An overview of the cost reduction opportunities for a Health Care provider. These opportunities can be identified, quantified and optimised through data-driven insights. The slide pack also provides a strategic overview of how one would set up such a project within a large organisation, whilst mitigating patient-care concerns.

1. © 2014, Confidential

2. AGENDA
1) Recap of project goals
2) Discuss initial findings
3) SWOT Analysis
4) Potential savings opportunities
5) Proposal & Timeframe
6) Project requirements
7) Risks
8) Benefits
9) Discussion and next steps
10) Contact Details

3. 1) PROJECT GOALS
Goals
• Achieve cost reductions through advanced data analytics
Reminder
• Not a business intelligence or management reporting project
Requirements
• Focus on big saving areas, however some quick wins are also
required
• Adopt a strategic approach
• Run analytics projects in conjunction with HC’s domain experts,
Hospital Quality Team and Procurement
• Transfer analytics skillset to HC’s staff in process
• Project costs are small in comparison to savings

4. 2a) INITIAL FINDINGS: DATA SOURCES
HC operates a number of hospitals across Australia, with a full range of
clinical services, a medication centre, pathology and pharmacy
businesses. It relies on the following systems:
• 7 independent SAP Accounts modules (collectively 900k transactions
/ month)
• 1 SAP General Ledger module (15k transactions / month)
• 1 of 2000 ‘green screen’ pathology billing system
• 1 web-based HR / payroll system
• 4 different time & attendance programs
• 17 different rostering systems
• 3 HSE management systems
• Numerous independent clinical systems and patient databases.
• A wide variety of excel sheets

5. 2b) INITIAL FINDINGS: DATA SKILLS
Over the last week, The Data Analysis Group (TDAG) met with 43
managers and department heads across the 4 divisions. Using Excel as
a benchmark:
• Most clinical employees do not use Excel
• Operational and unit managers have some skill and use Excel
regularly.
• Finance and procurement employees are regular users with two
people identifying themselves as advanced.
• IT Department has 2 database administrators however overworked
and unable to assist with project.
• A finance controller, <name withheld>, classifies herself as a ‘data
geek’. Her manager described her as ‘smart & hardworking’

6. From a data / analytics perspective:
3) SWOT ANALYSIS
STRENGTHS WEAKNESSES
• Capturing a lot of data
• Direct & indirect costs correctly
allocated to cost centres
• Existing SQL license and
infrastructure
OPPORTUNITIES THREATS
• Current environment is ripe for data-driven
savings
• Develop in-house analytics
capability
• Cost effectively improve patient care
• Multiple systems make for difficult
analysis
• Some data is unclean
• Lack of analytics expertise
• Amount of available data precludes
use of Excel
• Costs continue to rise
• Savings opportunities foregone
• Deterioration of patient care

7. 4) POTENTIAL SAVINGS OPPORTUNITIES
The following projects have been identified to potentially yield
significant savings:
• Accurately costing patient procedures and services
• Addition of new high-yielding patient services
• Benchmarking of comparative BUs within the group
• Closure of low-yielding patient services
• Cost reduction through elimination of unnecessary effort and waste
• Cross subsidisation of preventative services from curative services
• Developing standardised and optimised health care policies
• High spend and complicated supply chain categories
• Increased bed utilisation through greater number of admissions
• Convert inpatient care to high-tech outpatient services
• Moving from reactive maintenance to preventative maintenance
• Outsourcing / insourcing of indirect services
• Reduced labour costs through improved scheduling
• Stock-loss analysis of high value drugs and medical supplies
TARGET SAVING: 9% OF SPEND

8. TDAG propose a 4 week diagnostic process to quantify high-yielding
savings opportunities.
3 Weeks 1 Week 2 Weeks 8-10 Weeks 4 Weeks
Extract, Clean
& Load data
• Focus on last 5 years
SAP data.
• Extract, transform &
load years into SQL
data warehouse
• Include CC, BU &
Divisional dimensions
• Check aggregated
values align with
financials.
Data Mining
to Quantify
Opportunities
Develop & Select
Pilot Project
Pilot Project
Review, Refine &
Embed Processes
• Identify savings
opportunities
• Size the prize
• Select suitable
projects for pilot
approach
• Develop assessment
criteria for pilot
• Scope out 3 projects
in conjunction with
BU owners,
Procurement & HQT
• Assess project risks,
rewards & duration
• Identify
stakeholders,
internal/external
team & success
criteria
• Commence pilot,
TGAG running
analysis, HC running
project
• Identify & optimise
business levers
through data insights.
• Conduct regular
Stakeholder reviews
• Review / capture
learnings during each
stage of the process.
• Review pilot project
against key criteria
• Refine processes
based upon learnings
• Achieve sign-off from
stakeholders and
process owners
• Agree roll-out plan for
implementation
• Agree training and
resource
requirements to
deliver
Diagnostic Phase
Review findings &
agree next steps
5) PROPOSAL & TIMEFRAME
CFO approve
1 pilot project

9. 6) PROJECT REQUIREMENTS
Analytical skillsets required
• High-end analysts to be provided by TDAG.
• Access to <name withheld> within finance, to join team and transfer knowledge.
Business skillsets required
• Access to finance, procurement and Hospital Quality team, as per individual projects’ scope.
• Access to HC’s domain experts and stakeholders. Permission to be obtained from domain experts’
superior(s) first.
• IT department to set up server, and provide access to data exports for existing systems.
• Weekly review with project owners and monthly review with stakeholders.
Data requirements
• Data for each project will be specified in the project brief, along with any cleansing requirements
Software / hardware requirements
• Virtual server with administrative access
• Installation of
o SQL server (Existing license)
o SSIS, SSRS, SSAS and SSMS (Existing license)
o RapidMiner (OS)
o Notepad & Vim (OS)
o Excel (Existing license)
o Tableau Professional (1 seat at USD $1,999) and QlikView (no charge)
o Remote access to SQL and to server

10. 7) RISKS
Although each data-project will come with its own risks, the
common risks of this overall project include:
RISK MITIGATION
Compromised patient care
from too much cost cutting.
• Patient Standards Committee (PSC) signoff
before implementation phase
Loss of employee trust. • Acknowledge that staff will not be blamed or
victimised for past performance
• Involve stakeholders in project
Unsafe medical practices. • Project signoff from PSC
Project costs exceed savings. • Rigorous review process to prevent projects
with insufficient returns from going ahead
• “Fail fast” methodology, with weekly reviews
Disclosure of sensitive data. • Sensitive data (eg. salaries, patient data etc.) to
be aggregated and/or de-identified
• Available to project team on a restricted basis

11. The benefits from this project strategy include:
• Cost effective. Gain access to skilled data analysis professionals,
without the overhead associated with building and maintaining your
own internal infrastructure and team.
• Fast time-to-benefit
• Reduced possibility of project abandonment. Actively involving end
users in the process creates buy-in and commitment.
• Continuous feedback throughout the project cycle results
in processes that meet the actual needs of patients and staff.
• Streamlined face-to-face communications improve HC - TDAG
relationships by developing trust and sharing knowledge.
• Sign off procedure prevents compromised patient care.
8) BENEFITS

12. 9) DISCUSSION AND NEXT STEPS
Discussion
• Does this strategy meet your needs?
• If not, what do we need to change?
• Do you foresee any additional risks or implementation challenges?
Next Steps
• Formalise strategy and signoff
• Commence Diagnostic Phase
• Update meeting in 1 month.

13. 10) CONTACT DETAILS
For further information, please contact
The Data Analysis Group on:
James Karis
CEO & Chief Geek
p) 1300 788 662
e) info@data-analysis.com.au
w) www.data-analysis.com.au