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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

Process Mining

Thodoros Topaloglou

Daniele Barone

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

rouge valley health system
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

business process management
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

rationale for bi at rvhs
Rationale for BI at RVHS

RVHS Information Management

relevant real time process driven m etrics
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

from business objectives to processes
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

actor goal indicator object diagram
Actor-Goal-Indicator-Object Diagram

RVHS Business Intelligence Program

connect strategies to processes with agio
Connect Strategies to Processes with AGIO

RVHS Business Intelligence Program

patient flow process map
Patient Flow Process Map

RVHS Information Management

ed now dashboard
ED Now Dashboard

RVHS Information Management

process mining1
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

process mining tasks
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

process mining in healthcare
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

practical process mining
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

visualization of event logs
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

the future of process mining
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

thank you
Thank you

ttopaloglou@rougevalley.ca