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

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

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.

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

Slide 18

Results

Slide 19

County ED Admission Rates (With Transfers Counted as Admissions)

Image: A map of the United States is shown.

Slide 20

County ED Admission Rates (With Transfers Counted as Admissions)

Image: A map of the United States is shown.

Slide 21

Adjusted analysis

Slide 22

Adjusted Analysis

** 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.