individual task variability: linking process improvement to patient and hospital outcomes
DESCRIPTION
Individual Task Variability: Linking Process Improvement to Patient and Hospital Outcomes. Susan Meyer Goldstein & Rachna Shah Cincinnati Innovations in Healthcare Delivery 2006. Scenario…. Treatment of ST-elevation mycardial infarction (STEMI) in Greater Minnesota. Current Evidence. - PowerPoint PPT PresentationTRANSCRIPT
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Individual Task Variability: Linking Process
Improvement to Patient and Hospital Outcomes
Susan Meyer Goldstein & Rachna Shah
Cincinnati Innovations in Healthcare Delivery 2006
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Scenario…Treatment of ST-elevation mycardial infarction
(STEMI) in Greater Minnesota
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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
Current Evidence
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Pilot Study
Source: Henry et al., American Heart Journal Vol 150, Issue 3, 2005
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Community hospital
MHI
Standardized Protocol
Every patient, every time (24/7 coverage); no exclusions.
95 minutes
Source: Henry et al., American Heart Journal Vol 150, Issue 3, 2005
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no
yes
no
yes
yes
no
Patient arrives at rural hospital
with STEMI symptoms
Remove patient shirt; put on
gown
Perform ECG within 5 min. of
arrival
Activate team (MD, nurse, technician)
Is STEMI diagnos
ed?
Perform angiogram (image the blockage)
A cardiologist explains
procedure to patient; another
cardiologist preps patient
Move patient onto imaging
table
Security holds elevator and
escorts patient to cath lab
Does angiogr
am confirm blockag
e?
Perform PCI
Contact MHI
Start IV and monitors, draw
blood for testing (all in kit)
End of
process Is patient anxious
?
Give 2 more doses of
metoprolol during transport
Load patient into ground or air ambulance
Give sedation
Attach defibrillation
pads
Start second IV
Perform chest x-ray
Give aspirin, clopidogrel,
nitroglycerin, heparin,
metoprolol (all in kit)
Contact transport
Complete procedure and transfer patient
to recovery room
Arrive at MHI
Locate pre-stocked kit
MHI’s Standardized
Treatment Protocol for
STEMI
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Outcomes – Patient Mortality
< 60 60-90 90-120 120-180 180 +
Minutes, door to balloon
1.0
2.0
3.0
4.0
5.0
6.0
7.0
8.0
Mo
rtal
ity
Rat
e (%
)
MHI rate
NRMI rate
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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
MHI
Community Hospital j
Community Hospital j
Community Hospital j
etc.
Patient i
Patient i
Patient i
etc.
i = 1, … 651 j = 1, … 27
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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
1: arrive → EKG
2: EKG → call
3: call → arrive
4: arrive→depart
5: depart →MHI
6: MHI →Lab
7: Lab → begin
8: begin → flow
Interval CHosp Transpt MHI
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Independent Variables: Hospital-Level
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: Patient-Level
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
Median Hospital
Interval 1j
Patient Raw Minutes
Interval 1ij
= -
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Control Factors – Patient Characteristics
Systolic blood pressure Age Heart rate Killip class 4 Killip class 3 Killip class 2
Hypercholesterolemia Diabetes Hypertension Prior congestive heart
failure Anterior MI
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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
Baseline
Model
Full
Model
Sample size 619 619
F-change 43.87 (p<.001)
3.10(p<.001)
R2 0.18 0.24
Adjusted R2 0.17 0.21
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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 -0.090**
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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
8
7
6
5
4
3
2
1
Interval CHosp Transpt MHI
?Patient
‘Do how’
Hospital ‘Do what’ Drug score
EKG → call transport
Transport call → arrive
CHosp → transport handoff
Transport → MHI handoff
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Logistic Regression: Mortality Results
Baseline
Model
Full
Model
Sample size 651 651
Chi-square - change 63.32(p < .001)
31.37(p < .01)
Nagelkerke R2 0.09 0.14
Cox & Snell R2 0.37 0.55
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Hospital-Level Effects: MortalityHospital 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: MortalityPatient 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
8
7
6
5
4
3
2
1
Interval CHosp Transpt MHI
?
Patient
‘Do how’
Hospital
‘Do how’
?
Transport → MHI handoff
EKG → call transport
Arrive CHosp → EKG
Depart CHosp → arrive MHI
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ConclusionsIs the impact of variability in task activity on process
performance (cost, quality) observable?
8
7
6
5
4
3
2
1
8
7
6
5
4
3
2
1
Hospital ‘Do what’ Drug score
Length of Stay Mortality
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Conclusions
What is the relative importance of hospital-level versus patient-level task activity in predicting performance?
8
7
6
5
4
3
2
1
8
7
6
5
4
3
2
1
Hospital ‘Do what’ Drug score
Length of Stay Mortality
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Conclusions
What are the impact of process handoffs?
8
7
6
5
4
3
2
1
8
7
6
5
4
3
2
1
Hospital ‘Do what’ Drug score
Length of Stay Mortality
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Conclusions
In practice…
8
7
6
5
4
3
2
1
8
7
6
5
4
3
2
1
Hospital ‘Do what’ Drug score
Length of Stay Mortality