Clinical Information Technologies and Inpatient Outcomes:

A Multiple Hospital Study

Ruben Amarasingham, MD, MBAAssistant Professor of Medicine

University of Texas Southwestern Medical School

Medical Director, Medicine ServicesParkland Health & Hospital System

Clinical Information Technologies (CIT) in the Hospital

Amarasingham R et al, Clinical information technology capabilities in four U.S. hospitals: testing a new structural performance measure. Medical Care. 2006;44:216-24.

The Promise of Clinical Information Technologies (CIT)

• Reductions in waste• Gains in communication• Improved decision making• Provider accountability• Predictive modeling and surveillance

Despite this, problems exist…..

•Adoption remains low

•CIT associated with errors

•Proliferation of pre-/post- studies

•Crudeness of measurement

Despite this, problems exist…..

• Instrument designed to quantitatively assess a hospital’s automation in 4 areas.

• Socio-Technical View of the Workplace

• Physician-based survey

• Demonstrated reliability and validity across hospitals with varying levels of automation

Clinical Information Technology Assessment Tool (CITAT)

Amarasingham R, Diener-West M, Weiner M, Lehmann H, Herbers JE, Powe NR. Clinical information technology capabilities in four U.S. hospitals: testing a new structural performance measure. Med Care. 2006;44:216-24.

Domains assessed in the CITAT

Amarasingham R et al Clinical information technology capabilities in four U.S. hospitals: testing a new structural performance measure. Medical Care. 2006;44:216-24.

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Test Results

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Notes & Records

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Order Entry

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Processes

Amarasingham R et al Clinical information technology capabilities in four U.S. hospitals: testing a new structural performance measure. Medical Care. 2006;44:216-24.

CITAT Order Entry Scores at 4 Hospitals

Use of Clinical Information Technology Assessment Tool (CITAT)

• Re-tested and revised for intensive care unit settings

• Demonstrated reliability and validity

• Low sample size required: ~ 5-6 physicians per hospital

Amarasingham R, Pronovost PJ, Diener-West M, Goeschel C, Dorman T, Thiemann DR, Powe NR. Measuring clinical information technology in the ICU setting: application in a quality improvement collaborative. J Am Med Inform Assoc. 2007;14:288-94.

Research Question

What is the relationship between CIT automation and outcomes (mortality, complications, costs and LOS) for the following conditions?

• Myocardial infarction• Congestive heart failure• Coronary artery bypass grafting

(CABG)• Pneumonia• All causes

MethodsDesign: Cross-sectional regional study

Population: •Acute care urban hospitals and physicians in 10 largest

Texas metropolitan statistical areas

Data collection: • Automation of clinical information (test results, notes &

records, order entry, decision support) by CITAT survey of physicians delivering inpatient care

• All-cause and condition-specific mortality, complications, cost, length of stay (LOS) from administrative data

• Ownership status, bed size, total margin, teaching status, safety net status from American Hospital Association

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Hospital Sampling• 72 urban hospitals in 10

largest Texas MSAs with discharge data

• Excluded pediatric, long-term care, in transition hospitals

• Surveyed MDs living in 10 Texas MSAs• At least 5 physicians surveys required

Statistical Analysis

• Multivariable analysis: relationship between CIT scores and outcomes • Mortality and complications: logistic regression • Costs and LOS: linear regression after log transform• Risk adjustment: hospital characteristics, Risk-Adjusted

Mortality Index (RAMI), Risk-Adjusted Complication Index (RACI)

•Robust variance-covariance matrix estimates to account for clustering

Results: Characteristics of 41 Study Hospitals (57% response rate)

Ownership no. % Church/not-for-profit 24 60 Government/authority 3 8 Private 13 32Teaching status Non-teaching hospital 35 85 Teaching hospital 6 15Safety net status Non-safety net hospital 37 90 Safety net hospital 4 10

Characteristics ofStudy Hospitals (n=41)

no. (%)IT operating expense

<$1 million 10 (25%) ≥$1 million 30 (75%)Bedsize, mean SD 402.4 291.8Operating margin, mean SD 0.02 0.13Total margin, mean SD 0.05 0.10

CITAT Domain Scores

Domain mean SD

Notes & records 28.5 9.9

Test results 50.1 19.7

Order entry 3.7 14.9

Decision support 2.6 4.8

All Myocardial in-farction

Heart failure Coronary artery bypass graft

Pneumonia 0

0.2

0.4

0.6

0.8

1

1.2

Ad

just

ed

Od

ds

Ra

tio * *

*

*

Odds Ratio for Inpatient Death Associated with 10 point Increase in CIT Score

Decision Support

Order Entry

Test Results

Notes & Records

* p<.05

All Myocardial in-farction

Heart failure Coronary artery bypass graft

Pneumonia 0

0.2

0.4

0.6

0.8

1

1.2

1.4

1.6

Ad

just

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Od

ds

Ra

tio

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*

*

Decision Support

Order Entry

Test Results

Notes & Records

Odds Ratio for Complications Associated with 10 point Increase in CIT Score

* p<.05

Difference in Average Hospital Costs Associated with 10-Point Increase in CIT Score

-1600

-1400

-1200

-1000

-800

-600

-400

-200

0

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400

All

Myocardial Infarction

Heart failure

Coronary artery bypass graft

Pneumonia

Ad

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Ho

spita

l Co

sts

($)

**

**

*

* *

*

*

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Decision Support

Order Entry

Test Results

Notes & Records

* p<.05

Difference in Average Hospital LOS Associated with 10-Point Increase in CIT Score

-0.3

-0.2

-0.1

-1.1

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0.0999999999999999

0.2 0.3

Pneumonia

CABG

Heart failure

MI

All

Difference in Days

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*

*

Decision Support

Order Entry

Test Results

Notes & Records

**

* p<.05

Limitations

• Single state study• Possible residual unmeasured

organizational confounders• Extrapolation only for range of scores

Strengths

• One of largest hospital studies of CIT• Clinical Information Assessment Tool (CITAT)

independent variable• Socio-technical view of the workplace• Based on physicians interactions with CIT• Rewards usability, preference, and maturation

• Consistency of results• Adoption patterns mirrors previous studies

Conclusions

•Hospitals that automate notes and records, order entry, and clinical decision support in clinically friendly ways may experience fewer complications, less lives lost, and lower costs.

•Further studies needed, but if confirmed, US hospitals should accelerate their acquisition of these technologies

Acknowledgements

• Study Team

Neil R. Powe, MD, MPH, MBA

Laura Plantinga, ScM

Marie Diener-West, PhD

Darrell Gaskin, PhD

Aaron Cunningham• Sponsor: Commonwealth Fund, NY

•Stakeholder Involvement

TMF Quality Institute

Acknowledgements

Texas Department of Health