Founded in 1852 and with over 53,000 employees, Providence Health & Services is a 27 hospital, plus 100 other kinds of healthcare facilities, organization. In this session, you will learn how Dr. Marton lead Providence’s clinical transformation by learning from and applying the best available practices faster, and more effectively to achieve truly safe, high quality care with the least waste and expense in ALL parts of the system.

1. Keith Marton, MD
1/18/12

2.  Founded in 1852
 Catholic, non-profit
 Fairly highly integrated system across 5 western
states
 27 hospitals from critical access to large
quaternary facilities
 Plus over 100 other kinds of facilities
 ~53,000 employees
 Annual revenue: ~$8.5 billion
 NOI typically 4.5-5%
 ~1500 employed physicians (out of 13,000
medical staff members)

3. An uncertain future that didn’t
look great

4. Net Revenue Impact
2010-2012 (millions)
($29) ($86) ($59)
($160) ($109)
($340) ($283)
($428)
($530)
($1,458)
AK WAMT OR CA Total
Best Worst
Best: 1.5% NSR Reduction, Worst: 6.5% NSR Reduction Pa
ge
4

5.  People and Culture
◦ The ability to instill a culture of collaboration,
creativity, and accountability. (i.e. a learning
organization that embraces a just culture)
 Business intelligence
◦ The ability to collect, analyze, and connect accurate
quality and financial data to support organizational
decision making. (More on this later)

6.  Performance improvement
◦ The ability to use data to reduce variability in
clinical processes and improve the delivery , cost-
effectiveness, and outcomes of clinical care. (More
on this later )
 Contract and risk management
◦ The ability to develop and manage effective care
networks and predict and manage different forms
of patient-related risk. (i.e. integrated ACO’s with
good data)

7. And do it in many areas

8. Categories of Focus
1.Clinical Transformation
2.MD Partnership Transformation
3.Administrative Transformation
4.Balance Sheet Maximization
5.Contiguous Market Growth
Pa
ge
8

9. Targeted Impact
Categories of Focus (millions)
Clinical Transformation $ 784
MD Partnership Transformation $ 87
Administrative Transformation $ 257
Balance Sheet Maximization $ 150
Contiguous Market Growth $ 180
Total $ 1,458
Pa
ge
9

11.  Data Re-Use, AKA business intelligence,
Unified Intelligence, Comprehensive data
Warehouse
◦ Relatively new concept for health care
◦ Uses still being defined and explored
◦ Quantification of costs are pretty clear
◦ Quantification of benefits: still emerging

12.  Reduce the cost of collecting/analyzing data
 Speed the decision making process and faster
spread of innovation, based on near real-time
access to information
 Preclude the need for many, future small
database acquisitions
 Anticipate having data to answer questions that
we didn’t know we’d want to ask
 Identify which data (among many) really need to
be standardized
 Reduced waste and injury
 Data backup
Pa
ge
12

13.  Selected after a look at the options:
◦ review by outside consultants (First, Gartner)
◦ site visits to other users (SJHS)
 Initial Implementation:
◦ 2 of 4 regions
◦ 7+ “use cases”
◦ Evaluation of technical deployment, user friendliness,
future use, cost of ownership
 Enterprise agreement to support Providence
deployment
Pa
ge
13

14.  Initial Goal: identify the top 10 uses for initial
implementation
◦ Actually, we stopped after 47 potential uses
 Create the supporting infrastructure
◦ For managing the tool
◦ For spreading and implementing knowledge across
the system
 Connect as many data sources as possible.
Pa
ge
14

15.  System implemented in all 4 regions—107
different data inputs
 Governance/communications structure created
 Support staff hired--~26 FTE’s (mostly internal
staff)
 Continued training of key users
 Initial focus on 8 key uses:
◦ Catheter-Associated UTI’s
◦ Modified Early warning system (MEWS)
◦ Sepsis risk
◦ Central line blood stream infection
◦ Readmission tracking
Pa
ge
15

16.  Use cases, cont’d
◦ Core measure—CHF D/C
◦ Patient transfer activity
◦ Glycemic monitoring.
 Key strategic concept: use system to identify
patients requiring standardized interventions
(but allow staff to also do ad hoc inquiries)
Pa
ge
16

18.  In one (smaller) Providence region, it costs $7
million per year to collect and report core
measure data
◦ Due to “brute force” data collection: clinicians go on the
wards to find core measure candidates and hand tally
results.
 Amalga solution:
◦ Replace brute force method by using electronic data to
find those patients and alert ward staff.
 Expansion of core measures will only increase
the cost problem if a data warehouse does not
exist.

19.  A pharmacy alert system
◦ Multiple electronic inputs (lab, pharmacy, ADT)
◦ Locally developed rules scan the inputs and alert
pharmacist to intervene with at-risk patients.
 Impact (at 20 hospitals in 1 system):
◦ $4 million pharmacy savings/ month
◦ 70 serious events averted in 4 months
 Amalga could have done this, had it been
purchased earlier (and will eventually replace that
system)
 Caveat: system worked best where pharmacists
worked on the wards.

20.  Improving sepsis outcomes
◦ Early detection and treatment of sepsis: up to 50%
mortality improvement; 30% improvement in LOS
and 30% improvement in cost of care.
◦ So, why stop at treating patients who already have
sepsis?
◦ Next step: Use Amalga to ID patients at greatest
risk for sepsis for intensified monitoring and
prevention of sepsis.

21.  Catheter-associated UTI’s (CA-UTI’s)
◦ 50% of HAI’s
◦ In Providence, HAI’s cost ~$45 million/yr
◦ Equivalent to 247 nurses, who could be put to
better use
◦ Prior to Amalga it was impossible to even know who
had urinary catheters
◦ Now, catheter patients can be identified and
evidence- based standards applied
◦ Expected outcome: 50% reduction in CA-UTI’s

22. Post MEWS
75.4
65.4
55.4
Pre MEWS
45.4
35.4
UCL
25.4
CL
15.4
5.4 LCL
-4.6
1/23/2012

23. 14 Total Pages for Code Team from "PEAT" areas
12 UCL
10 Post MEWS &
PEAT rounds
8
6
CL
4
2
0
1/23/2012

24. $2,700
$453,600
14 Potential annual
savings using MEWS
Average
Admissions reimbursement
35 that could shortfall for an
be avoided ICU admission
(HFMA July 2006)
(McQuillan 1998)
Monthly
escalations of
care to ICU
More importantly…
MEWS at PAMC saved lives
1/23/2012

25.  Basic concept: use data mining to detect best
practices within one’s own system
◦ Internal best practices more likely to be adopted
◦ e.g. Most cost effective approach to stroke,
pneumonia, hip replacement
 Also, need appropriate communications
system and infrastructure to support spread
and adoption
 Ultimately, more rapid adoption of innovation
means faster savings, improvements.

26.  New clinical registries—for relating inputs to
outcomes
◦ Orthopedics
◦ Thoracic surgery
 Real-time ICU dashboards
 More active data mining by quality
department.
 Incorporation of cost data into the system.

27.  This is a new tool—not intuitive to many folks
◦ Communicate, communicate, communicate!
◦ Educate, educate, educate!
◦ Involve all stakeholders in the process.
 This is a pluri-potential tool
◦ Know which strategic goals are key
 This is an expensive tool
◦ Know (roughly) how it’s going to pay for itself—tell
stories to illustrate, know the costs that can be
reduced, even though the actual results are not in
yet; have an idea why real-time data are important.
Pa
ge
27

28.  This is not an expensive tool –compared to the
various alternatives
 Even with a state of the art EMR, this kind of tool
makes sense.
 This is not a magical tool
◦ Understand that it needs to be supported by a skilled
staff and effective infrastructure
 We have probably underestimated its potential
uses and value
 It is primarily limited by the amount of electronic
data available
◦ So, we expect that we’ll want to generate even more
sources of electronic data
Pa
ge
28

29.  Access to real-time data reveals multiple
opportunities to improve clinical outcomes
AND financial returns
 In general, the actual benefits have turned
out to be greater than the estimated benefits
 Data alone are not sufficient; also required
are:
◦ Skilled data mining and presentation
◦ Supporting infrastructure to act on the data

30. Questions?
Pa
ge
30