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

375 x 667px

6

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

8

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

9

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

11

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

14

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

iPhone 6

375 x 667px

iqueue.com/release

iPhone 6

375 x 667px

Proactive text Drive action (one click release)

16

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

17

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

20

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

ashley.walsh@uchealth.org

Sanjeev Agrawal

President and

CMO

LeanTaaS

sanjeev@leantaas.com