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1
Push Not Pull: Using Data Science to Improve OR Operations
Sanjeev Agrawal, President and CMO, LeanTaas
Session 198, March 8, 2018
Ashley Walsh, Former Perioperative Business Manager, UCHealth
2
Sanjeev Agrawal
Ashley Walsh
Have no real or apparent conflicts of interest to report.
Conflict of Interest
3
Agenda
• The Difference Between Pull and Push
• Real-World “Push” Examples
• Why Push not Pull?
• EHRs and Dashboards Are Mostly “Push” Based
• Key Issues UCHealth Identified in Early 2016 as Solvable Using Data Science
• Allocating Assets Based on Actual Use
• Anomaly and trend Detection
• Reinforcing Positive Trends with Alerts
• How UCHealth Pushes
• OR Time Exchange: Surgeon and Scheduler Block Release & Request
• UCHealth Example – Service Line Level Forecast To Allocate Blocks
4
Learning Objectives
• Explain why push is preferred over pull
• Discuss how predictive analytics, machine learning and mobile
technologies were used at UCHealth to improve OR operations
• Identify the most meaningful metrics for hospital OR operations
5
The Difference Between Pull and Push
Pull – Data applications are queried
and the results manually assembled
into a report that is printed and/or
distributed via email or fax. Additional
analyses require new report
compilation.
Push – Data applications distribute data relevant to
each recipient automatically via scheduled, automatic
communications. Recipients can interact with the data.
iPhone 6
375 x 667px
iqueue.com/release
iPhone 6
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Supply Chain – Projected demand determines what enters the pipeline and when.
• For example, warm jackets get
pushed to clothing retailers as
summer ends and the fall and
winter seasons start.
Community based traffic and
navigation app
• Traffic issues – as
experienced and logged by
users – pushed to drivers
traveling the affected roads
Apps and OS’s
Updates are sent and
the user notified
Travel notifications
Delays, gate changes,
etc. sent via text.
Real-World “Push” Examples
7
Why Push not Pull?
• Pull based reports often “admire the problem” – what happened, perhaps why it
happened but often not “what can I do now?”
• Push-based “In the moment” alerts can drive the right behavior when needed e.g.
“your utilization is falling, your turnover time is increasing”, “you should consider
releasing your time” etc.
• Administrators can spend an inordinate amount of time creating and pushing
historical reports no one reads or worse – no one believes.
• Too time-consuming for physicians to wade through a full report pushed to them to
find their metrics and then not being able to do much about it
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EHRs and Dashboards Are Mostly
“Push” Based
Descriptive Analytics‘What happened?’
# of Cases, On time start %,
Turnover Time, Block Utilization
Understanding profitability of
surgical cases and
opportunitie
s
to improve
contribution margin.
Identify underutili zed blocks,
project utili zation of new blocks,
and provide objective insights
for reallocation .
Establishing targets for
improving revenue per OR
minute by forecastin
g
dem and.
Diagnostic Analytics‘W hy did it happen?’
Predictive Analytics‘W hat will happen?’
Prescriptive Analytics‘How can we make it happen?’
THE OPPORTUNITY
Descriptive Analytics‘What happened?’
# of Cases, On time start %,
Turnover Time, Block Utilization
Understanding profitability of
surgical cases and
opportunitie
s
to improve
contribution margin.
Identify underutili zed blocks,
project utili zation of new blocks,
and provide objective insights
for reallocation .
Establishing targets for
improving revenue per OR
minute by forecastin
g
dem and.
Diagnostic Analytics‘W hy did it happen?’
Predictive Analytics‘W hat will happen?’
Prescriptive Analytics‘How can we make it happen?’
THE OPPORTUNITY
Descriptive Analytics‘What happened?’
# of Cases, On time start %,
Turnover Time, Block Utilization
Understanding profitability of
surgical cases and
opportunitie
s
to improve
contribution margin.
Identify underutili zed blocks,
project utili zation of new blocks,
and provide objective insights
for reallocation .
Establishing targets for
improving revenue per OR
minute by forecastin
g
dem and.
Diagnostic Analytics‘W hy did it happen?’
Predictive Analytics‘W hat will happen?’
Prescriptive Analytics‘How can we make it happen?’
THE OPPORTUNITY
Descriptive Analytics“What happened?”
Diagnostic Analytics“Why did it happen?”
Predictive Analytics“What will happen?”
Prescriptive Analytics“How can we make it happen?”
Minutes used, block
utilization, case
volume, FCOTS, TOT
Reasons for delays,
revenue and cost per
case, historical
profitability and trends
Identify and forecast
patterns of
underutilization, likely
need for OR time to fairly
and transparently re-
allocate time time and
encourage releases
Improve minutes used,
block utilization, case
volume, revenue and
profits
Data Science
Descriptive Analytics‘What happened?’
# of Cases, On time start %,
Turnover Time, Block Utilization
Understanding profitability of
surgical cases and
opportunitie
s
to improve
contribution margin.
Identify underutili zed blocks,
project utili zation of new blocks,
and provide objective insights
for reallocation .
Establishing targets for
improving revenue per OR
minute by forecastin
g
dem and.
Diagnostic Analytics‘W hy did it happen?’
Predictive Analytics‘W hat will happen?’
Prescriptive Analytics‘How can we make it happen?’
THE OPPORTUNITY
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Key Issues UCHealth Identified in Early
2016 as Solvable Using Data Science
5. Making the metrics more credible
4. Increasing transparency and fairness
3. Accommodating new surgeons
1. OR Access for surgeons
7. Cost per case
2. Accommodating rising case volume
6. improving access and availability of metrics
10
Hospital Scheduling Systems Are Built
on a Weak Mathematical Foundation
• Pooling capacity vs. “Reserved
Allocations
• Allocate assets based on actual use,
not by “birthright”. Keep stakeholders
apprised of utilization via weekly
push communications
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Allocating Assets Based on Actual Use
• Overall data and surgeon details
• Facilitate “hallway” conversations
• Always fact-based
• Timely data for the most recent period
• Any week, any month
12
Anomaly and Trend Detection
A turnover increase alert occurred between June 2016 and July 2016 in PPMP.
Turnovers are likely to increase in the following months.
24hr Cancellation Ratio:
Past 13 Weeks Trend for PPMPAverage Turnover rate for PPMP
13
Reinforcing Positive Trends with Alerts
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Hospital Scheduling Systems Are Built
on a Weak Mathematical Foundation
• Limited visibility makes scheduling
and managing changes time-
consuming and ineffective.
• Use mobile technologies to reserve
OR time like OpenTable and push
reminders.
15
How UCHealth Pushes
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iqueue.com/release
iPhone 6
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Proactive text Drive action (one click release)
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OR Time Exchange: Surgeon and
Scheduler Block Release & Request
• Can be done by surgeons or schedulers on
mobile or web
• Smoother communication
• Fewer emails
• More blocks “saved”
• More cases done
• Shorter wait time for patients
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Results - $400K/OR/year, Much Higher
Surgeon Satisfaction
Across the system:
• ~2000 blocks released
• ~ 1300 blocks requested
• Release lead time over 27 days
Release Reminders Launched – December 16 MBE Launched – June 16 Wishlist Launched – May 17
• 11 new surgeons added without permanent block assignment
• 4%+ improvement in block utilization
• Fewer add-ons
• Higher surgeon and scheduler satisfaction
• $400K/OR/year benefit
18
Hospital Scheduling Systems Are Built on a
Weak Mathematical Foundation
• Scheduling of complex events
HAS to use probability
• Use machine learning to forecast
block allocations
19
UCHealth Example – Service Line Level Forecast To Allocate Blocks
At Service Line Level Based On Usage (Not Including Trauma)Specialty
Allocated
Blocks
Suggestion Based on
Forecast & Volume/Volatility Model
2017
Q1 Actual
Service Line TotalIndividual
Surgeon Blocks
Service Line
Open BlocksBlock Needed Abandon Blocks
Cardiothoracic Surgery 193 165 152 13 131 22
General Surgery 345 287 235 52 243 19.5
General Surgery_trauma 64 64 64 0 50
Neurosurgery 270 217 217 0 205 39.5
Obstetrics and Gynecology 84 77 64 13 78 1
Orthopedics 248.5 227 227 0 202 21.5
Orthopedics_trauma 64 64 64 0 48
Otolaryngology 27 48 22 26 43 1
Plastics 66.5 82 69 13 64 6
Transplant Surgery 36.5 61 61 0 47 5
Urology 112 119 106 13 93 7
Vascular Surgery 52 68 68 0 61 2
Open 37.5 121
Total 1600 1479 1349 130 1279 124.5
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Summary
• EHRs and dashboards are ill-suited to providing quick, relevant and targeted
insight into important operational metrics. Their metrics are rear-facing and
difficult to access by all stakeholders.
• Push notifications keep stakeholders apprised of their performance without
digging through confusing, often obsolete reports.
• Commonly used metrics – like first case on-time starts and turnover times –
often mask what is really going on. Pushing more relevant metrics like
collectable time help make the case for proper block allocation.
21
Questions
Ashley Walsh, MHA
Former Perioperative
Business Manager
UCHealth
Sanjeev Agrawal
President and
CMO
LeanTaaS