6/29/2015 Individual Task Variability: Linking Process Improvement to Patient and Hospital Outcomes Susan Meyer Goldstein & Rachna Shah Cincinnati Innovations.

04/18/2304/18/23

Individual Task Variability: Linking Process

Improvement to Patient and Hospital Outcomes

Susan Meyer Goldstein & Rachna Shah

Cincinnati Innovations in Healthcare Delivery 2006

04/18/23

Scenario…Treatment of ST-elevation mycardial infarction

(STEMI) in Greater Minnesota

04/18/23

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

04/18/23

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



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 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 7 -0.030


Patient drug score -0.048

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Length of Stay Results

8

7

6

5

4

3

2

1


?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 drug score 1.718

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Patient-Level Effects: MortalityPatient effects:





Patient Interval 5 -0.110*




Patient drug score -3.753

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Mortality Results

8

7

6

5

4

3

2

1


?

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


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



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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



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Conclusions

In practice…

8

7

6

5

4

3

2

1

8

7

6

5

4

3

2

1



6/29/2015 Individual Task Variability: Linking Process Improvement to Patient and Hospital Outcomes Susan Meyer Goldstein & Rachna Shah Cincinnati Innovations.

Documents

stemi slide

escorts patient

transfer patient

patient shirt

patientlevel task activity

transport load patient

cardiologist preps patient

greater minnesota slide