The Imperative of Linking Clinical and Financial Data
to Improve Outcomes
Charles G. Macias M.D., M.P.H.
Chief Clinical Systems Integration Officer, Texas Children’s Hospital
Learning objectives
Assess the effectiveness of an organization’s quality gaps to ensure
organizational readiness, drive efficiency and leverage opportunities to
improve quality.
Illustrate how a blend of clinical and financial data informed by analytics
from an enterprise data warehouse can improve outcomes.
Describe how an EDW and care process implementation can encourage a
culture of quality and safety, providing physicians with the necessary tools
to integrate financial relevance into the practice of delivering high-quality
healthcare.
Discuss how strategy for integration of science, data and predictive analytics
and operational improvement through improvement science can transform
a system towards the triple aim.
Jenny Jones and the Challenges of a
Fragmented System
Within six months, Jenny had visited:
One PCP
Two Hospitals
Three ERs
Leading to:
Six different Asthma Action Plans with
conflicting discharge instructions
Quality Defined
1
Institute of Medicine
domains:
 Safe
 Effective
 Efficient
 Timely
 Patient centered
 Equitable
2
The degree to which health services
for individuals and populations
increase the likelihood of desired
health outcomes and are consistent
with current professional
knowledge.
– Lohr, K.N., & Schroeder, S.A. (1990). A strategy
for quality assurance in Medicare. New England
Journal of Medicine, 322 (10):707-712.
3
Importance of minimizing unintended
variation in health care delivery
The Healthcare Value Equation
In an environment where cost is
marginally increasing, healthcare
must markedly improve quality.
Adoption of EMRs and clinical
systems should help push the
quality agenda but alone may not be
enough to deliver data intelligence.
Quality
Value =
Cost
4
In Second Look, Few Savings from Digital Health Records
New York Times: January 10, 2013
 2005 RAND report forecasts $81 billion annual U.S. savings. “Seven years
later the empirical data on the technology’s impact on health care efficiency
and safety are mixed, and annual health care expenditures in the United
States have grown by $800 billion.”
 Disappointing performance of health IT to date largely attributed to:
 Sluggish adoption of health IT systems, coupled with the choice of systems that
are neither interoperable nor easy to use;
 The failure of health care providers and institutions to reengineer care processes
to reap the full benefits of health IT.
 EHRs, Red Tape Eroding Physician Job Satisfaction
 Most physicians express frustration with the failure to provide efficiency.
 20% want to return to paper
5
Variation in Care
 Describing variation in care in three pediatric diseases: gastroenteritis,
asthma, simple febrile seizure




Pediatric Health Information System database (for data from 21 member hospitals)
Two quality-of-care metrics measured for each disease process
Wide variations in practice
Increased costs were NOT associated with lower admission rates or 3-day ED
revisit rates
 Implications?
 Optimal care may be delivered at a lower cost than today’s care!
Kharbanda AB, Hall M, Shah SS, Freedman SB, Mistry RD, Macias CG, Bonsu B, Dayan PS,
Alessandrini EA, Neuman MI. Variation in resource utilization across a national
sample of pediatric emergency departments. J Pediatr. 2013
6
Consumer Care/Cost Uncertainty
Consumers:
 Trust their physicians
 Hope for the best
 Struggle to understand cost and care
 Don’t often know what they are getting
 Don’t always get great outcomes
Value is what they want
7
Challenge of Healthcare
Physicians are:
 Driven by science and key values
 Overwhelmed with medical literature
 Not well trained to turn that experience
into high quality patient outcomes
Transparency of local data is part of the
solution!
8
Poll Question #1
In your organization, what percentage of patient visits are your
physicians talking about cost and care tradeoffs at the bedside?
a) 0-19%
b) 20-39%
c) 40-59%
d) 60-79%
e) 80-100%
f) Unsure or not applicable
9
Physicians and Care Cost
Patient
values and
preferences
Evidence
Clinical
Expertise
Resource
issues
Source: SAEM. Evidence Based Medicine Online Course 2005
Physician
preferences
Once taboo, physicians should take cost into consideration:
No Money
No Mission
No Expansion
And so providers must…..
 Understand what creates improvements
 Understand the story that their data tells.
11
No Innovation
About Texas Children’s Hospital
Statistics
Number of Beds
469
Annual Inpatient
Admissions
21,744
Annual Outpatient
Visits
1.44 million
Emergency Room
Visits
82,049
Inpatient Surgeries
8,655
Outpatient Surgeries
14,439
A data management strategy to improve outcomes
IMPROVED OUTCOMES
from high quality of care
Patient centric outcomes
and institutional
outcomes achieved
DEPLOYMENT
SYSTEM
Operations
Evidence Based Guidelines and
Order sets, Clinical Decision
Support, patient and provider
materials
CLINICAL
CONTENT SYSTEM
Science and evidence
ANALYTIC SYSTEM
Data analytics and collaborative data
SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction)
Advanced Quality
Improvement course,
QI curriculum, Care
process teams
Informatics,
Electronic Data
Warehousing
Creating a foundation for EB practice
IMPROVED OUTCOMES
from high quality of care
Evidence Based
Guidelines and
Order sets, Clinical
Decision Support,
patient and provider
materials
DEPLOYMENT
SYSTEM
Operations
CLINICAL
CONTENT SYSTEM
Science and evidence
ANALYTIC SYSTEM
Data analytics and collaborative data
SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction)
Evidence-Based Guidelines: EBOC
Acute Chest Syndrome
Acute Gastroenteritis
Acute Heart Failure
Acute Hematogenous
Osteomyelitis
Acute Ischemic Stroke
Acute Otitis Media
Central Line-Associated
Bloodstream Infections
Closed Head Injury
Community-Acquired
Pneumonia
Cystic Fibrosis – Nutrition/GI
>12 y/o
Appendicitis
Autism Assessment and
Diagnosis
Arterial Thrombosis
C-spine Assessment
Asthma
Intraosseus Line Placement
Bronchiolitis
IV Lock Therapy
Cancer Center Procedural
Management
Postpartum Hemorrhage
Cardiac Thrombosis
Deep Vein Thrombosis
Diabetic Ketoacidosis
Fever and Neutropenia in Children with
Cancer
Fever Without Localizing Signs (FWLS)
0-60 Days
Fever Without Localizaing Signs
(FWLS) 2-36 Months
Housewide Procedural Sedation
Hyperbilirubinemia
Neonatal Thrombosis
Nutrition/Feeding in the Post-Cardiac
Neonate
Rapid Sequence Intubation
Skin and Soft Tissue Infection
Status Epilepticus
Tracheostomy Management
Urinary Tract Infection
Poll Question #2
In ambulatory settings, what is the best estimate for the percentage of
questions for which evidence exists to answer clinical questions that affect
the decision to treat?
a) 5%
b) 10%
c) 15%
d) 25%
e) 50%
f) Unsure or not applicable
16
Creating a foundation for data use
IMPROVED OUTCOMES
from high quality of care
DEPLOYMENT
SYSTEM
Operations
CLINICAL
CONTENT SYSTEM
Science and evidence
ANALYTIC SYSTEM
Data analytics and collaborative data
SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction)
Informatics,
Electronic Data
Warehousing
TCH’s EDW Architecture
Metadata: EDW Atlas Security and Auditing
Common, Linkable
Vocabulary; Late binding
FINANCIAL SOURCES
(e.g. EPSi,)
Financial
Source Marts
ADMINISTRATIVE
SOURCES
(e.g. API Time
Tracking)
Administrative
Source Marts
•
•
•
•
•
•
•
•
EMR
Source Marts
EMR SOURCE
(e.g. Epic)
Clinical
Asthma
Appendectomy
Deliveries
Pneumonia
Diabetes
Surgery
Neonatal dz
Transplant
DEPARTMENTAL
SOURCES
(e.g. Sunquest Labs)
Departmental
Source Marts
Operations
• Labor
productivity
• Radiology
• Practice
Mgmt
• Financials
• Patient
Satisfaction
• + others
Patient
Source Marts
PATIENT SATISFACTION
SOURCES
(e.g. NRC Picker,
HR
Source Mart
Human Resources
(e.g. PeopleSoft)
Creating a foundation for QI deployment
IMPROVED OUTCOMES
from high quality of care
Advanced Quality
Improvement course,
QI curriculum, Care
process teams
DEPLOYMENT
SYSTEM
Operations
CLINICAL
CONTENT SYSTEM
Science and evidence
ANALYTIC SYSTEM
Data analytics and collaborative data
Avenues for Dissemination
QUALITY LEADERS National Programs and Partnerships
ADVANCED
INTERMEDIATE
Classroom (e.g. AQI Program, Six Sigma Green Belt)
•Project Required
Online and Classroom (IHI Educational Resources, PEDI 101, EQIPP,
Fellows College)
•Project Required
BEGINNER
Online and Classroom (e.g. Nursing IMPACT (QI Basic). OJO Educational
Resources, Lean Awareness Training)
NEW
Classroom and Department (e.g. New Employee Orientation, e-Learning,
Unit/Department-based training)
Changes that result in process improvement
Improvement
Ideas
Adapted from: The Improvement Guide: A Practical Approach to
Enhancing Organizational Performance, 2nd Ed. Gerald J. Langley,
Ronald D. Moen, Kevin M. Nolan, Thomas W. Nolan, Clifford L.
Norman, and Lloyd P. Provost; Jossey-Bass 2009
Pareto 80/20 Principle in Healthcare
Amount of Variation
TCH’s Care Process Analysis
Asthma
Size of Clinical Process
Improvement Opportunity:
Large processes with significant variation
Bubble Size = Case Count
Driving clinical care improvement: linking science, data
management, operations
Clinical Program
Guidelines centered on evidence-based care
MD
Lead
#5 Care
Process
MD
Lead
#4 Care
Process
MD
Lead
#3 Care
Process
MD
Lead
MD
Lead
#2 Care
Process
Operation
s Lead
Domain
MD Lead
#1 Care
Process
Clinical
Director
Data
Manager
Outcomes
Analyst
BI
Developer
Data
Architect
Permanent, integrated teams composed of clinicians, technologists, analysts
and quality improvement personnel drive adoption of evidence-based
medicine and achieve and sustain superior outcomes.
Application
Service
Owner
Balanced scorecard-expanded visualizations
3.
1.
Care Process
Defined
2.
Current Literature
Research
5.
Group Creates Final
Scorecard
4.
Individual Ratings
Aggregate Ratings
Severity Adjusted Variation
Data Drives Waste Reduction:
Alternative Approaches
1.96 std
Mean
# of
Cases
1 box = 100
cases in a year
Excellent Outcomes
# of
Cases
Poor Outcomes
Excellent Outcomes
Poor Outcomes
Option 1: Focus on Outliers – the prescriptive approach
Strategy eliminate the unfavorable tail of the curve (“quality
assurance”)
Result Ithe impact is minimal
27
Alternative Approaches to Waste Reduction
Mean
# of
Cases
1 box = 100
cases in a year
Excellent Outcomes
# of
Cases
Poor Outcomes
Excellent Outcomes
Poor Outcomes
Option 2: Focus On Inliers – improving quality outcomes across the majority
Strategy Evidence and analytics applied through EBP clinical standards targets inlier
variation
Result Shifting more cases towards excellent outcomes has much more significant
impact
28
Improving Cost Structure Through Waste Reduction
Ordering Waste
Workflow Waste
Defect Waste
Ordering of tests that are neither
diagnostic nor contributory
Variation in Emergency Care wait
time
ADEs, transfusion reactions,
pressure ulcers, HAIs, VTE, falls,
wrong surgery
29
Care Redesign Methodology
CXR utilization in
patients with known
asthma, steroids in
bronchiolitis
Quicker steroid delivery for
status asthmaticus, goal
directed therapy for septic
shock
Evidence
Supports
Evidence equivocal
Hypertonic saline and
bronchodilators in select
patients with bronchiolitis
Evidence against
30
Asthma: Care Process Team Cohort, Percentage of Chest X-rays Ordered*
(Oct. 2010 - Ap r. 2013)
80%
70%
Percentage
60%
51%
50%
40%
35%
30%
20%
Feedba ck of ra tes to hospita lists
a nd Emergency Center clinicia ns
Order set
revisions
10%
Apr. 13
Mar. 13
Feb. 13
Jan. 13
Dec. 12
Nov. 12
Oct. 12
Sep. 12
Aug. 12
Jul. 12
Jun. 12
May. 12
Apr. 12
Mar. 12
Feb. 12
Jan. 12
Dec. 11
Nov. 11
Oct. 11
Sep. 11
Aug. 11
Jul. 11
Jun. 11
May. 11
Apr. 11
Mar. 11
Feb. 11
Jan. 11
Dec. 10
Nov. 10
Oct. 10
0%
Month yea r
* Inpa tient, Emergency Center (EC) a nd observa tion pa tients (Ca re Process Tea m cohort), P-Cha rt ba sed upon EDW da ta extra ction of 5/14/2013 (M& W).
31
Improving Cost Structure Through Waste Reduction
Ordering Waste
Workflow Waste
Defect Waste
Ordering of tests that are neither
diagnostic nor contributory
Variation in Emergency Care wait
time
ADEs, transfusion reactions,
pressure ulcers, HAIs, VTE, falls,
wrong surgery
32
Flow chart of a patient with acute gastroenteritis through the TCH Emergency
Department: Existing process
BEGIN
Patient discharged
home1
4
Does patient
have vomiting &/
or diarrhea
Patient
presents to
Emergency
Dept (ED).
3
Patient transferred
to inpatient bed2
Evaluate per
clinical symptoms
Fellow/
Attending
does pretransfer check
PCA checks
vital signs
Patient
registers
Triage nurse does the following:
·
Vitals
What is the
patient’s level of
dehydration?
Patient
waiting
Key:
___ solid arrow indicates “yes”
_ _ broken arrow indicates “no”
1
2
3
4
4
Outcome:
Outcome:
Outcome:
Outcome:
Outcome:
Time in ED
Time to inpatient bed
Length of stay (LOS)
Revisit from ED discharge
Revisit from inpatient discharge
Nurse-Nurse
checkout
occurs
Nurse
discharges
patient
Bed approved
Patient
evaluated by
triage nurse
PCA checks
vital signs
ED secretary
requests bed
Mild or
Moderate
dehydration
Severe
dehydration
MD does
discharge
orders
MD does
admission
orders
Is the patient
vomiting?
Put patient in
ED room
Decision to
discharge
patient
Triage nurse does the following:
·
Give Zofran
·
Provide gatorade/pedialyte
Decision to
admit patient
Triage nurse does the following:
·
Nothing or give patient gatorade/
pedialyte
Is the patient ok
for discharge?
Follow TCH AGE
clinical algorithm
Patient
waiting
Patient put in
ED room
Patient
evaluated by
nurse
Patient
evaluated by
Medical
student
Patient
evaluated by
ED resident
Patient
evaluated by
ED fellow
Process map before EBG
Patient
evaluated by
ED attending
Modified: 7/21/2009
Flow chart of a patient with acute gastroenteritis through the TCH Emergency Deparment
BEGIN
4
Does patient
have vomiting &/
or diarrhea
Patient
presents to
Emergency
Dept (ED).
Patient discharged
home1
3
Patient transferred
to inpatient bed2
Evaluate per
clinical symptoms
Fellow/
Attending
does pretransfer check
PCA checks
vital signs
Patient
registers
Key:
___ solid arrow indicates “yes”
_ _ broken arrow indicates “no”
Triage nurse does the following:
·
Vitals
·
Assess dehydration (Gorelick score)**
** New process
1
Outcome: Time in ED
2
Outcome: Time to inpatient bed
3 Outcome: Length of stay (LOS)
4 Outcome: Revisit from ED
discharge
4 Outcome: Revisit from inpatient
discharge
What is the
patient’s level of
dehydration?
Patient
waiting
Patient
evaluated by
triage nurse
Nurse-Nurse
checkout
occurs
Nurse
discharges
patient
Collect ORT
tracking sheet
PCA checks
vital signs
ED secretary
requests bed
Mild or
Moderate
dehydration
Severe
dehydration
MD does
discharge
orders
MD does
admission
orders
Is the patient
vomiting?
Put patient in
ED room
Follow TCH AGE
clinical algorithm
Decision to
discharge
patient
Triage nurse does the following:
·
Give Zofran
·
Provide patient education on ORT
·
Initiate ORT
·
Give ORT tracking sheet**
Patient
waiting
Patient put in
ED room
Decision to
admit patient
Triage nurse does the following:
·
Provide patient education on ORT
·
Initiate ORT
·
Give ORT tracking sheet**
Patient
evaluated by
nurse
Bedside nurse does the following:
·
Assesses dehydration (Gorelick score)**
·
Monitors progress on ORT tracking sheet**
·
Reemphasizes patient education on ORT
Patient
evaluated by
Medical
student
Bed approved
Is the patient ok
for discharge?
Patient
evaluated by
ED resident
Patient
evaluated by
ED fellow
ED Fellow does the following:
·
Assesses dehydration (Gorelick score)**
·
Monitors progress on ORT tracking sheet**
·
Reemphasizes patient education on ORT
·
Determines patient disposition
Patient
evaluated by
ED attending
Process map after EBG
Modified: 5/9/2009
35
Improving Cost Structure Through Waste Reduction
Ordering Waste
Workflow Waste
Defect Waste
Ordering of tests that are neither
diagnostic nor contributory
Variation in Emergency Care wait
time
ADEs, transfusion reactions,
pressure ulcers, HAIs, VTE, falls,
wrong surgery
36
Clinical Decision Support to
Minimize Errors
Streamlining and Improving
Processes and Operations to
Minimize Errors
37
Value =
EC: Early administration of Dexamethasone
Expanding evidence based practice
-Provider and staff inservicing
-Clinical decision support
-Bridging a continuum for home care: second dose
10% decrease in TID
Inpatient: prolonged LOS
•
•
35% reduction in LOS
No change in 7 or 30 day readmission rate
No change in days of school/days of work missed
•
•
Direct variable cost ($60/hr)
I-MR Chart of CT - 1st q3h to d/c by Phase
Baseline
Individual V alue
200
Improv ement
Improv
1 ement 2
1
150
100
0
LC L=-16.1
1
13
25
37
49
85
97
109
121
Improv ement
Improv
1 ement 2
1
M oving Range
61
73
O bser vation
Baseline
200
Evidence based approach to early
medication weaning
U C L=70.9
_
X=27.4
5
50
1
150
100
U C L=53.4
__
M R=16.4
LC L=0
50
0
1
13
25
37
49
61
73
O bser vation
85
97
109
121
The continuum: improved patient experience
and outcomes
Improved time to first beta agonist (ED or inpatient arrival)
•
Increase chronic severity assessment
Improve accuracy
Increase appropriate controller prescriptions
Clinical decision support
•
•
•
Increase influenza vaccination rate
Increase number of culturally sensitive
education encounters
Increase number of social work/ legal
support encounters
• AAP use went from 20% to 44% in first
cycle to 52% in second
• ACT use went from 0% to 30% in first
cycle to 41% in second
• Severity classification went from 10% to
35% in first cycle to 54% in second
Registry Financial Score Card
42
Asthma Care
Outcomes
Dashboard
Financial conversations
$5,000
$4,500
$4,000
$3,500
$3,000
$2,500
$2,000
$1,500
$1,000
$500
$0
-$500
-$1,000
-$1,500
-$2,000
-$2,500
-$3,000
-$3,500
-$4,000
-$4,500
-$5,000
Margin
y = 106.48x - 855.25
2011 Q1
2011 Q2
2011 Q3
2011 Q4
2012 Q1
2012 Q2
2012 Q3
47
2012 Q4
2013 Q1
2013 Q2
2013 Q3
2013 Q4
2014 Q1 2014 Q2
y = 291.62x - 3062.5
Examples Demonstrating ROI
 Improved clinical care




Decreases in LOS
Decrease in readmission rates
Decreased unnecessary test utilization
Millions in savings across several disease processes
 Reducing waste by systematizing reporting

EDW reports cost 70% less to build
 Clinical operations tools allow global views for increased
operational efficiency
48
Organizational direction for data
Improved
outcomes for
our patients
and our
enterprise
Organizational
evolution over
time
Decision
support
Data
analytics
Data
reporting
-EMR clinical
reports
-Financial reports
-Shortening event
to reporting time
-Transforming
data and
translating to
action
-Integrating best
evidence into
delivery system
infrastructures
-EMR based
recommendations
and alerts
-Integrated plans
of care across
continuums
Predictive
analytics
--Linking
likelihood of
outcomes to care
decisions driven
with realtime data
-Predicting
financial
outcomes and
linking to clinical
decisions for
populations of
patients
-Linking
outcomes across
infrastructures
Predictive analytics: High risk asthma
Targets: reduce ED visits, hospitalization,
albuterol overuse, ICS non adherence
Critical data source: TCHP, TDSHS data
SHORT ACTING BETA AGONISTS
Targets: reduce ED visits/ unscheduled PCP visits
Critical data source: TCH ED, PCP
Age 1-5 , 4 of 5 below
1 hospitalization = 1 point
>= 2 hospitalizations = 4 points
Government insurance (Medicaid or CHIP): Q2 under health insurance
information
Financial barrier to meds :Answered Yes to Q4 under health insurance
information
Previous asthma hospitalization: Yes to Q2 under past history of asthma
care
Chronic Severity= Mild persistent
Acute Severity= Mild
NUMBER PRESCRIBING PROVIDERS
Age 6+
6 to 9 SABA = 1 point
≥ 10 SABA = 2 points
EC UTILIZATION
1-2 ER = 1 point
> 2 ER = 2 points
HOSPITALIZATION
>= 3 different prescribing providers in 12 months
one of above criteria met, add 1 point
PRIMARY CARE VISITS
Last PCP visit > 6 months + one of above criteria met = add 1 point
INHALED CORTICOSTERIOD
>= 6 ICS low dose canister equivalent refills, subtract 1 point
Lieu TA et al Am J Respir Crit Care Med. 1998 Apr;157(4 Pt 1):1173-80
Farber HJ, et al. Ann Allergy Asthma Immunol. 2004 Mar;92(3):319-28.
Farber HJ. J Asthma. 1998;35(1):95-9
Spitzer WO, et al. N Engl J Med 1992 Feb 20;326(8):501-6
Suissa S, et al. Thorax. 2002 Oct;57(10):880-4.
All 3 of the following
Government insurance (Medicaid or CHIP): Q2 under health insurance
information
Chronic Severity= Mild persistent
Acute Severity= Mild
Or All 3 of the following
Government insurance (Medicaid or CHIP): Q2 under health insurance
information
Exercise induced asthma: Answered yes to exercise page 3 of TEDAS.
Acute Severity= Mild
Care Process Teams
Diabetes
Pregnancy
Asthma
Transplant
Pneumonia
Appendicitis
Newborn
Hospital Acquired Conditions
Sepsis and septic shock
Obesity
Transitions of care
Survey explorer
Additionally, completed a gap strategy for 38 “registries”
Content
System
Using and
innovating best
practices
Assuring an
excellent
patient
experience
Evidence
Integrated
practice via
guidelines, order
sets and
measures
Measurement
System
QI education and
culture change
Improved
Population
Health
Deployment
strategy—
Care Process
Teams
Data/predictive analytics:
measuring through
meaningful metrics
Deployment
System
Knowledge
management for
population health
Analytic
Insights
Questions &
Answers
A
Session Feedback Survey
1. On a scale of 1-5, how satisfied were you overall with the
Penny Wheeler, MD / Allina session?
2. What feedback or suggestions do you have for Penny
Wheeler, MD / Allina session?
3. On a scale of 1-5, how satisfied were you overall with the
Charles Macias / TCH session?
4. What feedback or suggestions do you have for the Charles
Macias / TCH session?
54