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. Business Process Management Document and catalog hospital processes using formal, visual notation like BPMN Actively manage processes by measuring their performance Continuously improve processes Business Intelligence Understand operational performance by monitoring process execution Provide process and data visibility to business users Monitor key performance metrics Process Mining A deeper dive into process execution to learn the structure of processes. Find the processes or sub-processes that really get executed vs. what thought to be executed. Understanding and Improving Hospital Processes T. Topaloglou5RVHS Information Management

6. 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 Talk Objective T. Topaloglou6RVHS Information Management

7. 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 Rouge Valley Health System 7T. TopaloglouRVHS Information Management

8. Business Process Management 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 8 lean Visual modeling BPMN T. TopaloglouRVHS Information Management

9. DefineMeasureAnalyseImproveControl From Processes to Measuring Outcomes Lean meets BPM meets BI 9T. TopaloglouRVHS Information Management

10. Evidence Process owners need evidence to manage their business Evidence hides in the data Intergration Create an integrated repository of opera- tional and clinical sources Access Enable process owners (mgrs) to access process data and gain insights Action Empower business users to take actions by monitoring process based performan ce metrics Rationale for BI at RVHS T. Topaloglou10RVHS Information Management

11. Relevant, Real-time, Process-driven Metrics 11 User Driven Business Intelligence Not everything that we can count, “matters” T. TopaloglouRVHS Information Management

12. From Business Objectives to Processes T. Topaloglou / December 2011RVHS Business Intelligence Program HSAA QIP Strategic Plan CEO PBCs Corporate Scorecard Corporate Scorecard Corp. ServicesAcute CarePost-Acute ED Medicine PIA Admit Beds Discharge process ERNI process 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 Improve access to care ED LOS < 4hrs 12

13. Actor-Goal-Indicator-Object Diagram T. Topaloglou / December 201113RVHS Business Intelligence Program

14. Connect Strategies to Processes with AGIO T. Topaloglou / December 201114RVHS Business Intelligence Program

15. Patient Flow Process Map T. Topaloglou15RVHS Information Management

16. ED Now Dashboard T. Topaloglou16RVHS Information Management

17. Process mining aims to discover, monitor, and improve real processes by extracting knowledge from event logs (Van Der Aalst, www.processmining.org) Process Mining T. Topaloglou17RVHS Information Management

18. Process Mining Tasks T. Topaloglou18RVHS Information Management 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

19. 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 Process Mining in Healthcare T. Topaloglou19RVHS Information Management

20. 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. Practical Process Mining T. Topaloglou20RVHS Information Management

21. Visualization of Event Logs T. Topaloglou21RVHS Information Management ActionSeq_NumStatusTypeLocationIDRoomIDBedIDReasonForVisitModified_Date INSERTED1SCH SDCOYCCLNULLNULL+/- HEART CATH2013-04-19 15:56:14.570 UPDATED2PRE SDCOYCCLNULLNULL+/- HEART CATH2013-04-19 15:59:51.150 UPDATED3REG SDCOYCCLNULLNULL+/- HEART CATH2013-04-19 17:06:45.050 UPDATED4ADM INIY9WCY9101PCI2013-04-19 23:00:32.133 UPDATED5ADM INIY9WY910M1PCI2013-04-20 10:53:01.400 UPDATED6ADM INIY9WY9283PCI2013-04-21 12:27:59.420 UPDATED7ADM INIY9WCY9102PCI2013-04-22 13:48:33.443 UPDATED8DIS INIY9WCY9102PCI2013-04-23 17:26:41.247

22. 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. The Future of Process Mining T. Topaloglou22RVHS Information Management

23. ttopaloglou@rougevalley.ca Thank you