Individual Task Variability:
Linking Process Improvement
to Patient and Hospital
Outcomes
Susan Meyer Goldstein
& Rachna Shah
Cincinnati Innovations in
Healthcare Delivery 2006
4/11/2020
Scenario…
Treatment of ST-elevation mycardial infarction
(STEMI) in Greater Minnesota
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Current Evidence
Medical Science
Balloon angioplasty (PCI) is preferred treatment for
heart attack (based on numerous global studies)
Practice
Less than half receive primary
balloon treatment; often delayed
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Pilot Study
Source: Henry et al.,
American Heart Journal
Vol 150, Issue 3, 2005
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Standardized Protocol
95 minutes
Community hospital
MHI
Every patient, every time (24/7 coverage); no exclusions.
Source: Henry et al.,
American Heart Journal
Vol 150, Issue 3, 2005
4/11/2020
Patient arrives
at rural hospital
with STEMI
symptoms
Arrive at MHI
Security holds
elevator and
escorts patient
to cath lab
Give 2 more
doses of
metoprolol
during transport
Remove patient
shirt; put on
gown
Load patient
into ground or
air ambulance
Perform ECG
within 5 min. of
arrival
A cardiologist
explains
procedure to
patient; another
cardiologist
preps patient
Give sedation
yes
Is
STEMI
diagnos
ed? yes
Activate team
(MD, nurse,
technician)
no
End
of
process
Is
patient
anxious
?
Attach
defibrillation
pads
Contact
transport
Start second IV
no
Perform
angiogram
(image the
blockage)
Does
angiogr
am
confirm
blockag
e? yes
Contact MHI
Perform chest
x-ray
Perform PCI
Locate prestocked kit
Start IV and
monitors, draw
blood for testing
(all in kit)
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Give aspirin,
clopidogrel,
nitroglycerin,
heparin,
metoprolol
(all in kit)
MHI’s
Standardized
Treatment
Protocol for
STEMI
Move patient
onto imaging
table
Complete
procedure and
transfer patient
to recovery
room
no
Outcomes – Patient Mortality
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Research Problem
Practitioners’ questions:
·Can we further improve an already well-performing system?
· Are the community hospitals doing everything they can?
Researchers’ questions:
· Are
there systematic factors within process-level activities
that can be improved?
What is the impact of hospital-level task activity on the
outcomes of interest?
Patient-level task activity?
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Research Propositions
 Is the impact of variability in task activity on process
performance (cost, quality) observable?
 What is the relative importance of hospital-level versus
patient-level task activity in predicting performance?
 What are the impact of process handoffs?
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Literature Base
 Service process variability
 Frei et al. (1999), Management Science
 Tsikriktsis & Heineke (2004), Decision Sciences
 Field et al. (2006), Decision Sciences
 Process improvement
 Zantek et al. (2002), Management Science
 Rust & Metters (1996), EJOR
 Process handoffs
 Hammer (re-engineering)
 Shingo (set-ups)
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Sample Characteristics





27 Minnesota community hospitals
Average 81 miles from MHI (range 17-149 miles)
Data collection period: March 2003 – Feb. 2006
Total 720 patients
Exclusions: 54 false positives, 4 extreme time outliers
(2 for weather delay; 1 for diagnostic dilemma; 1 for
LOS), 11 intentional protocol deviations/missing partial
data
 Final data set for analysis: 651 patients
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Outcomes of Interest
 Patient hospital length of stay – proxy for cost
 Sample mean = 3.8 days (range 0-34)
 Mortality cases excluded due to truncation
 Skewed distribution; 90% of patients hospitalized
6 days or fewer
 Logarithmic function used in analysis
 Patient in-hospital mortality – proxy for quality
 Sample mean = 3.2%
 21 deaths in sample
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Data Structure
Patient i
Patient i
Community
Hospital j
Patient i
etc.
Community
Hospital j
Community
Hospital j
etc.
i = 1, … 651
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j = 1, … 27
MHI
Process Description
0. Pt arrives at CHosp
1. EKG started
2. Transport called
3. Transport arrives
4. Pt departs CHosp
5. Pt arrives at MHI
6. Pt arrives at Cath Lab
7. Procedure begins
8. Normal blood flow
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Interval
1: arrive → EKG
2: EKG → call
3: call → arrive
4: arrive→depart
5: depart →MHI
6: MHI →Lab
7: Lab → begin
8: begin → flow
CHosp Transpt
MHI
Independent Variables: HospitalLevel
 From ‘Know what’ to ‘Do what’
 Proportion of 4 drugs given
 From ‘Know how’ to ‘Do how’
 Hospital median time intervals
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Independent Variables: PatientLevel
 From ‘Know what’ to ‘Do what’
 Proportion of 4 drugs given
 From ‘Know how’ to ‘Do how’
 Difference from hospital median time intervals
• Reduces multi-collinearity
• Keeps VIFs below 2.0
Patient
Interval 1ij
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=
Median
Hospital
Interval 1j
-
Patient Raw
Minutes
Interval 1ij
Control Factors – Patient
Characteristics






Systolic blood pressure
Age
Heart rate
Killip class 4
Killip class 3
Killip class 2
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



Hypercholesterolemia
Diabetes
Hypertension
Prior congestive heart
failure
 Anterior MI
Regression Model: Length of
Stay
Baseline with control factors:
ln(length of stay)ij = β0 + β1-3[Patient risk factorsij] + εij
Full model:
ln(length of stay)ij = β0 + β1-3[Patient risk factorsij]
+ β4-8[Hosp median intervalj] + β9Hosp drug scorej
+ β10-17[Pt intervalij] + β18Pt drug scoreij + εij
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Length of Stay Results
Sample size
Full
Model
Model
619
619
43.87
3.10
(p<.001)
(p<.001)
R2
0.18
0.24
Adjusted R2
0.17
0.21
F-change
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Baseline
Hospital-Level Effects: LOS
Hospital effects:
Hosp median Interval 1
0.035
Hosp median Interval 2
-0.012
Hosp median Interval 3
-0.048
Hosp median Interval 4
-0.031
Hosp median Interval 5
-0.051
Hosp drug score
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-0.090**
Patient-Level Effects: LOS
Patient effects:
Patient Interval 1
0.041
Patient Interval 2
-0.101***
Patient Interval 3
0.128**
Patient Interval 4
0.142***
Patient Interval 5
0.014
Patient Interval 6
0.074**
Patient Interval 7
-0.030
Patient Interval 8
0.031
Patient drug score
-0.048
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Length of Stay Results
Interval
CHosp Transpt
MHI
1
Patient
‘Do how’
2
?
EKG → call transport
3
Transport call → arrive
4
CHosp → transport handoff
5
6
7
8
Hospital ‘Do what’ Drug score
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Transport → MHI handoff
Logistic Regression: Mortality
Results
Sample size
Full
Model
Model
651
651
63.32
31.37
(p < .001)
(p < .01)
Nagelkerke R2
0.09
0.14
Cox & Snell R2
0.37
0.55
Chi-square - change
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Baseline
Hospital-Level Effects:
Mortality
Hospital effects:
Hosp median Interval 1
0.554*
Hosp median Interval 2
-0.470**
Hosp median Interval 3
-0.122
Hosp median Interval 4
-1.013
Hosp median Interval 5
-0.064
Hosp drug score
1.718
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Patient-Level Effects: Mortality
Patient effects:
Patient Interval 1
0.004
Patient Interval 2
-0.005
Patient Interval 3
0.001
Patient Interval 4
0.011
Patient Interval 5
-0.110*
Patient Interval 6
0.068**
Patient Interval 7
0.126
Patient Interval 8
-0.014
Patient drug score
-3.753
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Mortality Results
Interval
Hospital
1
‘Do how’
2
CHosp Transpt
MHI
Arrive CHosp → EKG
EKG → call transport
?
3
4
Patient
5
‘Do how’
6
7
8
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?
Depart CHosp → arrive MHI
Transport → MHI handoff
Conclusions
Is the impact of variability in task activity on process
performance (cost, quality) observable?
Length of Stay
Mortality
Hospital ‘Do what’ Drug score
1
2
3
4
5
6
7
8
4/11/2020
1
2
3
4
5
6
7
8
Conclusions
What is the relative importance of hospital-level versus
patient-level task activity in predicting performance?
Length of Stay
Mortality
Hospital ‘Do what’ Drug score
1
2
3
4
5
6
7
8
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1
2
3
4
5
6
7
8
Conclusions
What are the impact of process handoffs?
Length of Stay
Mortality
Hospital ‘Do what’ Drug score
1
2
3
4
5
6
7
8
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1
2
3
4
5
6
7
8
Conclusions
In practice…
Length of Stay
Mortality
Hospital ‘Do what’ Drug score
1
2
3
4
5
6
7
8
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1
2
3
4
5
6
7
8