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Tools for Data Driven Decisions

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Tools for Data Driven Decisions. Agenda. The Toolkit Designing for Outputs Understanding Tool Inputs Data sources Data collection Tool parameters Spreadsheet Tools Analytical Models Simulation Models. Context, Purpose, Outcome. - PowerPoint PPT Presentation

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Page 1: Tools for Data Driven Decisions

Tools for Data Driven Decisions

Page 2: Tools for Data Driven Decisions

FOR OFFICIAL USE ONLY2

Agenda• The Toolkit• Designing for Outputs• Understanding Tool Inputs

• Data sources• Data collection• Tool parameters

• Spreadsheet Tools• Analytical Models• Simulation Models

Page 3: Tools for Data Driven Decisions

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Context, Purpose, Outcome

1) Context: Navy Medicine faces key operational decisions that can be made easier with the appropriate use of data through tools

2) Purpose: Guidance will be provided in choosing the right tool for the problem at hand, and examples of successful tools used for performance improvement will be discussed

3) Outcome: Identification of opportunities for decision tools and the appropriate application of these tools will lead to more confidence in future decision making

Page 4: Tools for Data Driven Decisions

Tool Choice

"When the only tool you have is a hammer, every problem begins to resemble a nail."

– Abraham Maslow

FOR OFFICIAL USE ONLY4

Page 5: Tools for Data Driven Decisions

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The Toolkit• Spreadsheet Models• Analytical Models• Simulation• Other Support Tools

Page 6: Tools for Data Driven Decisions

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Designing for the Outputs

• What does the customer want out of the tool?• What questions are to be answered?

• What is a usable tool for the customer?• IT skill level• Angle of interest

• What decisions will be made with this tool?• Day-to-day operations?• System-wide planning?• Facilities/Construction?

• Beware of tendency to overdesign!

Page 7: Tools for Data Driven Decisions

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Understanding Tool Inputs

• Data inputs must match tool choice• Will the tool be “live” or based on a snapshot of

available data• Availability of input data impacts fidelity of output

• Quantity of data• Granularity of data• Quality of data

• Examples of data sources:• Less specific: M2• More specific: Local data pulls (CHCS, Operating Room

systems, Essentris)• Custom to needs: Manual data collection

Page 8: Tools for Data Driven Decisions

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Electronic Data Mining

• Reasons for electronic data mining• Existing processes can be described by the

data they generate• Allows for massive sample sizes

• Examples of electronic data sources• Coarse information: M2• Detailed information: CHCS, Essentris, S3, etc.• Custom information: data from specially built

applications such as a discharge board or triage board

Page 9: Tools for Data Driven Decisions

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Manual Data Collection

• Reasons for manual data collection• Validation of system data• Needed data not available in system

• Examples of manual data needs• Process times • Hourly volume counts

• Processes for manual data collection

Page 10: Tools for Data Driven Decisions

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Tool Parameters

• Queue types• What are processes for serving customers?

• Levers• Resources such as staff or rooms• Volumes• Process changes including sequencing and

scheduling

• Process types• Can be statistically described

• Acceptable service levels• What is acceptable wait time?• What is acceptable turn away rate?

Page 11: Tools for Data Driven Decisions

Tool Parameter Examples

• Scheduling modalities• Templates• Random• Shortest case first

• Queuing• Number of queues• Number of servers

• Process types• Sequencing and

location of process steps

• Capacity• Rooms• Staff

• Service Levels• Wait time• ALOS• Resource

utilization• Patients deferred

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Page 12: Tools for Data Driven Decisions

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Spreadsheet Tools

• Software tools widely available• Users familiar with interface• Distributable• Ability for modification• Less robust when representing dynamic

systems

Page 13: Tools for Data Driven Decisions

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Birth Volume Tool

• What is the primary question?• “How many births should be expected in

catchment area X?”

• Who are expected tool users?• High level decision makers, not “in the weeds”

• What decisions will be made with this tool?• System-wide planning• What-if ? analysis

Page 14: Tools for Data Driven Decisions

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Birth Volume Tool

• Solution implemented: Spreadsheet Model

Select whether you want to use the baseline for % direct care, population change and fertility rate. If not, select “No” and enter data in blue cells

Forecast Results

Learn more at Population Based

Healthcare Delivery Design

(II) Session:Wednesday 1100-1200

Page 15: Tools for Data Driven Decisions

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OR Volume Tool• What is the primary question?

• “How do changes in volume and capacity impact OR utilization”

• Who are expected tool users?• Experienced analysts with report outs to

decision makers

• What decisions will be made with this tool?• System-wide planning• What-if ? analysis

Page 16: Tools for Data Driven Decisions

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OR Volume Tool• Solution implemented: Spreadsheet Model

Click to Include

Procedures Have Same %

Click to Adjust Procedures

Your Considered

Volume

Average Case Time (hours)

Percentage Accepted

Accepted Volume

Total Case Room Hours

a a 494 4.80 100% 494 2373 IIIIII

a a 3314 2.99 50% 1641 4910 IIIIIIIIIIIII

a a 1332 2.58 50% 666 1720 IIII Chart data

a a 741 3.57 100% 741 2643 IIIIIIICase Room Hours

Target Room

a a 607 2.18 97% 592 1291 III Average Historic Volume (Step 1)53018.5 36144.0

a a 885 1.76 50% 439 773 II Considered Volume (Step 2)53018.5 36144.0

a a 2693 3.24 50% 1337 4328 IIIIIIIIIII Accepted Volume (Step 4)36116.3 36144.0

a a 453 1.93 34% 154 298

a a 1184 2.68 50% 587 1572 IIII

a a 831 2.70 50% 414 1118 III

a a 70 2.35 14% 10 22

a a 1265 3.26 50% 637 2077 IIIII

a a 607 3.67 65% 392 1439 III

a a 3042 3.80 100% 3042 11552 IIIIIIIIIIIIIIIIIIIIIIIIIIIIIII

a

a

a

Total Accepted Case Room Hours11,145 36,144 36,116 Current scenario enables all case volume to be scheduled. There are 27

hours avilable to schedule more cases.

Unmatched

Urology_GU Surgery

Total Accepted Volume (count)

Target Utilization (in Hours)

Vascular

VIP and WW

Other Diagnostic_Therapeutic

Percent of HoursMOR Service Category

Cardiothoracic

General Surgery

GYN Surgery

Neurosurgery

Obstetrics

Ophthalmology

Orthopedic Surgery

Otolaryngology

Plastic_Recon Surgery

36,144

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000

55,000

60,000

Average HistoricVolume (Step 1)

Considered Volume(Step 2)

Accepted Volume(Step 4)

Room

Cas

e Ti

me

in H

ours

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OB Bed Sizing• What is the primary question?

• “What is the facility capacity?”

• Who are expected tool users?• Experienced analysts with report outs to

decision makers

• What decisions will be made with this tool?• Setting facility volume targets• Construction needs

Page 18: Tools for Data Driven Decisions

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OB Bed Sizing• Solution implemented:

• Spreadsheet model

• Spreadsheet model has many assumptions• Assumptions reduce data needs while

providing reasonable answer• Model can highlight the need for

additional analysis if results are borderline

Page 19: Tools for Data Driven Decisions

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OB Procedure Scheduling Model

• What is the primary question?• “How should inductions and c-sections be

scheduled to level demand”

• Who are expected tool users?• Experienced analysts with report outs to

decision makers

• What decisions will be made with this tool?• A recommended schedule template for c-

sections and inductions

Page 20: Tools for Data Driven Decisions

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OB Procedure Scheduling Model

• Solutions implemented• Analytical model produces a new template for

scheduled procedures that levels day of week peaks

Page 21: Tools for Data Driven Decisions

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Simulation Tools

• Software tools not widely available• Limited familiarity with tools• Very robust representation of dynamic

systems• Can require extensive data as inputs• Can provide very granular results

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OB Simulation Model

• What is the primary question?• “What is the capacity of our facility?”

• Who are expected tool users?• Experienced analysts with report outs to

decision makers

• What decisions will be made with this tool?• Setting facility targets• Construction needs

Page 23: Tools for Data Driven Decisions

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OB Simulation Model

• Solution implemented:• Simulation identified the bottleneck was MBU

beds

• Additional analysis:• “Assuming process and facility

reconfigurations that alleviate the bottleneck, what is the new capacity”

• Solution recommended:• Reclaim non-used LDRP rooms and keep

vaginally delivered patients in the same room for entire stay

Page 24: Tools for Data Driven Decisions

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Pharmacy Simulation Model

• What is the primary question?• “What processes/equipment are required to

meet performance criteria at a given volume”

• Who are expected tool users?• Experienced analysts with report outs to

decision makers

• What decisions will be made with this tool?• Staffing decisions• Facility decisions• Equipment decisions

Page 25: Tools for Data Driven Decisions

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Pharmacy Simulation Model

• Solution implemented:• Simulation model• Complex input needs• Complex output processing needs

Page 26: Tools for Data Driven Decisions

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Simulation User Interface

• Run all possible scenarios and present results in spreadsheet model• Method for reducing

demands on users• Limit input requirements

for front line

Page 27: Tools for Data Driven Decisions

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Dynamic Decision Support Tools

• OB Forecasting Tool• Answers the question “Should I accept or

defer this patient”• Used by front line staff• Dynamically updated with live data