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. CONTINUOUS CLINICAL PATHWAYS EVALUATION BY USING AUTOMATIC LEARNING ALGORITHMS Carlos Fernández-Llatas, Teresa Meneu, Jose Miguel Benedí andVicente Traver vtraver@itaca.upv.es
- 2. Introduction Using Clinical Pathways for Health Care standardization Workflow Technology Activity-Based Process Mining for Clinical Pathways automatic Learning Clinical Pathways Process Miner Conclusions and Future Work Index
- 3. Clinical pathways Formal care protocols Efficient and coordinated use of resources From the evidence based medicine approach Advantages Facilitate the praxis of health professionals Improvement of quality of care Unify criteria Help the administrative management of clinical processes Using Clinical Pathways for Health Care standardization
- 5. Complexity
- 6. Subjectivity
- 10. Workflow Technology Workflow Formal description of a process designed to be automated Readable by experts Can be automatically executed by using workflow engines Workflows can help Clinical Pathways Standardization
- 11. Workflow Technology Difficulties in the process design Complete and explicit definition needed Differences between actual and perceived process Large time process design High knowledge of representation language needed
- 12. Process Mining Process Mining Obtain a model from the execution logs Corpus Set of workflow execution logs used to train the model Can be used in an iterative way to refine continuously clinical pathways Evaluation of deployed Clinical Guidelines Human readable and modifiable inferred model
- 13. Workflow Technology Event Based Process Mining Inference from system events Insufficient for clinical pathways inference Gettemperature Medicate Yes Fever No Do nothing
- 14. Activity-Based Process Mining for Clinical Pathways automatic Learning Activity Based Process Mining Inference from Activities Corpus with information of start, duration and result of actions 07/05/2010 10:22:59 => i:1 BeginAction: Admission 07/05/2010 10:34:01 => i:1 EndAction: Admission Res: OK 07/05/2010 10:35:03 => i:1 BeginAction: Triage 07/05/2010 10:46:06 => i:1 EndAction: Triage Res: InHospital 07/05/2010 10:56:07 => i:1 BeginAction: TNS 07/05/2010 10:56:08 => i:1 BeginAction: TMP 07/05/2010 11:23:10 => i:1 EndAction: TNS Res: OK 07/05/2010 11:33:12 => i:1 EndAction: TMP Res: Fever 07/05/2010 11:43:19 => i:1 BeginAction: TMP 07/05/2010 11:50:19 => i:1 EndAction: TMP Res: OK 07/05/2010 11:51:24 => i:1 BeginAction: QualityTest 07/05/2010 12:00:28 => i:1 EndAction: QualityTestRes: OK 07/05/2010 12:15:29 => i:1 BeginAction: Discharge 07/05/2010 12:20:30 => i:1 EndAction: DischargeRes: OK
- 16. Input: Activity based log samples
- 17. Output: TPA (Timed Parallel Automaton)
- 18. 4 Subalgorithms
- 19. Parallel prefix aceptor tree
- 20. Parallel Merge
- 21. Onward Merge
- 22. Elimination of repeated transitions and not useful statesAlgorithmdesignedwithin Carlos Fernandez’ thesis
- 23. TPA (Timed Parallel Automaton) Based on parallel finite automaton approach Alphabet enriched with discrete temporal labels Able to describe complex transitions Description of parallelism and synchronization patterns Double function of transition: g: N+ N+ d: Q Q where: Q is a set of nodes N Activity-Based Process Mining for Clinical Pathways automatic Learning
- 28. Process Mining Techniques can be used to help in the Clinical Pathways design process A Clinical Pathway can be approached in an iterative way Errors and new patterns can be detected and corrected in each iteration The process can be continuously improved designing more and more cost-efficient Clinical Pathways Conclusions
- 29. CONTINUOUS CLINICAL PATHWAYS EVALUATION BY USING AUTOMATIC LEARNING ALGORITHMS Carlos Fernández-Llatas, Teresa Meneu, Jose Miguel Benedí andVicente Traver vtraver@itaca.upv.es