Clinical Information Technologies and Inpatient Outcomes: A Multiple Hospital Study Ruben Amarasingham, MD, MBA Assistant Professor of Medicine University of Texas Southwestern Medical School Medical Director, Medicine Services Parkland 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:

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:

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:

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:

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

Methods Design: 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

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) Ownershipno.% Church/not-for-profit Government/authority 3 8 Private Teaching status Non-teaching hospital 3585 Teaching hospital 6 15 Safety net status Non-safety net hospital Safety net hospital 4 10

Characteristics of Study Hospitals (n=41) no. (%) IT operating expense <$1 million 10 (25%) ≥$1 million 30 (75%) Bedsize, mean  SD  Operating margin, mean  SD 0.02  0.13 Total 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

Odds Ratio for Inpatient Death Associated with 10 point Increase in CIT Score Decision Support Order Entry Test Results Notes & Records * p<.05

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 Decision Support Order Entry Test Results Notes & Records * p<.05

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