Transcript Process Mining - e-Health Conference

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
• 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
Understanding and Improving
Hospital Processes
Business Process
Management
• Document and
catalog hospital
processes using
formal, visual
notation like
BPMN
• Actively manage
processes by
measuring their
performance
• Continuously
improve
processes
T. Topaloglou
Business
Intelligence
• Understand
operational
performance by
monitoring
process
execution
• Provide process
and data
visibility to
business users
• Monitor key
performance
metrics
RVHS Information Management
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.
5
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
T. Topaloglou
RVHS Information Management
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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
T. Topaloglou
RVHS Information Management
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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
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RVHS Information Management
lean
Visual modeling
BPMN
8
From Processes to Measuring Outcomes
Lean meets BPM meets BI
Define
Reference
Date
Previous 7
Days
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
6
ED visits CTAS V (%)
0.8%
2.5%
2.4%
N/A
N/A
7 Left Without Being Seen (%)
13.1%
8.8%
7.0%
7.0%
4.0%
8 ED visits admitted (%)
#
Measure
Emergency Department
Control
10.0%
10.5%
10.5%
11.0%
11.0%
4.0
5.0
4.8
5.0
4.5
10
3.4
3.9
3.7
4.0
4.8
ED ALOS for non-admitted patients (hrs)
11
CTAS I-II non-admitted patients with LOS <= 7 hrs (%)
90.0%
88.1%
86.0%
80.0%
75.0%
12
CTAS III non-admitted patients with LOS <= 7 hrs (%)
83.1%
76.8%
79.3%
80.0%
85.0%
13
CTAS IV-V non-admitted patients with LOS <= 4 hrs (%)
90.0%
74.5%
79.6%
80.0%
85.0%
6.6
6.1
6.2
6.0
12.0
ED ALOS for admitted patients (hrs)
15
CTAS I-II admitted patients with LOS <= 8 hrs (%)
42.9%
42.9%
42.3%
45.0%
40.0%
16
CTAS III admitted patients with LOS <= 8 hrs (%)
40.0%
26.2%
22.3%
25.0%
30.0%
17
CTAS IV-V admitted patients with LOS <= 8 hrs (%)
0.0%
0.0%
11.1%
15.0%
20.0%
18 Admitted patients in ED - no IP bed at 06:00 (#)
5.0
3.3
2.7
3.0
3.0
19 IP ALOS (excl. ALC) (days)
4.5
5.0
4.4
4.5
4.0
20 IP Discharges by 11:00 (%)
10.0%
21.2%
19.8%
20.0%
30.0%
21 IP Discharges by 14:00 (%)
10.0%
25.0%
25.0%
20.0%
30.0%
22 IP Discharges (#)
40.0
29.7
31.1
27.0
35.0
23 ALC patients (#)
18.0
21.7
27.1
35.0
30.0
24
IP ALOS (excl. ALC) of patients in Unit 1 (days)
3.8
4.4
5.2
5.0
6.0
25
IP Discharges by 11:00 Unit 1 (%)
0.0%
4.2%
9.5%
20.0%
30.0%
26
IP Discharges by 14:00 Unit 1 (%)
35.7%
35.4%
35.9%
25.0%
27
ALC patients Unit 1 (#)
6.0
6.9
7.5
10
99
28
IP ALOS (excl. ALC) of patients in Unit 2 (days)
4.0
3.9
4.2
4.6
4.0
RVHS Information Management
Admit & Dischg
Analyse
Unit 1
Improve
Previous 30
Days
Baseline Target
9 ED ALOS – all dispositions (hrs)
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T. Topaloglou
Metric (units) (definitions)
30.0%
Rationale for BI at RVHS
Evidence
• Process
owners
need
evidence to
manage
their
business
• Evidence
hides in the
data
T. Topaloglou
Intergration
• Create an
integrated
repository
of operational and
clinical
sources
Access
• Enable
process
owners
(mgrs) to
access
process
data and
gain
insights
RVHS Information Management
Action
• Empower
business
users to
take
actions by
monitoring
process
based
performan
ce metrics
10
Relevant, Real-time, Process-driven Metrics
ID: 16
ER Department
Manager
<responsible for>
Reduce the length of
stay of Emergency
Department CTAS III
admitted patients in
the Emergency
Department
Dimensions
Time
Location
Provider
CTAS
Percentage of
CTAS III
admitted
patients with
Length of Stay
(LOS) equals
or less than 8
hours
User Driven Business
Intelligence
Triage
Assessment
ALC-MED
ALC-CCC
Depart
E
D
Leave the
Emergency
Department
to home
MED
CCC
L
T
C
Home
Clinical
activity
Patient
care
T. Topaloglou
Infectionc Financial
ontrol
activity
Clinical
activity
Patient
care
RVHS Information Management
Infectionc Financial
ontrol
activity
11
From Business Objectives to Processes
Improve access to care
Strategic
Plan
QIP
Corporate
Scorecard
HSAA
CEO
PBCs
ED LOS < 4hrs
Corp. Services
Corporate
Scorecard
Acute Care
Post-Acute
ED LOS < 4hrs
Admit
•
•
•
•
•
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
T. Topaloglou / December 2011
RVHS Business Intelligence Program
ED
ERNI process
PIA
Beds
Medicine
Discharge
process
12
Actor-Goal-Indicator-Object Diagram
T. Topaloglou / December 2011
RVHS Business Intelligence Program
13
Connect Strategies to Processes with AGIO
T. Topaloglou / December 2011
RVHS Business Intelligence Program
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Patient Flow Process Map
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ED Now Dashboard
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Process Mining
• Process mining aims to discover, monitor, and improve real processes by
extracting knowledge from event logs (Van Der Aalst, www.processmining.org)
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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
T. Topaloglou
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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
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RVHS Information Management
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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.
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Visualization of Event Logs
Action
INSERTED
UPDATED
UPDATED
UPDATED
UPDATED
UPDATED
UPDATED
UPDATED
Seq_Num
1
2
3
4
5
6
7
8
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Status
SCH SDC
PRE SDC
REG SDC
ADM IN
ADM IN
ADM IN
ADM IN
DIS IN
Type
O
O
O
I
I
I
I
I
LocationID
YCCL
YCCL
YCCL
Y9WC
Y9W
Y9W
Y9WC
Y9WC
RoomID
NULL
NULL
NULL
Y910
Y910M
Y928
Y910
Y910
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BedID
NULL
NULL
NULL
1
1
3
2
2
ReasonForVisit Modified_Date
+/- HEART CATH 2013-04-19 15:56:14.570
+/- HEART CATH 2013-04-19 15:59:51.150
+/- HEART CATH 2013-04-19 17:06:45.050
PCI
2013-04-19 23:00:32.133
PCI
2013-04-20 10:53:01.400
PCI
2013-04-21 12:27:59.420
PCI
2013-04-22 13:48:33.443
PCI
2013-04-23 17:26:41.247
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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.
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Thank you
[email protected]