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Process Mining

Process Mining. Thodoros Topaloglou Daniele Barone. Faculty/Presenter Disclosure. Faculty: Thodoros Topaloglou Relationships with commercial interests: Grants/Research Support: NSERC Discovery Grant (2006-12), PI

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Process Mining

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  1. Process Mining Thodoros Topaloglou Daniele Barone

  2. Faculty/Presenter Disclosure • Faculty: Thodoros Topaloglou • Relationships with commercial interests: • Grants/Research Support: • NSERC Discovery Grant (2006-12), PI • NSERC Strategic Network Grant: Business Intelligence Network (2008-2014), Co-PI • Speakers Bureau/Honoraria: None • Consulting Fees: None • Other: Employee of Rouge Valley Health System

  3. Disclosure of Commercial Support This program has NOT received financial support from any Commercial Organization This program has NOT received in-kind support from any Commercial Organization Potential for conflict(s) of interest: None

  4. Mitigating Potential Bias • [Explain how potential sources of bias identified in slides 1 and 2 have been mitigated]. • Refer to “Quick Tips” document

  5. Understanding and ImprovingHospital Processes RVHS Information Management

  6. Talk Objective • The objective of this presentation is to discuss how to “understand” processes by pairing process models and data • I will also share an experience-report from the Rouge Valley Health System’s (RVHS) journey to support process based performance management through two transformative initiatives • Business process management • Enterprise business intelligence and review some of our early efforts on process mining RVHS Information Management

  7. Rouge Valley Health System • RVHS is a two site hospital with 479 beds serving the East GTA community • Key facts • 2700 employees • Over 500 physicians and 1000 nurses • 122,000 ED visits in 2012-13 • 26,000 admissions • 25,000 surgeries • 3,700 births • over 189,000 clinic visits • Has a corporate performance mgmt framework and corporate scorecard • Has adopted Lean as a management and quality improvement philosophy • In 2010-11, RVHS launched two transformative IT initiatives to • create a competency center in business process management, and • develop an enterprise Business Intelligence system RVHS Information Management

  8. Business Process Management lean Visual modeling BPMN If you cannot measure a process you cannot improve it But… if you cannot “see” it you cannot measure it! A visual notation that business and clinical users can understand RVHS Information Management

  9. From Processes to Measuring OutcomesLean meets BPM meets BI RVHS Information Management

  10. Rationale for BI at RVHS RVHS Information Management

  11. Relevant, Real-time, Process-driven Metrics • User Driven Business Intelligence ALC-MED ALC-CCC ED MED CCC LTC Home Not everything that we can count, “matters” Clinical activity Patient care Infectioncontrol Financial activity Clinical activity Patient care Infectioncontrol Financial activity RVHS Information Management

  12. From Business Objectives to Processes • Improve access to care • Corporate • Scorecard Strategic Plan QIP HSAA CEO PBCs • Corp. Services • Acute Care • Post-Acute • Corporate • Scorecard • ED LOS < 4hrs • ED LOS < 4hrs • Admit • PIA • Beds • BI supports business goals • Series of linked & cascading scorecards • Scorecards as collections of metrics • Metrics depend on other metrics or process KPIs • Linking processes performance to metrics • ED • Medicine • ERNI process • Discharge process RVHS Business Intelligence Program

  13. Actor-Goal-Indicator-Object Diagram RVHS Business Intelligence Program

  14. Connect Strategies to Processes with AGIO RVHS Business Intelligence Program

  15. Patient Flow Process Map RVHS Information Management

  16. ED Now Dashboard RVHS Information Management

  17. Process Mining Process mining aims to discover, monitor, and improve real processes by extracting knowledge from event logs (Van Der Aalst, www.processmining.org) RVHS Information Management

  18. Process Mining Tasks Wil Van Der Aalst. 2012. Process mining. Commun. ACM 55, 8 (August 2012), 76-83. DOI=10.1145/2240236.2240257 http://doi.acm.org/10.1145/2240236.2240257 RVHS Information Management

  19. Process Mining in Healthcare • Event logs • ADT and Order Entry applications are rich sources of events • Process complexity • Many sources of variations • by performer, by case/patient, or practice variation. • BI applications intend to monitor variation • Process hierarchies • Multiple levels of process-subprocess relationships • BI applications typically focus on higher level processes • Process pools • There are multiple processes or initiatives active at any time • Many process metrics measure aggregate effects RVHS Information Management

  20. Practical Process Mining • Process signatures are distinct data markers that correspond to execution (or not) of specific processes • e.g, CTAS 4-5 patients in the range 8-24 indicate non-departed charts! • Queries for presence of specific sequence of events in transaction (event) logs or data warehouses • if we know what we are looking for we can find it! • Abnormal results • We found that ALC designation is performed differently between sites (practice variation) because the calculated metrics didn’t match • By visualizing data and searching for patterns that can be process signatures and then find matches for these signatures • Through process mining we were able to reverse engineer actual processes and found activities in the logs were redundant e.g, not all clinic visits have to be scheduled before registered. RVHS Information Management

  21. Visualization of Event Logs Action Seq_Num Status Type LocationIDRoomIDBedIDReasonForVisitModified_Date INSERTED 1 SCH SDC O YCCL NULL NULL +/- HEART CATH 2013-04-19 15:56:14.570 UPDATED 2 PRE SDC O YCCL NULL NULL +/- HEART CATH 2013-04-19 15:59:51.150 UPDATED 3 REG SDC O YCCL NULL NULL +/- HEART CATH 2013-04-19 17:06:45.050 UPDATED 4 ADM IN I Y9WC Y910 1 PCI 2013-04-19 23:00:32.133 UPDATED 5 ADM IN I Y9W Y910M 1 PCI 2013-04-20 10:53:01.400 UPDATED 6 ADM IN I Y9W Y928 3 PCI 2013-04-21 12:27:59.420 UPDATED 7 ADM IN I Y9WC Y910 2 PCI 2013-04-22 13:48:33.443 UPDATED 8 DIS IN I Y9WC Y910 2 PCI 2013-04-23 17:26:41.247 RVHS Information Management

  22. The Future of Process Mining Discover process flows from even logs (Van Der Aalst) Discover BPMN from event logs or database tables (exploit richer data semantics) Data mining of event logs for similar patterns (process signatures), and further discovery of process flows within pattern clusters Process mining is the combination of data mining and business process management, and very much an active research field with tremendous potential in helping healthcare organization understand their processes. RVHS Information Management

  23. Thank you ttopaloglou@rougevalley.ca

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