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Hospital overcrowding and emergency admission mortality:

A new imperative for patient safety

Peter Sprivulis MBBS PhD, Julie-Ann Da Silva BPsych, Ian Jacobs RN PhD, Amanda Frazer MBBS LLB, George Jelinek MD FACEM

For the Emergency Care, Hospitalization & Outcome Study (ECHO)

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The ECHO Study• Links Perth’s prehospital care, emergency care,

hospital morbidity and death records 2000-2005

• ECHO Investigators include: Roger Swift, Neil Banham, Simon Wood, Judith Finn, Gary Geelhoed, Adrian Goudie, Tom Hitchcock, Jack Hodge, Andrew Jan, Michelle Johnston, Debra O’Brien, Alan O’Connor, Paul Mark, David Mountain, Yusuf Nagree, Greg Sweetman, Garry Wilkes

• The ECHO investigators acknowledge: The Western Australian Data Linking Unit

• ECHO is funded by: Australian Health Ministers’ Advisory Council

• I also acknowledge: The Commonwealth Fund

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The problem of overcrowding• High hospital occupancy is associated

with emergency department boarding and emergency department dysfunction

• There is indirect evidence of harm from overcrowding– Longer hospital lengths of stay– More representations after not being

seen in the emergency department– Poorer hospital infection control/disaster

responsiveness

• Hypothesis– Overcrowding may be associated with

increased admission mortality

‘…AND THAT WARD SHOULD HOLD A COUPLE DOZEN MORE COMFORTABLY..’

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Aim

Examine the relationship between hospital occupancy, emergency department Boarder occupancy and

emergency admission mortality

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Setting• Three 400 – 550 bed hospitals

• 130 000 ED attendances

• 50 000 annual emergency admissions

• Over 80% of ambulance attendances

• Nearly 70% of ambulance diversion episodes

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Methods• Analysis of probabilistically linked data for first

admission of adults between July 2000 and June 2003– Emergency department information system records– Hospital admission records– State death records

• Linkage conducted by the Western Australian Department of Health Data linking unit as part of the Western Australian Linked Data System*

* Holman CDJ, Bass AJ, Rouse IL, Hobbs MST. Population-based linkage of health records in Western Australia: Development of a health services research linked database. Aust N Z J Public Health 1999;23:453-59.

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BEDS

Empty

Occupied

Boarder

Outlier

Med Surg

Emergency Room

Med Surg

Emergency Room

Uncrowded CrowdedHospital Hospital

Low ward occupancy• Good flow• Empty beds• Few ED boarders• No medical outliers

High ward occupancy• Poor flow• No empty beds• Many ED boarders• Medical outliers

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Analysis• Multivariate analysis:

Survival and Death hazard by day 2, 7 and 30 for adult admissions associated with:

– Hospital occupancy at 2359 hours on day of ED attendance– Number of ED boarders present during hour of attendance– Definitions:

• Boarder = admission with a total ED duration of stay greater than 8 hours• Admission = Hospital length of stay greater than 12 hours or died in ED

• Controlling for:– Age– Principal Diagnosis (medical illness vs traumatic injury)– Transport to hospital (ambulance vs nonambulance)– Referral source (physician vs nonphysician)– Triage urgency (Australasian 5 level urgency scale)

• Also tested associations with:Admission length of stay, ED duration of stay, ED waiting time to be seen by Physician, ED attendance rate per hour, Ambulance arrival rate per hour, Hospital admission rate per hour, Hospital bed turnover per day, Day of week, Time of day, Month, Holiday periods, Winter-season, Hospital attended

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Analysis• Test for admission selection confounding

– Risk of admission vs overcrowding, multivariate analysis

• Deaths associated with overcrowding– Characteristics of patients who died who experienced

overcrowded conditions versus those who didn’t experience overcrowding, multivariate analysis

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Characteristics of study sample

* For all between group tests

Hospital occupancy group: <90% 90-99% 100%+ P

Sample characteristic % or mean % or mean % or mean

Age (% 50 years or older) 62 64 66 <0.001

Diagnosis (% Injury) 26 24 21 <0.001

Day (% Mon-Fri) 64 75 80 <0.001

Winter attendance (% Jun-Sep) 11 40 73 <0.001

Gender (% Female) 46 47 47 0.05

Shift (% 0800-1559 hours) 44 45 46 0.12

Referral source (% physician referred) 34 34 34 0.36

Transport to emergency (% ambulance) 51 51 51 0.09

Triage urgency (% resuscitation cases) 3.8 3.6 3.7 0.27

Length of stay (mean) 6.6 6.7 6.7 >0.2*

Length of stay (mean), weighted for deaths 6.8 6.9 7.0 >0.1*

Total (% of 62,495 admissions) 26 64 10

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Hospital occupancy vs 7-day mortality

< 90 90 - 99.9 100 +

Occupancy (%)

0.020

0.022

0.025

0.027

0.030

0.032

0.03595

% C

I Mo

rta

lity

P = 0.002

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Overcrowding vs 7-day survival

0 1 2 3 4 5 6 7

Days survived

0.985

0.990

0.995

1.000

Cu

mu

lati

ve S

urv

ival

< 90%

90-99%

100% +

0 1 2 3 4 5 6 7

Days survived

0.985

0.987

0.990

0.992

0.995

0.997

1.000

Cu

mu

lati

ve S

urv

ival

<10%

10-19%

20%+

P = 0.001

Hospital occupancy ED Boarder occupancy

P = 0.001

Both hospital occupancy and ED boarder occupancy are associated with increased 7-day mortality

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Overcrowding Hazard Scale

Hospital occupancy

(%)

<90 90-99 100+

ED Boarder occupancy

(%)

OZS

Weight

1 2 3

<10 1 1 2 3

10-19 2 2 4 6

20+ 3 3 6 9

(ED Boarder occupancy at attendance) x (Hospital occupancy at midnight)

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Overcrowding Hazard Scale vs 7 Day survival

0 1 2 3 4 5 6 7

Days survived

0.980

0.985

0.990

0.995

1.000C

um

ula

tive

Su

rviv

alOvercrowding Hazard

Scale

1

2

3

4

6

9

P < 0.001

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Overcrowding Hazard Scale 7-day mortality hazard

1.1

1.41.4

1.7

1.3

1.0

R2 = 0.95

0.5

1

1.5

2

2.5

1 2 3 4 5 6 7 8 9 10

Overcrowding Hazard Scale

Rela

tive s

even

day m

ort

ality

hazard A score greater than 2

defines hazardous “Overcrowded Conditions”

P < 0.001

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Deaths attributable to overcrowding

Censoring date Hazard ratio

Attributable deaths / 1000 Emergency hospital

admissions P

HR 95%CI Deaths 95%CI

Day 2 1.3 1.1 - 1.6 1.0 0.4 - 1.4 0.001

Day 7 1.3 1.2 - 1.5 1.9 0.7 - 2.5 <0.001

Day 30 1.2 1.1 - 1.3 2.3* 1.2 - 3.2 <0.001

*120 deaths per year by Day 30 for Perth (pop. 1.8 million)

Suggests over 1000 deaths per annum for Australia (pop. 20 million)

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Tests for confounding: Winter

1.3

1.0

1.3

0.5

1

1.5

2

October through May Entire Year

Re

lati

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se

ve

n d

ay

mo

rta

lity

ha

zard

Removing winter months’ data does not affect the relationship between overcrowding and mortality

Overcrowding hazard scale > 2

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Tests for admission selection confounding

Measurement of occupancy

exposure

0:00

Time of day

23:5912:00

Admission decision period

Relative risk of admission associatedwith hospital occupancy: 1.0 (95%CI 1.0-1.1, P = 0.001)

Relative risk of admission associatedwith overcrowding: 1.0 (95%CI 1.0-1.1, P = 0.06)

- No evidence of adverse selection confounding

- There is a very weak relationship between the perception of bed availability and measured hospital occupancy

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Deaths associated withovercrowded conditions

• Undifferentiated with respect to:– Age– Diagnosis– Urgency– Mode of transport– Referral source – Hospital length of stay

• Longer ED durations of stay– (RR per hour of ED stay, 1.1, 95%CI 1.1-1.1, P < 0.001)

• Slightly longer ED physician waiting time– (RR per hour of ED wait, 1.2, 95%CI 1.1-1.3, P = 0.01)

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Conclusions• Overcrowding is associated with increased mortality

– Can not be explained by adverse admission selection

– This suggests overcrowding is a patient safety concern, not just a workflow issue

• The Overcrowding Hazard Scale seems a useful hazard assessment tool

– Easy to measure in real time:• Count ED Boarders and the number of empty beds

– Provides an objective definition for overcrowded conditions:• OZS greater than 2 is associated with increased mortality

• Deaths during overcrowded conditions are associated with longer ED physician waits and longer durations of ED stay

– Possibly a proxy for delays for time critical care

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Limitations and suggested further research

• No definitive assessment of mediators of the relationship between overcrowding and harm

• Further research is needed into the mediators of this relationship:

– Adverse events, medical errors vs overcrowding

– ‘Off usual service’ ward placement vs adverse events and medical errors

– Assessment of delays in time critical care vs overcrowding

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Implications for policy• Hospitals operating between at 90-100% occupancy need to have

near perfect management of patient flow in and out of beds in order to operate safely

• Operating above 100% appears to be unsafe

• Prudence suggests hospitals should aim to operate at no higher than 90% occupancy

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Understanding admission decision making

• Admission decisions may be affected by the perception of poor bed availability

• In reality however, there is a very weak relationship between bed availability and this perception, as illustrated by the Gap of Neglect (next slide)

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The gap of neglectRPH, SCGH, FH Combined overnight accommodation demand and accommodationPrimary axis: Unplanned accommodation trends (solid lines)Secondary axis: Planned accommodation trends and Unaccommodated Unplanned trends (dotted lines)

850

900

950

1000

1050

1100

1150

1200

1250

Month

0

50

100

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200

250

300

350

400

• Over 300 bed variation in demand and supply at our largest 3 hospitals…….

• Yet near perfect maintenance of a gap of neglect of between 25 – 50 overnight beds

• It always feels crowded!

Bed supply and demand 2004