enhancing medical evidence discovery through interactive pattern recognition and process mining

ENHANCING MEDICAL EVIDENCE DISCOVERY THROUGH INTERACTIVE PATTERN

RECOGNITION

V. TRAVER, A. MARTINEZ- ROMERO, J.L. BAYO, P. SALA, P. CARVALHO, J. HENRIQUES, M. G. RUANO, A. BIANCHI, C. FERNÁNDEZ- LLATAS

Madrid, 8th April 2016

Corresponding author: Vicente Traver [email protected]

Surrounded by heterogenous clinical data, medical staff needs to takethe right decision supported by medical evidence

Translational research is now happening in the healthfield, making use of business processes and workflows

in daily practice

Are we using evidence based medicine to take our decisions?

How are we defining our clinical processes? Is it done univocally?

Are the clinical pathways the solution? Are they understandableby the clinicians?

Are they used in real clinical practice?

Using Workflows to describe Clinical Pathways• Facilitate the praxis of health professionals• Improvement of quality of care• Unify criteria• Help the administrative management of clinical processes

DESIGN OF CLINICAL PATHWAYS

• Automation and Traceability• Measure and OptimizationProcess

Standardization

• Complete, Unambiguous formal processes • Expressivity Vs Understandability

Workflows as automation Language

• Complete and explicit definition needed• Differences between actual and perceived process• High timing consuming• High knowledge of representation language

needed

Difficulties in the process design

using workflows

INTERACTIVE PATTERN RECOGNITION

PROCESS MINING

• Not possible from typical databasesObtain a model from the execution logs

• Set of workflow execution logs used to train the modelCorpus

• Parallel Activity-based Log Inference Algorithm (PALIA)

Our Vision: Activity-Based Workflow Mining

…27/05/2013 19:31:18.65 => i:9 Fin Accion: FirstTriageResult Res: ACUTE27/05/2013 19:31:18.68 => i:9d4fcabf Nodo: Get BP -> InicioAccion: getBloodPressure27/05/2013 19:31:18.69 => i:9d4fcabf Nodo: Get BP -> InicioReloj: clk427/05/2013 19:31:18.71 => i:9d4fcabf Nodo: Get Temp -> InicioAccion: getTemperature27/05/2013 19:31:18.72 => i:9d4fcabf Nodo: Get Temp -> InicioReloj: clk327/05/2013 19:31:18.83 => i:8229056d Fin Accion: FirstTriageResult Res: ACUTE27/05/2013 19:31:18.88 => i:8229056d Nodo: Get BP -> InicioAccion: getBloodPressure27/05/2013 19:31:18.90 => i:8229056d Nodo: Get BP -> InicioReloj: clk427/05/2013 19:31:18.91 => i:8229056d Nodo: Get Temp -> InicioAccion: getTemperature27/05/2013 19:31:18.93 => i:8229056d Nodo: Get Temp -> InicioReloj: clk327/05/2013 19:31:19.47 => i:1a5c92f6 Fin Accion: getTemperatureResult Res: FEVER27/05/2013 19:31:20.19 => i:aa3380da Fin Accion: getTemperatureResult Res: OK27/05/2013 19:31:20.27 => i:aa3380da Nodo: TEMP OK -> InicioAccion: 27/05/2013 19:31:20.38 => i:aa3380da Fin Accion: getBloodPressureResult Res: OK27/05/2013 19:31:20.40 => i:aa3380da Nodo: BP OK -> InicioAccion: 27/05/2013 19:31:20.41 => i:aa3380da Nodo: Quality Test -> InicioAccion: QualityTest…

PROCESS MINING

Process Mining can infer the real deployment of processes• Computer Aided Design of Clinical Pathways• Clinical Pathways tracing • Detect BottleNecks• Cost Effectiveness study of Processes• Measure adherence of Clinical Pathways instances

Processes Deployment Support

Individualized behavior Modeling

PROCESS MINING LIFECYCLE

Discovery

Conformance

EnhancementAnalysis of processes through Process Mining

…27/05/2013 19:31:18.65 => i:9 Fin Accion: FirstTriageResult Res: ACUTE27/05/2013 19:31:18.68 => i:9d4fcabf Nodo: Get BP -> InicioAccion: getBloodPressure27/05/2013 19:31:18.69 => i:9d4fcabf Nodo: Get BP -> InicioReloj: clk427/05/2013 19:31:18.71 => i:9d4fcabf Nodo: Get Temp -> InicioAccion: getTemperature27/05/2013 19:31:18.72 => i:9d4fcabf Nodo: Get Temp -> InicioReloj: clk327/05/2013 19:31:18.83 => i:8229056d Fin Accion: FirstTriageResult Res: ACUTE27/05/2013 19:31:18.88 => i:8229056d Nodo: Get BP -> InicioAccion: getBloodPressure27/05/2013 19:31:18.90 => i:8229056d Nodo: Get BP -> InicioReloj: clk427/05/2013 19:31:18.91 => i:8229056d Nodo: Get Temp -> InicioAccion: getTemperature27/05/2013 19:31:18.93 => i:8229056d Nodo: Get Temp -> InicioReloj: clk327/05/2013 19:31:19.47 => i:1a5c92f6 Fin Accion: getTemperatureResult Res: FEVER27/05/2013 19:31:20.19 => i:aa3380da Fin Accion: getTemperatureResult Res: OK27/05/2013 19:31:20.27 => i:aa3380da Nodo: TEMP OK -> InicioAccion: 27/05/2013 19:31:20.38 => i:aa3380da Fin Accion: getBloodPressureResult Res: OK27/05/2013 19:31:20.40 => i:aa3380da Nodo: BP OK -> InicioAccion: 27/05/2013 19:31:20.41 => i:aa3380da Nodo: Quality Test -> InicioAccion: QualityTest…

CONCLUSIONS AND NEXT STEPS

Major challenges to speed up scalability

•Integration with legacy systems•Ease of use•Awareness about possibilities in

clinical daily practice

ENHANCING MEDICAL EVIDENCE DISCOVERY THROUGH INTERACTIVE PATTERN

RECOGNITIONV. TRAVER, A. MARTINEZ- ROMERO, J.L. BAYO, P. SALA, P. CARVALHO, J.

HENRIQUES, M. G. RUANO, A. BIANCHI, C. FERNÁNDEZ- LLATAS

Madrid, 8th April 2016

Corresponding author: Vicente Traver [email protected]

THANKS FOR YOUR ATTENTION

enhancing medical evidence discovery through interactive pattern recognition and process mining

Healthcare