17. Output: TPA (Timed Parallel Automaton)
  18. 18. 4 Subalgorithms
  19. 19. Parallel prefix aceptor tree
  20. 20. Parallel Merge
  21. 21. Onward Merge
  22. 22. Elimination of repeated transitions and not useful states</li></ul>Algorithmdesignedwithin Carlos Fernandez’ thesis<br />
  23. 23. TPA (Timed Parallel Automaton)<br />Based on parallel finite automaton approach<br />Alphabet enriched with discrete temporal labels<br />Able to describe complex transitions<br />Description of parallelism and synchronization patterns<br />Double function of transition:<br />g: N+ N+<br />d: Q  Q<br /> where: Q is a set of nodes N <br />Activity-Based Process Mining <br />for Clinical Pathways automatic Learning<br />
  24. 24. Activity-Based Process Mining <br />for Clinical Pathways automatic Learning<br /><ul><li>Parallelprefix aceptor tree</li></li></ul><li>Activity-Based Process Mining <br />for Clinical Pathways automatic Learning<br /><ul><li>ParallelMerge</li></li></ul><li>Activity-Based Process Mining <br />for Clinical Pathways automatic Learning<br /><ul><li>OnwardMerge</li></li></ul><li>Activity-Based Process Mining <br />for Clinical Pathways automatic Learning<br /><ul><li>Eliminate repeated transitions and not useful states</li></li></ul><li>Clinical Pathways Process Miner<br />Tool for Process Mining over deployed Clinical Pathways <br />Analysis of deviations on Clinical Pathways executions<br />Comparison between real execution and designed pathway<br />Discovery of new Clinical Pathways patterns<br />Analysis of Costs produced of Clinical Pathways deviations<br />Cost evaluation of inefficient cases<br />Bottle-necks detection.<br />Currently testing with experts in Heart Cycle project<br />Clinical Pathways Process Miner<br />
  25. 25. ClinicalPathwaysProcessMining Demo<br />
  26. 26.
  27. 27.
  28. 28. Process Mining Techniques can be used to help in the Clinical Pathways design process<br />A Clinical Pathway can be approached in an iterative way<br />Errors and new patterns can be detected and corrected in each iteration<br />The process can be continuously improved designing more and more cost-efficient Clinical Pathways<br />Conclusions<br />
  29. 29. CONTINUOUS CLINICAL PATHWAYS EVALUATION BY USING AUTOMATIC LEARNING ALGORITHMS<br />Carlos Fernández-Llatas, Teresa Meneu, Jose Miguel Benedí andVicente Traver<br />vtraver@itaca.upv.es<br />

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