CENTER FOR CLINICAL & TRANSLATIONAL SCIENCE

Carrie L. Byington, MD

H.A. and Edna Benning Presidential Professor of Pediatrics

Director, Utah Center for Clinical and Translational Science

AVP Faculty and Academic Affairs, Health Sciences

A High-Quality Electronic Health A High-Quality Electronic Health Record and EDW: Tools to Record and EDW: Tools to

Eliminate Health Disparities Eliminate Health Disparities

CENTER FOR CLINICAL & TRANSLATIONAL SCIENCE

Etiologies of Health Disparities

• Insurance coverage• Access to care

– Preventive Services

• Cultural and language barriers• Geographic or facility barriers

– Relevant to care of children

• Stereotyping, overt or implicit bias

134 133 133140

155

0

40

80

120

160

200

Total White, non-

Hispanic

Black, non-

Hispanic

Hispanic Asian/Pac ific

Islander

Note: Complications of care include postoperative pneumonia, urinary tract infection, and blood clot in the leg.

Note: Estimates are adjusted by age, gender, age–gender interactions, comorbidities, and DRG clusters.

Source: Agency for Healthcare Research and Quality. National Healthcare Disparities Report. 2006.

Asians/Pacific Islanders and Hispanics are more likely to die from complications in hospital care than Whites or Blacks

Deaths per 1,000 discharges with complicationsof care in hospitalization, 2003

Centers for Disease Control and Prevention

7

3643

54

70

85 87767370

6253

46

83 84

6963

0

20

40

60

80

100

1993 1994 1995 1996 1997 1998 1999 2000

White

Black

Quality improvement efforts in dialysis care are associated with improved quality overall and smaller disparities between black and

white patients

Note: p<0.001.Source: A. R. Sehgal, “Impact of Quality Improvement Efforts on Race and Sex Disparities in Hemodialysis,” Journal of the American Medical Association, Feb. 26, 2003 289(8):996–1000.

Percentage of patients age 18 and over receivingadequate hemodialysis dose, 1993–2000

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IOM Recommendation• Efforts designed to improve the quality of health

services may result in decreasing disparities• If every person received the indicated care at

the right time, then theoretically differences (and disparities) in their care would not exist

• The rigorous application of evidence-based care to reduce disparities is needed

The Febrile Infant: Using Evidence to

Reduce Disparities

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Background• Fever in infants 1-90 days of age is one of the most common

reasons for medical encounters– 58% of all ED visits at PCMC

• Fever of ≥38°C is associated with serious bacterial infection (SBI)– Bacteremia, meningitis, and UTI

• 42% of febrile infants with bacteremia or bacterial meningitis evaluated by experienced PROS practitioners appeared clinically well– Pantell 2004

•Integrated not-for-profit •24 Hospitals•144 Clinics•736 employed & 2,000+ affiliated physicians•Serves >90% of Utah Infants•Guidance Council Mechanism

Intermountain Healthcare

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Evidence Base• Screening Criteria are essentially equivalent

(Rochester, Philadelphia, Boston) AHRQ March 2012

• UTI most common SBI• Age and Viral status effect risk of having SBI• Bacterial epidemiology allows tailored

antimicrobial therapy

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Evidence Base Continued• Early discharge can be accomplished

safely

Generated in Utah @ University and Intermountain with support of the CCTS

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Intermountain EDW• The EDW is a large database comprised of

data from most of Intermountain’s electronic systems

• Includes– financial and clinical datasets.

• Data are integrated, organized, structured and cataloged to facilitate population-based analysis, queries, and research.

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Methods• We developed and validated a surrogate

definition that allows us to identify febrile infants using an administrative database

• Sensitivity of surrogate definition: 93%

• Specificity of surrogate definition: 90%

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Identifying Febrile Infants

Pediatrics July 2012

An Evidence-Based Care Process Model

Measure Baseline Implementation P-Value

Core Labs 60% 80% <0.001

HR 68% 74% <0.001

Viral Testing 76% 84% <0.001

Antibiotic Formulary

77% 92% <0.001

D/C Antibiotics 47% 63% <0.001

D/C Home 48% 75% <0.001

Quality Measures-Decreased Variation

Outcome P-Value

Diagnosis of UTI (29%) <0.001

Diagnosis of Viral Illness (40%) <0.001

Antibiotics in LR Infants (26%) 0.002

Length of Stay (27%) <0.001

= Readmission (< 1%) 0.70

Admission of Bacteremia or Meningitis at First Encounter

91% vs. 99% (p=0.06)

Infant Outcomes

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Did racial and ethnic disparities exist?

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• We used validated definitions for febrile infants• Infants were classified as White (W) or Under-

represented minorities (URM)– URM infants included

• American Indian, Asian, Black, Native American, Pacific Islander race or those of any race identified as Hispanic/Latino

• We evaluated quality indicators before and after EB-CPM

Infant Cohort

• 16,987 Febrile Infant Episodes from July 1, 2004- June 30, 2014– PRE EB-CPM July 2004-December 2007– POST EB-CPM January 2008-June 2014

• 6011 episodes (35%) in URM infants

Insurance Coverage

PRE-EBCPMMedicaid

•White Infants 22.9%•URM Infants 48.2%

P< 0.001

POST-EBCPMMedicaid

•White Infants 27.7%•URM Infants 56.3%

P< 0.001

Disparities PRE-EBCPM

White URM P-ValueIdentification of HR

47.2% 43.8% 0.01

HR discharged home for care

9.3% 12.1% 0.004

Unscheduled Admission

5.3% 9.1% < 0.001

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Post EB-CPM• URM infants were more likely to be diagnosed

with SBI– 11.9% vs. 8.3% (p < 0.001)

• The proportion of URM infants admitted increased – 40.3% to 43.5% (p=0.02)

• Unscheduled admissions decreased – 9.1% to 4.1% (p < 0.001)

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Conclusions• Following implementation of an EB-CPM we

demonstrated– Decreased variation in care

• Geographic and facility based

– Lower costs– Improved outcomes for all infants– Elimination of disparities between White and URM

infants

Questions?

Primary Children’s Medical Center FoundationRobert Wood Johnson Foundation

NIH/NCRR M01-RR00064 and 1UL1RR024764NIH/NICHD K24 HD047249

NIH/NIAID U01-A1061611 and U-01 A1074419 and U-01 AI082482AHRQ R18HS018034

The HA and Edna Benning SocietyCarrie.byington@hsc.utah.edu

Mankind has three great enemies, fever, famine, and war. And of these by far the greatest is fever.―William Osler, 1897