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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 
Institute of Medicine 
domains: 
 Safe 
 Effective 
 Efficient 
 Timely 
 Patient centered 
 Equitable 
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. 
Importance of minimizing unintended 
variation in health care delivery 
1 2 
3
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. 
4 
Value= 
Quality 
Cost
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 
6 
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, NeumanMI. Variation in resource utilization across a national 
sample of pediatric emergency departments. J Pediatr. 2013
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? 
9 
a) 0-19% 
b) 20-39% 
c) 40-59% 
d) 60-79% 
e) 80-100% 
f) Unsure or not applicable
Physicians and Care Cost 
Evidence Patient 
Source: SAEM. Evidence Based Medicine Online Course 2005 
Clinical 
Expertise 
values and 
preferences 
Physician 
preferences 
Resource 
issues
Once taboo, physicians should take cost into consideration: 
No Money No Mission No Expansion No Innovation 
And so providers must….. 
 Understand what creates improvements 
 Understand the story that their data tells. 
11
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 
DEPLOYMENT 
SYSTEM 
Operations 
ANALYTIC SYSTEM 
Data analytics and collaborative data 
Patient centric outcomes 
and institutional 
outcomes achieved 
SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) 
CLINICAL 
CONTENT SYSTEM 
Science and evidence 
Advanced Quality 
Improvement course, 
QI curriculum, Care 
process teams 
Informatics, 
Electronic Data 
Warehousing 
Evidence Based Guidelines and 
Order sets, Clinical Decision 
Support, patient and provider 
materials
Creating a foundation for EB practice 
IMPROVED OUTCOMES 
from high quality of care 
DEPLOYMENT 
SYSTEM 
Operations 
CLINICAL 
CONTENT SYSTEM 
Science and evidence 
ANALYTIC SYSTEM 
Data analytics and collaborative data 
Evidence Based 
Guidelines and 
Order sets, Clinical 
Decision Support, 
patient and provider 
materials 
SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction)
Evidence-Based Guidelines: EBOC 
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 
Acute Chest Syndrome 
Acute Gastroenteritis 
Acute Heart Failure 
Acute Hematogenous 
Osteomyelitis 
Acute Ischemic Stroke 
Acute Otitis Media 
Appendicitis 
Arterial Thrombosis 
Asthma 
Bronchiolitis 
Cancer Center Procedural 
Management 
Cardiac Thrombosis 
Central Line-Associated 
Bloodstream Infections 
Closed Head Injury 
Community-Acquired 
Pneumonia 
Cystic Fibrosis – Nutrition/GI 
>12 y/o 
Autism Assessment and 
Diagnosis 
C-spine Assessment 
Intraosseus Line Placement 
IV Lock Therapy 
Postpartum Hemorrhage
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? 
16 
a) 5% 
b) 10% 
c) 15% 
d) 25% 
e) 50% 
f) Unsure or not applicable
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 
Informatics, 
Electronic Data 
Warehousing 
SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction)
Metadata: EDW Atlas Security and Auditing 
Common, Linkable 
Vocabulary; Late binding 
Financial 
Source Marts 
Administrative 
Source Marts 
Departmental 
Source Marts 
Patient 
Source Marts 
EMR 
Source Marts 
HR 
Source Mart 
FINANCIAL SOURCES 
(e.g. EPSi,) 
ADMINISTRATIVE 
SOURCES 
(e.g. API Time 
Tracking) 
EMR SOURCE 
(e.g. Epic) 
DEPARTMENTAL 
SOURCES 
(e.g. Sunquest Labs) 
PATIENT SATISFACTION 
SOURCES 
(e.g. NRC Picker, 
Human Resources 
(e.g. PeopleSoft) 
TCH’s EDW Architecture 
Operations 
• Labor 
productivity 
• Radiology 
• Practice 
Mgmt 
• Financials 
• Patient 
Satisfaction 
• + others 
Clinical 
• Asthma 
• Appendectomy 
• Deliveries 
• Pneumonia 
• Diabetes 
• Surgery 
• Neonatal dz 
• Transplant
Creating a foundation for QI deployment 
IMPROVED OUTCOMES 
from high quality of care 
DEPLOYMENT 
SYSTEM 
Operations 
CLINICAL 
CONTENT SYSTEM 
Science and evidence 
ANALYTIC SYSTEM 
Data analytics and collaborative data 
Advanced Quality 
Improvement course, 
QI curriculum, Care 
process teams
Avenues for Dissemination 
QUALITY LEADERS National Programs and Partnerships 
ADVANCED 
Classroom (e.g. AQI Program, Six Sigma Green Belt) 
•Project Required 
INTERMEDIATE 
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 
Ideas 
Improvement 
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
TCH’s Care Process Analysis 
Asthma 
Amount of Variation 
Size of Clinical Process 
Bubble Size = Case Count 
Improvement Opportunity: 
Large processes with significant variation
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 
#2 Care 
Process 
MD 
Lead 
#1 Care 
Process 
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 
Clinical 
Director 
Domain 
MD Lead 
Operation 
s Lead
Balanced scorecard-expanded visualizations 
1. Care Process 
Defined 
2. Current Literature 
Research 
3. Individual Ratings 
5. Group Creates Final 4. Aggregate Ratings 
Scorecard
Severity Adjusted Variation
Data Drives Waste Reduction: 
Alternative Approaches 
1 box = 100 
cases in a year 
Option 1: Focus on Outliers – the prescriptive approach 
Strategy eliminate the unfavorable tail of the curve (“quality 
assurance”) 
Result Ithe impact is minimal 
# of 
Cases 
Excellent Outcomes Poor Outcomes 
1.96 std 
# of 
Cases 
Mean 
Excellent Outcomes Poor Outcomes 
27
Alternative Approaches to Waste Reduction 
Excellent Outcomes Poor Outcomes 
# of 
Cases 
Mean 
1 box = 100 
cases in a year 
Excellent Outcomes 
# of 
Cases 
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 
29 
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
Care Redesign Methodology 
Evidence against 
30 
CXR utilization in 
patients with known 
asthma, steroids in 
bronchiolitis 
Evidence equivocal 
Hypertonic saline and 
bronchodilators in select 
patients with bronchiolitis 
Evidence 
Supports 
Quicker steroid delivery for 
status asthmaticus, goal 
directed therapy for septic 
shock
Asthma: Care Process Team Cohort, Percentage of Chest X-rays Ordered* 
(Oct. 2010 -Apr. 2013) 
Feedback of rates to hospitalists 
and Emergency Center clinicians 
31 
51% 
35% 
80% 
70% 
60% 
50% 
40% 
30% 
20% 
10% 
0% 
Oct. 10 
Nov. 10 
Dec. 10 
Jan. 11 
Feb. 11 
Mar. 11 
Apr. 11 
May. 11 
Jun. 11 
Jul. 11 
Aug. 11 
Sep. 11 
Oct. 11 
Nov. 11 
Dec. 11 
Jan. 12 
Feb. 12 
Mar. 12 
Apr. 12 
May. 12 
Jun. 12 
Jul. 12 
Aug. 12 
Sep. 12 
Oct. 12 
Nov. 12 
Dec. 12 
Jan. 13 
Feb. 13 
Mar. 13 
Apr. 13 
Percentage 
Month year 
Order set 
revisions 
* Inpatient,Emergency Center (EC) and observation patients (Care Process Team cohort), P-Chart based upon EDW data extraction of 5/14/2013 (M& W).
Improving Cost Structure Through Waste Reduction 
Ordering Waste Workflow Waste Defect Waste 
32 
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
Patient 
presents to 
Emergency 
Dept (ED). 
Patient 
registers 
Patient 
waiting 
Patient 
evaluated by 
triage nurse 
Flow chart of a patient with acute gastroenteritis through the TCH Emergency 
Does patient 
have vomiting &/ 
or diarrhea 
Triage nurse does the following: 
· Vitals 
What is the 
patient’s level of 
dehydration? 
Severe 
dehydration 
Evaluate per 
Department: Existing process 
clinical symptoms 
Mild or 
Moderate 
dehydration 
Put patient in 
ED room 
Triage nurse does the following: 
· Give Zofran 
· Provide gatorade/pedialyte 
Is the patient 
vomiting? 
Follow TCH AGE 
clinical algorithm 
4 3 
Triage nurse does the following: 
· Nothing or give patient gatorade/ 
pedialyte 
BEGIN 
Patient 
waiting 
Patient put in 
ED room 
Patient 
evaluated by 
nurse 
Patient 
evaluated by 
Medical 
student 
Patient 
Nurse 
discharges 
patient 
PCA checks 
vital signs 
MD does 
discharge 
evaluated by 
ED resident 
Patient 
evaluated by 
ED fellow 
Is the patient ok 
for discharge? 
Patient 
evaluated by 
ED attending 
Fellow/ 
Attending 
does pre-transfer 
check 
Nurse-Nurse 
checkout 
occurs 
Bed approved 
ED secretary 
requests bed 
MD does 
admission 
orders 
Decision to 
admit patient 
orders 
Decision to 
discharge 
patient 
PCA checks 
vital signs 
Patient discharged 
home1 
Patient transferred 
to inpatient bed2 
Key: 
___ solid arrow indicates “yes” 
_ _ broken arrow indicates “no” 
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 
Modified: 7/21/2009 Process map before EBG
Patient 
presents to 
Emergency 
Dept (ED). 
Patient 
registers 
Patient 
waiting 
Patient 
evaluated by 
triage nurse 
Flow chart of a patient with acute gastroenteritis through the TCH Emergency Deparment 
Does patient 
have vomiting &/ 
or diarrhea 
clinical symptoms 
Triage nurse does the following: 
· Vitals 
· Assess dehydration (Gorelick score)** 
What is the 
patient’s level of 
dehydration? 
Severe 
dehydration 
Evaluate per 
Mild or 
Moderate 
dehydration 
Put patient in 
ED room 
Is the patient 
Triage nurse does the following: 
· Give Zofran 
· Provide patient education on ORT 
· Initiate ORT 
· Give ORT tracking sheet** 
vomiting? 
Follow TCH AGE 
clinical algorithm 
4 3 
Triage nurse does the following: 
· Provide patient education on ORT 
· Initiate ORT 
· Give ORT tracking sheet** 
BEGIN 
Patient 
waiting 
Patient put in 
ED room 
Patient 
evaluated by 
nurse 
Patient 
evaluated by 
Medical 
student 
Patient 
Nurse 
discharges 
patient 
PCA checks 
vital signs 
MD does 
discharge 
evaluated by 
ED resident 
Patient 
evaluated by 
ED fellow 
Patient 
evaluated by 
ED attending 
Bedside nurse does the following: 
· Assesses dehydration (Gorelick score)** 
· Monitors progress on ORT tracking sheet** 
· Reemphasizes patient education on ORT 
orders 
ED Fellow does the following: 
· Assesses dehydration (Gorelick score)** 
· Monitors progress on ORT tracking sheet** 
· Reemphasizes patient education on ORT 
· Determines patient disposition 
Is the patient ok 
for discharge? 
Fellow/ 
Attending 
does pre-transfer 
check 
Nurse-Nurse 
checkout 
occurs 
Bed approved 
ED secretary 
requests bed 
MD does 
admission 
orders 
Decision to 
admit patient 
Decision to 
discharge 
patient 
PCA checks 
vital signs 
Patient discharged 
home1 
Patient transferred 
to inpatient bed2 
Key: 
___ solid arrow indicates “yes” 
_ _ broken arrow indicates “no” 
** New process 
1Outcome: 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 
Collect ORT 
tracking sheet 
Process map after EBG 
Modified: 5/9/2009
35
Improving Cost Structure Through Waste Reduction 
Ordering Waste Workflow Waste Defect Waste 
36 
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
37 
Clinical Decision Support to 
Minimize Errors 
Streamlining and Improving 
Processes and Operations to 
Minimize Errors
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 
Evidence based approach to early 
medication weaning 
• 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 Improv emeInmt p1rov ement 2 
1 13 25 37 49 61 73 85 97 109 121 
200 
150 
100 
50 
0 
Observation 
Individual Value 
UC L=70.9 
_ 
X=27.4 
LC L=-16.1 
Baseline Improv emeInmt p1rov ement 2 
1 13 25 37 49 61 73 85 97 109 121 
200 
150 
100 
50 
0 
Observation 
Moving Range 
UC L=53.4 
__ 
MR=16.4 
LC L=0 
5 
1 
1 
1
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
The Imperative of Linking Clinical and Financial Data to Improve Outcomes - HAS Session 17
The Imperative of Linking Clinical and Financial Data to Improve Outcomes - HAS Session 17
Asthma Care 
Outcomes 
Dashboard
Financial conversations
Margin 
47 
y = 106.48x - 855.25 
y = 291.62x - 3062.5 
$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 
2011 Q1 2011 Q2 2011 Q3 2011 Q4 2012 Q1 2012 Q2 2012 Q3 2012 Q4 2013 Q1 2013 Q2 2013 Q3 2013 Q4 2014 Q1 2014 Q2
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 
Data 
reporting 
Data 
analytics 
Decision 
support 
Predictive 
analytics 
Organizational 
evolution over 
time 
-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 
--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 
Improved 
outcomes for 
our patients 
and our 
enterprise
Predictive analytics: High risk asthma 
SHORT ACTING BETA AGONISTS 
6 to 9 SABA = 1 point 
≥ 10 SABA = 2 points 
EC UTILIZATION 
1-2 ER = 1 point 
> 2 ER = 2 points 
HOSPITALIZATION 
1 hospitalization = 1 point 
>= 2 hospitalizations = 4 points 
NUMBER PRESCRIBING PROVIDERS 
>= 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 
Age 1-5 , 4 of 5 below 
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 
Age 6+ 
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 
Targets: reduce ED visits, hospitalization, 
albuterol overuse, ICS non adherence 
Critical data source: TCHP, TDSHS data 
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. 
Targets: reduce ED visits/ unscheduled PCP visits 
Critical data source: TCH ED, PCP
Diabetes Pregnancy Asthma Transplant Pneumonia Appendicitis Newborn 
Hospital Acquired Conditions 
Sepsis and septic shock 
Obesity 
Transitions of care 
Survey explorer 
Care Process Teams 
Additionally, completed a gap strategy for 38 “registries”
Assuring an 
excellent 
patient 
experience 
QI education and 
culture change 
Data/predictive analytics: 
measuring through 
meaningful metrics 
Content 
System 
Measurement 
System 
Deployment 
System 
Improved 
Population 
Health 
Deployment 
strategy— 
Care Process 
Teams 
Evidence 
Integrated 
practice via 
guidelines, order 
sets and 
measures 
Using and 
innovating best 
practices 
Knowledge 
management for 
population health
Analytic 
Insights 
Questions & 
A 
Answers
54 
Session Feedback Survey 
5 
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?

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The Imperative of Linking Clinical and Financial Data to Improve Outcomes - HAS Session 17

  • 1. 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
  • 2. 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.
  • 3. 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
  • 4. Quality Defined Institute of Medicine domains:  Safe  Effective  Efficient  Timely  Patient centered  Equitable 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. Importance of minimizing unintended variation in health care delivery 1 2 3
  • 5. 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. 4 Value= Quality Cost
  • 6. 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
  • 7. 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 6 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, NeumanMI. Variation in resource utilization across a national sample of pediatric emergency departments. J Pediatr. 2013
  • 8. 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
  • 9. 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
  • 10. Poll Question #1 In your organization, what percentage of patient visits are your physicians talking about cost and care tradeoffs at the bedside? 9 a) 0-19% b) 20-39% c) 40-59% d) 60-79% e) 80-100% f) Unsure or not applicable
  • 11. Physicians and Care Cost Evidence Patient Source: SAEM. Evidence Based Medicine Online Course 2005 Clinical Expertise values and preferences Physician preferences Resource issues
  • 12. Once taboo, physicians should take cost into consideration: No Money No Mission No Expansion No Innovation And so providers must…..  Understand what creates improvements  Understand the story that their data tells. 11
  • 13. 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
  • 14. A data management strategy to improve outcomes IMPROVED OUTCOMES from high quality of care DEPLOYMENT SYSTEM Operations ANALYTIC SYSTEM Data analytics and collaborative data Patient centric outcomes and institutional outcomes achieved SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction) CLINICAL CONTENT SYSTEM Science and evidence Advanced Quality Improvement course, QI curriculum, Care process teams Informatics, Electronic Data Warehousing Evidence Based Guidelines and Order sets, Clinical Decision Support, patient and provider materials
  • 15. Creating a foundation for EB practice IMPROVED OUTCOMES from high quality of care DEPLOYMENT SYSTEM Operations CLINICAL CONTENT SYSTEM Science and evidence ANALYTIC SYSTEM Data analytics and collaborative data Evidence Based Guidelines and Order sets, Clinical Decision Support, patient and provider materials SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction)
  • 16. Evidence-Based Guidelines: EBOC 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 Acute Chest Syndrome Acute Gastroenteritis Acute Heart Failure Acute Hematogenous Osteomyelitis Acute Ischemic Stroke Acute Otitis Media Appendicitis Arterial Thrombosis Asthma Bronchiolitis Cancer Center Procedural Management Cardiac Thrombosis Central Line-Associated Bloodstream Infections Closed Head Injury Community-Acquired Pneumonia Cystic Fibrosis – Nutrition/GI >12 y/o Autism Assessment and Diagnosis C-spine Assessment Intraosseus Line Placement IV Lock Therapy Postpartum Hemorrhage
  • 17. 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? 16 a) 5% b) 10% c) 15% d) 25% e) 50% f) Unsure or not applicable
  • 18. 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 Informatics, Electronic Data Warehousing SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction)
  • 19. Metadata: EDW Atlas Security and Auditing Common, Linkable Vocabulary; Late binding Financial Source Marts Administrative Source Marts Departmental Source Marts Patient Source Marts EMR Source Marts HR Source Mart FINANCIAL SOURCES (e.g. EPSi,) ADMINISTRATIVE SOURCES (e.g. API Time Tracking) EMR SOURCE (e.g. Epic) DEPARTMENTAL SOURCES (e.g. Sunquest Labs) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, Human Resources (e.g. PeopleSoft) TCH’s EDW Architecture Operations • Labor productivity • Radiology • Practice Mgmt • Financials • Patient Satisfaction • + others Clinical • Asthma • Appendectomy • Deliveries • Pneumonia • Diabetes • Surgery • Neonatal dz • Transplant
  • 20. Creating a foundation for QI deployment IMPROVED OUTCOMES from high quality of care DEPLOYMENT SYSTEM Operations CLINICAL CONTENT SYSTEM Science and evidence ANALYTIC SYSTEM Data analytics and collaborative data Advanced Quality Improvement course, QI curriculum, Care process teams
  • 21. Avenues for Dissemination QUALITY LEADERS National Programs and Partnerships ADVANCED Classroom (e.g. AQI Program, Six Sigma Green Belt) •Project Required INTERMEDIATE 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)
  • 22. Changes that result in process improvement Ideas Improvement 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
  • 23. Pareto 80/20 Principle in Healthcare
  • 24. TCH’s Care Process Analysis Asthma Amount of Variation Size of Clinical Process Bubble Size = Case Count Improvement Opportunity: Large processes with significant variation
  • 25. 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 #2 Care Process MD Lead #1 Care Process 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 Clinical Director Domain MD Lead Operation s Lead
  • 26. Balanced scorecard-expanded visualizations 1. Care Process Defined 2. Current Literature Research 3. Individual Ratings 5. Group Creates Final 4. Aggregate Ratings Scorecard
  • 28. Data Drives Waste Reduction: Alternative Approaches 1 box = 100 cases in a year Option 1: Focus on Outliers – the prescriptive approach Strategy eliminate the unfavorable tail of the curve (“quality assurance”) Result Ithe impact is minimal # of Cases Excellent Outcomes Poor Outcomes 1.96 std # of Cases Mean Excellent Outcomes Poor Outcomes 27
  • 29. Alternative Approaches to Waste Reduction Excellent Outcomes Poor Outcomes # of Cases Mean 1 box = 100 cases in a year Excellent Outcomes # of Cases 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
  • 30. Improving Cost Structure Through Waste Reduction Ordering Waste Workflow Waste Defect Waste 29 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
  • 31. Care Redesign Methodology Evidence against 30 CXR utilization in patients with known asthma, steroids in bronchiolitis Evidence equivocal Hypertonic saline and bronchodilators in select patients with bronchiolitis Evidence Supports Quicker steroid delivery for status asthmaticus, goal directed therapy for septic shock
  • 32. Asthma: Care Process Team Cohort, Percentage of Chest X-rays Ordered* (Oct. 2010 -Apr. 2013) Feedback of rates to hospitalists and Emergency Center clinicians 31 51% 35% 80% 70% 60% 50% 40% 30% 20% 10% 0% Oct. 10 Nov. 10 Dec. 10 Jan. 11 Feb. 11 Mar. 11 Apr. 11 May. 11 Jun. 11 Jul. 11 Aug. 11 Sep. 11 Oct. 11 Nov. 11 Dec. 11 Jan. 12 Feb. 12 Mar. 12 Apr. 12 May. 12 Jun. 12 Jul. 12 Aug. 12 Sep. 12 Oct. 12 Nov. 12 Dec. 12 Jan. 13 Feb. 13 Mar. 13 Apr. 13 Percentage Month year Order set revisions * Inpatient,Emergency Center (EC) and observation patients (Care Process Team cohort), P-Chart based upon EDW data extraction of 5/14/2013 (M& W).
  • 33. Improving Cost Structure Through Waste Reduction Ordering Waste Workflow Waste Defect Waste 32 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
  • 34. Patient presents to Emergency Dept (ED). Patient registers Patient waiting Patient evaluated by triage nurse Flow chart of a patient with acute gastroenteritis through the TCH Emergency Does patient have vomiting &/ or diarrhea Triage nurse does the following: · Vitals What is the patient’s level of dehydration? Severe dehydration Evaluate per Department: Existing process clinical symptoms Mild or Moderate dehydration Put patient in ED room Triage nurse does the following: · Give Zofran · Provide gatorade/pedialyte Is the patient vomiting? Follow TCH AGE clinical algorithm 4 3 Triage nurse does the following: · Nothing or give patient gatorade/ pedialyte BEGIN Patient waiting Patient put in ED room Patient evaluated by nurse Patient evaluated by Medical student Patient Nurse discharges patient PCA checks vital signs MD does discharge evaluated by ED resident Patient evaluated by ED fellow Is the patient ok for discharge? Patient evaluated by ED attending Fellow/ Attending does pre-transfer check Nurse-Nurse checkout occurs Bed approved ED secretary requests bed MD does admission orders Decision to admit patient orders Decision to discharge patient PCA checks vital signs Patient discharged home1 Patient transferred to inpatient bed2 Key: ___ solid arrow indicates “yes” _ _ broken arrow indicates “no” 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 Modified: 7/21/2009 Process map before EBG
  • 35. Patient presents to Emergency Dept (ED). Patient registers Patient waiting Patient evaluated by triage nurse Flow chart of a patient with acute gastroenteritis through the TCH Emergency Deparment Does patient have vomiting &/ or diarrhea clinical symptoms Triage nurse does the following: · Vitals · Assess dehydration (Gorelick score)** What is the patient’s level of dehydration? Severe dehydration Evaluate per Mild or Moderate dehydration Put patient in ED room Is the patient Triage nurse does the following: · Give Zofran · Provide patient education on ORT · Initiate ORT · Give ORT tracking sheet** vomiting? Follow TCH AGE clinical algorithm 4 3 Triage nurse does the following: · Provide patient education on ORT · Initiate ORT · Give ORT tracking sheet** BEGIN Patient waiting Patient put in ED room Patient evaluated by nurse Patient evaluated by Medical student Patient Nurse discharges patient PCA checks vital signs MD does discharge evaluated by ED resident Patient evaluated by ED fellow Patient evaluated by ED attending Bedside nurse does the following: · Assesses dehydration (Gorelick score)** · Monitors progress on ORT tracking sheet** · Reemphasizes patient education on ORT orders ED Fellow does the following: · Assesses dehydration (Gorelick score)** · Monitors progress on ORT tracking sheet** · Reemphasizes patient education on ORT · Determines patient disposition Is the patient ok for discharge? Fellow/ Attending does pre-transfer check Nurse-Nurse checkout occurs Bed approved ED secretary requests bed MD does admission orders Decision to admit patient Decision to discharge patient PCA checks vital signs Patient discharged home1 Patient transferred to inpatient bed2 Key: ___ solid arrow indicates “yes” _ _ broken arrow indicates “no” ** New process 1Outcome: 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 Collect ORT tracking sheet Process map after EBG Modified: 5/9/2009
  • 36. 35
  • 37. Improving Cost Structure Through Waste Reduction Ordering Waste Workflow Waste Defect Waste 36 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
  • 38. 37 Clinical Decision Support to Minimize Errors Streamlining and Improving Processes and Operations to Minimize Errors
  • 40. 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
  • 41. Inpatient: prolonged LOS Evidence based approach to early medication weaning • 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 Improv emeInmt p1rov ement 2 1 13 25 37 49 61 73 85 97 109 121 200 150 100 50 0 Observation Individual Value UC L=70.9 _ X=27.4 LC L=-16.1 Baseline Improv emeInmt p1rov ement 2 1 13 25 37 49 61 73 85 97 109 121 200 150 100 50 0 Observation Moving Range UC L=53.4 __ MR=16.4 LC L=0 5 1 1 1
  • 42. 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
  • 46. Asthma Care Outcomes Dashboard
  • 48. Margin 47 y = 106.48x - 855.25 y = 291.62x - 3062.5 $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 2011 Q1 2011 Q2 2011 Q3 2011 Q4 2012 Q1 2012 Q2 2012 Q3 2012 Q4 2013 Q1 2013 Q2 2013 Q3 2013 Q4 2014 Q1 2014 Q2
  • 49. 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
  • 50. Organizational direction for data Data reporting Data analytics Decision support Predictive analytics Organizational evolution over time -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 --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 Improved outcomes for our patients and our enterprise
  • 51. Predictive analytics: High risk asthma SHORT ACTING BETA AGONISTS 6 to 9 SABA = 1 point ≥ 10 SABA = 2 points EC UTILIZATION 1-2 ER = 1 point > 2 ER = 2 points HOSPITALIZATION 1 hospitalization = 1 point >= 2 hospitalizations = 4 points NUMBER PRESCRIBING PROVIDERS >= 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 Age 1-5 , 4 of 5 below 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 Age 6+ 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 Targets: reduce ED visits, hospitalization, albuterol overuse, ICS non adherence Critical data source: TCHP, TDSHS data 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. Targets: reduce ED visits/ unscheduled PCP visits Critical data source: TCH ED, PCP
  • 52. Diabetes Pregnancy Asthma Transplant Pneumonia Appendicitis Newborn Hospital Acquired Conditions Sepsis and septic shock Obesity Transitions of care Survey explorer Care Process Teams Additionally, completed a gap strategy for 38 “registries”
  • 53. Assuring an excellent patient experience QI education and culture change Data/predictive analytics: measuring through meaningful metrics Content System Measurement System Deployment System Improved Population Health Deployment strategy— Care Process Teams Evidence Integrated practice via guidelines, order sets and measures Using and innovating best practices Knowledge management for population health
  • 55. 54 Session Feedback Survey 5 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?