This document summarizes a presentation about using data-driven analysis to plan an integrated interventional platform by merging departments at Stanford Health Care. Key points discussed include collecting utilization and volume data from multiple sources; analyzing past trends and projecting future demand; using the analysis to identify opportunities to optimize processes and reduce space needs; and how the data-driven approach improved confidence in the resulting master plan compared to previous intuition-based approaches. The presentation concludes with positive feedback from hospital leadership on how the new plan is better than previous proposals and their excitement about the opportunities identified through this analysis.

1. Data-model Driven Master Planning
Merging Departments to Create an Integrated Interventional Platform

2. 01 Introduction
Speakers
Ashley Umana, MS, EDAC, LEED AP
Stanford Health Care
Director of Campus Planning
Planning, Design & Construction
Eric Peabody, Architect, LEED AP
Taylor Design
Principal | Project Director

3. 03 Data Collection
Getting Good Data
Understanding the Data
04 Retrospective Analysis
Volume
Utilization
05 Prospective Analysis
Business Case vs CAGR
Project Room Demand
Identifying Gaps in Demand
for Construction
06 System Analysis
Process Engineering
Preop Startup vs Steady
State
PACU Scenario Planning
07 Platform outcomes
Original Master Plan
Savings by eliminating PACU
bays
Opportunity in fewer Cath Labs
08 Takeaways
09 Q&A
01 Introduction
Speakers
Agenda
02 Project Overview

4. 02 Project Overview

5. 02 Project Overview
Stanford Existing Plan

6. 03 Data Collection
Getting Good Data
Understanding the Data

7. 03 Data Collection
Getting Good Data
Data Types
Date time stamps
Location data
Campus
Room/modality number
Categorical data
Service line
Patient types
In/outpatient
Encounter Data
Exam/procedure
Procedure type
Special business cases
Advice
• Unsummarized data
• Remove Protected Health
Information (PHI)
• Record number to track patients
across service lines: Preop →
OR → PACU → Nursing
• Linking data across system
changes (e.g. Cerner to Epic)
depends on future projection
method
• 14M data points: single data
table but subject to Excel limits:
1,048,576 rows x 16,384
columns

8. 03 Data Collection
Understanding the Data

9. 03 Data Collection
Understanding the Data

10. 04 Retrospective Analysis
Volume
Utilization

11. 04 Retrospective Analysis
Volume

12. 04 Retrospective Analysis
Utilization

13. 05 Prospective Analysis
Business Case vs CAGR
Project Room Demand
Identifying Gaps in Demand for Construction

14. 05 Prospective Analysis
Business Case vs CAGR
FV = EV * (CAGR / 100 + 1) n
Future Volume = Ending Volume * (Compound Annual Growth Rate / 100 + 1) number of years
CAGR = (EV / SV)1 / n - 1
Compound Annual Growth Rate = (Ending Volume / Starting Volume)1 / number of years – 1
Starting
Volume
Data Year 2
Data Year 3
Data Year 4
Ending
Volume
Projected
Year 1
Projected
Year 2
Projected
Year 3
Future
Volume

15. 05 Prospective Analysis
Project Room Demand

16. 05 Prospective Analysis
Identifying Gaps in Demand for Construction

17. 06 System Analysis
Process Engineering
Preop Startup vs Steady State
PACU Scenario Planning

18. 06 System Analysis
Process Engineering

19. 06 System Analysis
Process Engineering

20. 06 System Analysis
Preop Startup vs Steady State

21. 06 System Analysis
PACU Scenario Planning

22. 06 System Analysis
PACU Scenario Planning

23. 07 Platform Outcomes
Original Master Plan
Savings by Eliminating PACU Bays
Opportunity in Fewer Cath Labs

24. 07 Platform Outcomes
Original 2016 Master Plan

25. 07 Platform Outcomes
Savings by Eliminating PACU Bays

26. 07 Platform Outcomes
Opportunity in Fewer Cath Labs

27. 08 Takeaways

28. 08 Takeaways
• Get clean data and develop confidence with the clinicians
that all relevant data is included
• Data frequently contradicts clinician intuition about the
business and operations
• Including clinicians & leadership throughout is essential
to getting them to release their preconceptions and buy-
in to the results
• In-house data analysis streamlines business and spatial
planning

29. This is really impressive. In all the
years of trying to get data to validate
how our volume projections translate
to equipment needs, I have never had
this much confidence in a final report.
Susan Spielman
“” I’m really excited about this; we’ve
been able to create a plan in an
existing building that is even better
than in the new hospital.
Dr Rusty Hoffman
“”
Senior Director, Programs Projects,
Rad/Radiology Finance and Administration
Stanford University Medical Center
Professor of Radiology Chief, Vascular and
Interventional Radiology
Stanford University Medical Center

30. Q & A
Ashley Umana, MS, EDAC, LEED AP
Stanford Health Care
Director of Campus Planning
Planning, Design & Construction
Eric Peabody, Architect, LEED AP
Taylor Design
Principal | Project Director
Q&A