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). Plugin Software Help.


Slide 1

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

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

Disclosures / Funding

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

Slide 3

Study team

Study team

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

Slide 4

Introduction

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

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

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

Introduction

  • Hospital Care Intensity (HCI).

Image: A map of the United States is shown.

www.dartmouthatlas.org

Slide 8

Introduction

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

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

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

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

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

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

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

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

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

Results

VariableMeanStd. Dev.
Patient Characteristics of EDs  
% of ED encounters resulting in admission or transfer17.56.5
% of ED encounters paid by Medicare21.77.16
% of ED encounters paid by Medicaid20.811.0
% of ED encounters paid by private insurance36.813.8
% of ED encounters by the uninsured15.99.0
% of ED encounters paid by other source4.84.5
% of ED encounters aged 0 to 1718.87.5
% of ED encounters aged 18 to 3428.25.1
% of ED encounters aged 35 to 5425.43.8
% of ED encounters aged 55 to 649.11.7
% of ED encounters aged 65+18.47.0

Slide 18

Results

Results

VariableMeanStd. Dev.
Hospital Characteristics of EDs  
Number of inpatient beds265.5225.0
ED volume40,903.928,462.8
% of EDs at teaching hospitals31.546.5
% of EDs in an urban location87.333.3
% of EDs at for-profit hospitals15.536.3
% of EDs at non-profit hospitals72.444.7
% of EDs at Level 1 trauma centers8.928.5
% of EDs at Level 2 trauma centers9.729.7
% of EDs at Level 3 trauma centers7.626.4
% of EDs at non-trauma centers73.844.0
Socioeconomic and marketing characteristics of EDs
% of ED encounters resulting in admission, county level with subject ED excluded18.07.1
Per capita income, county level$39,954.113,268.7
General practice MDs providing patient care per 100,000 county level29.113.8

Slide 19

County ED Admission Rates (With Transfers Counted as Admissions)

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)

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

Adjusted analysis

VariableCoefficientt-statistic
Intercept2.746**4.62
Patient Characteristics of EDs  
% of ED encounters paid by Medicare0.236**6.61
% of ED encounters paid by Medicaid0.0030.19
% of ED encounters by the uninsured0.0071.31
% of ED encounters paid by other source0.0121.50
% of ED encounters aged 0 to 170.0010.04
% of ED encounters aged 18 to 34-0.181*-2.37
% of ED encounters aged 35 to 540.0650.70
% of ED encounters aged 55 to 640.0150.20
** p < .01
* p < .05
† p < .10

Slide 22

Adjusted analysis

Adjusted Analysis

Hospital Characteristics of EDsCoefficientT-statistic
Number of inpatient beds0.168**9.04
ED volume-0.080**-3.01
Teaching hospital0.0321.72
Urban location0.0040.13
For-profit hospital0.0541.95
Non-profit hospital-0.012-0.56
Level 1 trauma center0.118**4.66
Level 2 trauma center0.0140.64
Level 3 trauma center0.0060.27
Socioeconomic and market characteristics of EDs  
% of ED encounters resulting in admission, county level with subject ED excluded0.145**4.78
Per capita income, county level0.0070.21
General practice MDs providing patient care per 100,000, county level-0.073**-3.68

** p < .01
* p < .05
† p < .10

Slide 23

Discussion

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

Discussion

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

Slide 25

Conclusion

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

Conclusion

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

Slide 27

Future Directions

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 March 2012
Internet Citation: Hospital Admission Rates Through the Emergency Department: An Important, Expensive Source of Variation. March 2012. Agency for Healthcare Research and Quality, Rockville, MD. http://www.ahrq.gov/news/events/conference/2011/pines/index.html