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Hospital Admission Rates Through the Emergency Department: An Important, Expensive Source of Variation

Slide Presentation from the AHRQ 2011 Annual Conference

On September 21, 2011, Jesse Pines made this presentation at the 2011 Annual Conference. Select to access the PowerPoint® presentation (2 MB). 


Slide 1

Hospital Admission Rates Through the Emergency Department: An Important, Expensive Source of Variation

Jesse M. Pines, MD, MBA, MSCE
Mark Zocchi
George Washington University
AHRQ Annual Meeting

Slide 2

Disclosures / Funding

  • AHRQ.
  • Robert Wood Johnson Foundation.
  • National Priorities Partnership on Aging.
  • Department of Homeland Security.
  • Kingdom of Saudi Arabia.

Slide 3

Study team

  • Ryan Mutter (AHRQ).
  • Mark Zocchi (GWU).
  • Andriana Hohlbauch (Thomson-Reuters).
  • David Ross (Thomson-Reuters).
  • Rachel Henke (Thomson-Reuters).

Slide 4

Introduction

  • HCUP Data: 125 million ED visits in 2008:
    • 15.5% admission rate.
    • 19.4 million hospitalizations.
    • ED visit growth outpacing population growth.
  • Why are EDs so popular?
    • Variable outpatient primary care availability.
    • High-technology care has become the standard.
    • Patient preferences / convenience.

Slide 5

Introduction

  • EDs are becoming the hospital's front door.
  • 2008 v. 1997:
    • 43% of U.S. hospital admissions originated in the ED v. 37%.
    • Mean charge per hospital stay—$29,046 v. $11,281.

Slide 6

Introduction

  • Why are ED admissions important?
    • Variation in inpatient charges are one of the major drivers of cost variation.

Images of chart showing inpatient charges and map of United States showing average charges by state.

Welch NEJM 1993

Slide 7

Introduction

  • Hospital Care Intensity (HCI).

Image: A map of the United States is shown.

www.dartmouthatlas.org

Slide 8

Introduction

  • The perspective of the ED.
  • Why admit someone?
    • Requires hospital resources.
    • Critically ill.
    • Is unable to access a timely resource outside the hospital.
    • Has a high-risk presentation.
    • Other reasons.

Slide 9

Introduction

  • Variation in the decision to admit from the ED:
    • 2-3 fold variation in the decision for primary care practices to hospitalize on emergency basis.
    • Individual ED physician admission rates vary in Canada: 8%—17%.
    • Emergency physicians more likely to admit than family physicians or internal medicine physicians.
    • Differences in risk tolerance by individual physicians.
    • Malpractice fear.
    • Differences in patient & community resources.

Slide 10

Introduction

  • Three categories:
    • Clear cut admissions:
      • AMI, stroke, severely-injured trauma.
    • Clear cut discharges:
      • Minor conditions.
    • The remainder:
      • Shades of gray.

Slide 11

Specific Aims

  • Explore the regional variation in hospital-level ED admission rate across a wide sample of hospitals.
  • Determine predictors the hospital-level ED admission rate:
    • Hospital-level factors, ED case-mix, and age-mix, and local economic factors that may drive differences in admission rate.
  • Determine the contribution of local standards of care to explain hospital-level variation in admission rate.

Slide 12

Methods

  • HCUP Data from 2008.
  • All ED encounters from the 2,558 hospital-based EDs in the 28 states:
    • Had a SID and a SEDD to HCUP in 2008.
  • Calculate an admission rate for each ED:
    • Transfers included as admissions.

Slide 13

Methods

  • Exclusions:
    • EDs removed "atypical characteristics":
      • 639 EDs removed with an annual volume < 8,408, the 25th percentile.
      • Removed 4 EDs with admit rate > 49%.
    • HCUP requirements:
      • Counties < 2 hospitals not appear in a map.
    • Additional exclusions:
      • Empirical analysis of the effects of local practice patterns on a facility's ED admission rate.
      • Excluded 493 facilities that had the only ED in the county.
  • 1,376 EDs: Final sample.

Slide 14

Methods

  • Calculated variables:
    • County-level ED admission rate.
    • Age-mix proportions.
    • Insurance proportions.
    • Case-mix: 25 most common CCS categories.
  • Other characteristics:
    • Hospital factors (2008 AHA survey).
    • Trauma-level (2008 TIEP survey).
    • Community-factors (2007-8 ARF).

Slide 15

Methods

  • Mapped of ED admission rates at the county level.
  • Each ED's admission rate was weighted by its annual volume.
  • Counties that did not have a sufficient number of EDs or which are in states that did not provide a SID and a SEDD are in gray.

Slide 16

Methods

  • Adjusted analysis:
    • Other factors associated with variations in ED admission rates using multivariate analysis.
    • Hospital-level ED admission rate (dependent variable).
    • Natural log of the dependent variable and the continuous independent variables so that the coefficients on the regressors are elasticities.
    • Clustered at the hospital-level.

Slide 17

Results

Variable Mean Std. Dev.
Patient Characteristics of EDs    
% of ED encounters resulting in admission or transfer 17.5 6.5
% of ED encounters paid by Medicare 21.7 7.16
% of ED encounters paid by Medicaid 20.8 11.0
% of ED encounters paid by private insurance 36.8 13.8
% of ED encounters by the uninsured 15.9 9.0
% of ED encounters paid by other source 4.8 4.5
% of ED encounters aged 0 to 17 18.8 7.5
% of ED encounters aged 18 to 34 28.2 5.1
% of ED encounters aged 35 to 54 25.4 3.8
% of ED encounters aged 55 to 64 9.1 1.7
% of ED encounters aged 65+ 18.4 7.0

Slide 18

Results

Variable Mean Std. Dev.
Hospital Characteristics of EDs    
Number of inpatient beds 265.5 225.0
ED volume 40,903.9 28,462.8
% of EDs at teaching hospitals 31.5 46.5
% of EDs in an urban location 87.3 33.3
% of EDs at for-profit hospitals 15.5 36.3
% of EDs at non-profit hospitals 72.4 44.7
% of EDs at Level 1 trauma centers 8.9 28.5
% of EDs at Level 2 trauma centers 9.7 29.7
% of EDs at Level 3 trauma centers 7.6 26.4
% of EDs at non-trauma centers 73.8 44.0
Socioeconomic and marketing characteristics of EDs
% of ED encounters resulting in admission, county level with subject ED excluded 18.0 7.1
Per capita income, county level $39,954.1 13,268.7
General practice MDs providing patient care per 100,000 county level 29.1 13.8

Slide 19

County ED Admission Rates (With Transfers Counted as Admissions)

Image: A map of the United States is shown with rates of ED admission.

Slide 20

County ED Admission Rates (With Transfers Counted as Admissions)

Image: A map of the United States with rates of ED admissions in columns is shown.

Slide 21

Adjusted analysis

Variable Coefficient t-statistic
Intercept 2.746** 4.62
Patient Characteristics of EDs    
% of ED encounters paid by Medicare 0.236** 6.61
% of ED encounters paid by Medicaid 0.003 0.19
% of ED encounters by the uninsured 0.007 1.31
% of ED encounters paid by other source 0.012 1.50
% of ED encounters aged 0 to 17 0.001 0.04
% of ED encounters aged 18 to 34 -0.181* -2.37
% of ED encounters aged 35 to 54 0.065 0.70
% of ED encounters aged 55 to 64 0.015 0.20
** p < .01
* p < .05
† p < .10

Slide 22

Adjusted Analysis

Hospital Characteristics of EDs Coefficient T-statistic
Number of inpatient beds 0.168** 9.04
ED volume -0.080** -3.01
Teaching hospital 0.032 1.72
Urban location 0.004 0.13
For-profit hospital 0.054 1.95
Non-profit hospital -0.012 -0.56
Level 1 trauma center 0.118** 4.66
Level 2 trauma center 0.014 0.64
Level 3 trauma center 0.006 0.27
Socioeconomic and market characteristics of EDs    
% of ED encounters resulting in admission, county level with subject ED excluded 0.145** 4.78
Per capita income, county level 0.007 0.21
General practice MDs providing patient care per 100,000, county level -0.073** -3.68

** p < .01
* p < .05
† p < .10

Slide 23

Discussion

  • Patient-level characteristics:
    • % Medicare (higher → higher).
    • % 18-34 (higher → lower).
  • Hospital-level characteristics:
    • Number of inpatient beds (higher → higher).
    • ED volume (higher → lower).
    • Teaching hospital (Yes → higher).
    • Level 1 trauma center (Yes → higher).

Slide 24

Discussion

  • Community-level characteristics:
    • County-level admission rate (higher → higher).
    • Number of primary care doctors (higher → lower).

Slide 25

Conclusion

  • There is tremendous variability in ED admission rates across 28 states:
    • May be the most expensive, regular discretionary decision in U.S. healthcare.
  • Patient & Hospital-level factors predict admission rates:
    • Medicare & hospitals more likely to receive admissions (trauma, teaching, larger).

Slide 26

Conclusion

  • Community-factors:
    • Significant standard of care effect.
    • Impact of local primary care MDs.

Slide 27

Future Directions

  • Exploring specific diagnoses that may drive this impact:
    • Pneumonia, DVT, Chest pain, others.
  • Testing solutions to control variation:
    • Clinical decision rules.
    • Enhancing care coordination.
Page last reviewed October 2014
Internet Citation: Hospital Admission Rates Through the Emergency Department: An Important, Expensive Source of Variation. October 2014. Agency for Healthcare Research and Quality, Rockville, MD. http://archive.ahrq.gov/news/events/conference/2011/pines/index.html

 

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