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

a system towards the triple aim.

through improvement science can transform

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.

Value

=

Quality Cost

4

I

n 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

Evidence Clinical Expertise Resource issues

Source: SAEM. Evidence Based Medicine Online Course 2005

Patient values and preferences Physician preferences

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

Number of Beds Annual Inpatient Admissions Statistics 469 21,744 1.44 million Annual Outpatient Visits Emergency Room Visits 82,049 Inpatient Surgeries 8,655 14,439 Outpatient Surgeries

A data management strategy to improve outcomes

IMPROVED OUTCOMES from high quality of care

Patient centric outcomes and institutional outcomes achieved Evidence Based Guidelines and Order sets, Clinical Decision Support, patient and provider materials

DEPLOYMENT SYSTEM Operations CLINICAL CONTENT SYSTEM Science and evidence

Advanced Quality Improvement course, QI curriculum, Care process teams Informatics, Electronic Data Warehousing

ANALYTIC SYSTEM Data analytics and collaborative data

SOURCE SYSTEMS (e.g. EMR, Financial, Costing, Patient Satisfaction)

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 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 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,) ADMINISTRATIVE SOURCES (e.g. API Time Tracking)

Financial Source Marts Administrative Source Marts Clinical

• Asthma • Appendectomy • Deliveries • Pneumonia • Diabetes • Surgery • Neonatal dz • Transplant

EMR Source Marts Departmental Source Marts Operations

• Labor productivity • Radiology • Practice Mgmt • Financials • Patient Satisfaction • + others

Patient Source Marts HR Source Mart

EMR SOURCE (e.g. Epic) DEPARTMENTAL SOURCES (e.g. Sunquest Labs) PATIENT SATISFACTION SOURCES (e.g. NRC Picker, 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 BEGINNER

Classroom (e.g. AQI Program, Six Sigma Green Belt) •Project Required Online and Classroom (IHI Educational Resources, PEDI 101, EQIPP, Fellows College) •Project Required 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

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

MD Lead #5 Care Process

Clinical Program

Guidelines centered on evidence-based care MD Lead #4 Care Process MD Lead #3 Care Process MD Lead #2 Care Process MD Lead #1 Care Process Operation s Lead Clinical Director Domain MD Lead 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.

Individual Ratings 1.

Care Process Defined 2.

Current Literature Research 5.

Group Creates Final Scorecard 4.

Aggregate Ratings

Severity Adjusted Variation

Data Drives Waste Reduction: Alternative Approaches

1.96 std

# of Cases 1 box = 100 cases in a year Mean # of Cases Excellent Outcomes Poor Outcomes Excellent Outcomes

Option 1: Focus on Outliers – the prescriptive approach Strategy

eliminate the unfavorable tail of the curve (“quality assurance”)

Result

Ithe impact is minimal Poor Outcomes 27

Alternative Approaches to Waste Reduction

# of Cases 1 box = 100 cases in a year Mean # of Cases Excellent Outcomes Poor Outcomes Excellent Outcomes Poor Outcomes

Option 2: Focus On Inliers – improving quality outcomes across the majority Strategy

variation Evidence and analytics applied through EBP clinical standards targets inlier

Result

impact Shifting more cases towards excellent outcomes has much more significant 28

Improving Cost Structure Through Waste Reduction

Ordering Waste

Ordering of tests that are neither diagnostic nor contributory

Workflow Waste Defect Waste

Variation in Emergency Care wait time ADEs, transfusion reactions, pressure ulcers, HAIs, VTE, falls, wrong surgery 29

Care Redesign Methodology

Quicker steroid delivery for status asthmaticus, goal directed therapy for septic shock Hypertonic saline and bronchodilators in select patients with bronchiolitis Evidence Supports Evidence equivocal Evidence against CXR utilization in patients with known asthma, steroids in bronchiolitis 30

80% 70% 60% 50% 40% 30% 20% 10% 0% Asthma: Care Process Team Cohort, Percentage of Chest X-rays Ordered* (Oct. 2010 - Ap r. 2013) Feedba ck of ra tes to hospita lists a nd Em ergency Center clinicia ns 51% Order set revisions 35% Month yea r * Inpa tient, Em ergency 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

Ordering of tests that are neither diagnostic nor contributory

Workflow Waste Defect Waste

Variation in Emergency Care wait time ADEs, transfusion reactions, pressure ulcers, HAIs, VTE, falls, wrong surgery 32

BEGIN Patient presents to Emergency Dept (ED).

Patient registers Patient waiting Patient evaluated by triage nurse Put patient in ED room Follow TCH AGE clinical algorithm

Flow chart of a patient with acute gastroenteritis through the TCH Emergency Department: Existing process

4 Patient discharged home

1

3 Does patient have vomiting &/ or diarrhea Evaluate per clinical symptoms Patient transferred to inpatient bed

2

Triage nurse does the following: · Vitals What is the patient’s level of dehydration?

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

Nurse discharges patient Fellow/ Attending does pre transfer check PCA checks vital signs Nurse-Nurse checkout occurs Bed approved Severe dehydration Triage nurse does the following: · Give Zofran · Provide gatorade/pedialyte Patient waiting Patient put in ED room PCA checks vital signs ED secretary requests bed Mild or Moderate dehydration Is the patient vomiting?

MD does discharge orders Decision to discharge patient MD does admission orders Decision to admit patient Triage nurse does the following: · Nothing or give patient gatorade/ pedialyte Patient evaluated by nurse Is the patient ok for discharge?

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

BEGIN Patient presents to Emergency Dept (ED).

Flow chart of a patient with acute gastroenteritis through the TCH Emergency Deparment

Does patient have vomiting &/ or diarrhea Evaluate per clinical symptoms 4 Patient discharged home

1

3 Patient transferred to inpatient bed

2

Fellow/ Attending does pre transfer check PCA checks vital signs Patient registers Patient waiting Triage nurse does the following: · Vitals ·

Assess dehydration (Gorelick score)**

What is the patient’s level of dehydration?

Key:

___ solid arrow indicates “yes” _ _ broken arrow indicates “no”

** 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

Nurse discharges patient Collect ORT tracking sheet Nurse-Nurse checkout occurs Bed approved Patient evaluated by triage nurse PCA checks vital signs ED secretary requests bed Severe dehydration Mild or Moderate dehydration MD does discharge orders MD does admission orders Is the patient vomiting?

Put patient in ED room Follow TCH AGE clinical algorithm Triage nurse does the following: · Provide patient education on ORT · Initiate ORT ·

Give ORT tracking sheet**

Decision to discharge patient Decision to admit patient Triage nurse does the following: · Give Zofran · Provide patient education on ORT · · Initiate ORT

Give ORT tracking sheet**

Is the patient ok for discharge?

Patient waiting Patient put in ED room 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 Patient evaluated by ED resident Patient evaluated by ED fellow Patient evaluated by ED attending ED Fellow does the following: · · · · Determines patient disposition

Process map after EBG

Reemphasizes patient education on ORT Modified: 5/9/2009

35

Improving Cost Structure Through Waste Reduction

Ordering Waste

Ordering of tests that are neither diagnostic nor contributory

Workflow Waste Defect Waste

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

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

1 200 150 100 50 0 Baseline 1 13 25 37 5 49 61

Obser vation

73 85 97 109 121 U C L=70.9

_ X=27.4

LC L=-16.1

200 150 100 50 0 1 Baseline 13 25 37 1 1 49 61

Obser vation

73 85 97 109 121 U C L=53.4

__ M R=16.4

LC L=0

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 2012 Q4 2013 Q1 2013 Q2 2013 Q3 2013 Q4 2014 Q1 2014 Q2

y = 291,62x - 3062,5

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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

Data reporting

-EMR clinical reports -Financial reports Organizational evolution over time

Data analytics

-Shortening event to reporting time -Transforming data and translating to action

Decision support

-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

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 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

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

Care Process Teams

Diabetes Pregnancy Asthma Transplant Pneumonia Hospital Acquired Conditions Sepsis and septic shock Obesity Appendicitis Transitions of care Survey explorer Newborn 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

Improved Population Health

QI education and culture change Deployment strategy — Care Process Teams

Measurement System

Data/predictive analytics: measuring through meaningful metrics

Deployment System Knowledge management for population health

Analytic Insights Questions &

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?

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