Quality and cost improvements require the intelligent use of financial and clinical data coupled with education for multi-disciplinary teams who are driving process improvements. Once a data warehouse is established, healthcare organizations need to set up multi-disciplinary clinical, financial, and IT specialist teams to make the best use of the data. Sometimes, financial involvement is minimized or even excluded for a number of reasons that can turn out to be counterproductive. However, including financial measurements and participation up front can help enhance the recognized value and sustainability of quality improvement or waste reduction efforts. the In this session you will learn keys to success and real-life examples of linking clinical, financial and patient satisfaction data via multi-disciplinary teams that produce impressive results.
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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
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
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
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?