Exploring a New Mechanism Increasing Emergency Department Visits

Katrina HullUniversity at AlbanyApril 17, 2015

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Contents

• Background• Current Theories• Dynamic Hypothesis• Model Scenario• Model Results• Discussion

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Emergency Department Crowding

• Definition– Wait times– Ambulance diverting

• Historical trend– Increase in per capita use, twice what would be

predicted by population growth– This research focuses on the increased per capita

use

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Source: Kaiser Family Foundation <http://www.statehealthfacts.org/comparetrend.jsp?sub=217&sortc=1&o=a&ind=388&typ=1&sort=a&cat=8&yr=138&srgn=1>

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Research Goal

• Propose an endogenous dynamic mechanism increasing emergency department visits

• Develop a model of this hypothesis• Use the model to determine useful empirical

data to support this hypothesis

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Health of ED Patients

• Ambulance diversions correlated to poorer outcomes for heart attack patients

• Overcrowded EDs may result in lower quality of care– Patient boarding– Stressed physicians less effective

• Stakeholders– ED Patients

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Internal Process Variables

• Premise: A better structured ED and Hospital could handle the increased load

• Prior focus of system dynamics work• Models examine patient flow through the ED to

discharge (appropriate or not) or admission• ED as backdoor to admissions• Stakeholders– ED staff– Hospitals

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General Population Health

• Increase ED use as symptom of poorer health in the population

• Ambulatory sensitive care conditions• Failure of system to care for vulnerable

populations• ED visits are urgent but should be avoidable• Stakeholders– Society– Patients

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Literature Summary

• Lot of exogenous theories• Multiple actors with disparate motivations• Conflicting policy approaches • Perfect space for a model– Unify multiple stakeholder perspectives– Highlight interaction of their activities– Examine outcomes of suggested policies

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Dynamic Hypothesis

• Where would ED patients come from within the healthcare system?

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The Story

• Costs of hospital admissions rose due to factors such as improved medical technology

• Payers became alarmed at the cost of hospital admissions

• Policies were created to reduce the cost of individual admissions and reduce total admissions

• The unintended consequence was more emergency department visits

19901992

19941996

19982000

20022004

20062008

20102012

2014300

320

340

360

380

400

420

440

Model Historic No Change

ED V

istis

per

100

0Reference Mode

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Physicians Redirecting Patients

Increase OutsideModel Boundary

Historical Data from Avalere Health analysis of American Hospital Association Annual Survey data

Feedback Loops

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total ED visits

total costs topayers

Expectedcosts

pressure from payerto reduce hospital use

wait time to admitgp referral

Expected waittime for gp

referralpercent of gp admits

redirected to ED

ED referralsby gp

-

RedirectingGP referrals

to ED

Adjustmentto Increasing

Costs

Payer Sector

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pressure from payer toreduce hospital use

time adjustedcost per ED visit

<total admissions>

expectedcostschng expected

costs

total costs

<Total ED Visits>

time to changeexpectations

- -

<Time>

ED costs

Time adjusted costper admission

<Time>

Total inpatient cost

Time Adjusted Costs

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1990 1995 2000 2011 2015$0

$200

$400

$600

$800

$1,000

$1,200

$1,400

Time Adjusted Cost per ED Visit

1990 1995 2000 2006 2012 2015$0

$2,000

$4,000

$6,000

$8,000

$10,000

$12,000

$14,000

Time Adjusted Cost per Hospital Admission

Based on time series data from 1990, 2000 and 2010 (2015 value extrapolated based on exponential growth)

General Practitioner Sector

16gp visits per

capita

actual wait time toadmit gp referral

ED referrals bygp

effect of wait time toadmit on gp referrals to

ed

percent of gp visitsrequiring hospital

admission

normal wait time toadmit gp referral

percent of gp admitsredirected to ed

desired admitsfrom gp

total gp visitsper year

Expected wait timefo gp referralchg expected wait

for gp referral

-

time to adjustexpectation

effect of costs onartificial barriers to direct

referrals

<Population>

<pressure from payer toreduce hospital use>

direct admitsfrom gp

-

payer sensitivityto pressure

expected overnormal wait time

gp sensitivity towait

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Unknown Parameters

• Normal wait times• Actual wait times• Percent of patients referred to ED• BUT, these don’t matter because the model is

normalized, the key unknowns are:– Payer sensitivity to pressure– GP sensitivity to pressure

• GP and Payer time to adjust expectations also unknown

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Sensitivity Testing

• GP Sensitivity to wait times is the slope of the Effect of Wait Times on Referrals to ED

• This effect was formulated using a Gompertz function rather than as a lookup to allow for easier sensitivity testing

• Similar for Payer sensitivity

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Effect of Wait Times on GP Redirects

0 1 1.5 2 2.5 3 3.5 4 4.5 5 5.6 6 6.5 7 7.5 80.00

0.10

0.20

0.30

0.40

0.50

0.60

0.70

0.80

0.90

1.00

Expected over Normal Wait Time

Perc

ent o

f Pati

ent R

edire

cted

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Final Value of Per Capita ED Visits

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In 2 DimensionsSurface Base Run50% 75% 95% 100%

ED visits per 1000400

375

350

325

3001990 1996 2002 2008 2015

Date

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Estimating Sensitive Parameters

• Next step is find data on these sensitive parameters

• Where? I don’t know yet.