process mining thodoros topaloglou daniele barone
TRANSCRIPT
Process Mining
Thodoros TopaloglouDaniele Barone
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
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
Mitigating Potential Bias
• [Explain how potential sources of bias identified in slides 1 and 2 have been mitigated].
• Refer to “Quick Tips” document
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 ImprovingHospital Processes
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• 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
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• 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
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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
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Define
Measure
AnalyseImprove
Control
From Processes to Measuring OutcomesLean meets BPM meets BI
# Metric (units) (definitions)Reference
DatePrevious 7
DaysPrevious 30
Days Baseline Target
1 Total ED visits (#) 130 129.7 129.2 N/A N/A
2 ED visits CTAS I (%) 1.5% 0.6% 0.5% N/A N/A
3 ED visits CTAS II (%) 11.5% 9.7% 10.7% N/A N/A
4 ED visits CTAS III (%) 63.1% 55.5% 55.0% N/A N/A
5 ED visits CTAS IV (%) 23.1% 31.6% 31.3% N/A N/A
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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 performance metrics
Rationale for BI at RVHS
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Relevant, Real-time, Process-driven Metrics
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Clinical activity
Infectioncontrol
Patient care
Financialactivity
Clinical activity
Infectioncontrol
Patient care
Financialactivity
User Driven Business Intelligence
Not everything th
at we ca
n count, “
matters”
T. Topaloglou RVHS Information Management
From Business Objectives to Processes
T. Topaloglou / December 2011 RVHS Business Intelligence Program
HSAAQIPStrategic Plan
CEO PBCs
CorporateScorecard
CorporateScorecard
Corp. Services Acute Care Post-Acute
EDMedicine
PIAAdmit
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
ED LOS < 4hrs
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Actor-Goal-Indicator-Object Diagram
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Connect Strategies to Processes with AGIO
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Patient Flow Process Map
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ED Now Dashboard
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• Process mining aims to discover, monitor, and improve real processes by extracting knowledge from event logs (Van Der Aalst, www.processmining.org)
Process Mining
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Process Mining Tasks
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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
• 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
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• 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
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Visualization of Event Logs
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Action Seq_Num Status Type LocationID RoomID BedID ReasonForVisit Modified_DateINSERTED 1 SCH SDC O YCCL NULL NULL +/- HEART CATH 2013-04-19 15:56:14.570UPDATED 2 PRE SDC O YCCL NULL NULL +/- HEART CATH 2013-04-19 15:59:51.150UPDATED 3 REG SDC O YCCL NULL NULL +/- HEART CATH 2013-04-19 17:06:45.050UPDATED 4 ADM IN I Y9WC Y910 1 PCI 2013-04-19 23:00:32.133UPDATED 5 ADM IN I Y9W Y910M 1 PCI 2013-04-20 10:53:01.400UPDATED 6 ADM IN I Y9W Y928 3 PCI 2013-04-21 12:27:59.420UPDATED 7 ADM IN I Y9WC Y910 2 PCI 2013-04-22 13:48:33.443UPDATED 8 DIS IN I Y9WC Y910 2 PCI 2013-04-23 17:26:41.247
• 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
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