Clinical Information Technologies and Inpatient Outcomes: A Multiple Hospital Study

1. 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

2. 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.

3. The Promise of Clinical Information Technologies (CIT) • Reductions in waste • Gains in communication • Improved decision making • Provider accountability • Predictive modeling and surveillance

4. Despite this, problems exist….. • Adoption remains low • CIT associated with errors • Proliferation of pre- /post- studies • Crudeness of measurement

5. Despite this, problems exist…..

6. Clinical Information Technology Assessment Tool (CITAT) • 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 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.

7. 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.

8. CITAT Order Entry Scores at 4 Hospitals 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.

9. 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.

10. 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

11. 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 CITATsurvey 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

12. 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

13. 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

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

15. Characteristics ofStudy Hospitals (n=41)

16. CITAT Domain Scores

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

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

19. Difference in Average Hospital CostsAssociated with 10-Point Increase in CIT Score Notes & Records Test Results Order Entry Decision Support * p<.05

20. Difference in Average Hospital LOS Associated with 10-Point Increase in CIT Score Notes & Records Test Results Order Entry Decision Support * * * p<.05

21. Limitations • Single state study • Possible residual unmeasured organizational confounders • Extrapolation only for range of scores

22. 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

23. 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

24. 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

25. Acknowledgements • Stakeholder Involvement Texas Department of Health TMF Quality Institute