Assessing Inpatient Care Using Hospital Quality Alliance Patient Level Quality Data

1. Assessing Inpatient Care Using Hospital Quality Alliance Patient Level Quality Data Joel S. Weissman, PhD (P.I. ); MGH Institute for Health Policy and Harvard Medical School Romana Hasnain-Wynia, PhD; Northwestern University, Feinberg School of MedicineRaymond Kang, M.A.; American Hospital Association, Health Research and Educational Trust (HRET) Mary Beth Landrum, Ph.D.; Harvard Medical School, Department of Health Care Policy Christine Vogeli, PhD; MGH Institute for Health Policy What can we learn about inpatient care quality from patient-level data The authors acknowledge the assistance of the IFQHC and the Centers for Medicare and Medicaid Services in providing data which made this research possible. The conclusions prescribed are solely those of the author(s) and do not represent those of IFQHC or CMS. Jointly funded by The Commonwealth Fund and the Robert Wood Johnson Foundation’s Changes in Health Care Financing and Organization (HCFO) Initiative

2. National Hospital Quality Alliance • Alliance between the Joint Commission and CMS to collect and report hospital-level quality. • CMS currently collects data on 31 measures (AMI, HF, PN, and surgical care). • All payer data. • Study based on CY 2005 data containing over 2.3 million discharges from 4,450 non-federal hospitals. • Discharge (Patient level) data includes information on: • Attainment of each process / measure • Patient characteristics (race / ethnicity, age, gender) • Routine discharge abstract data • Hospital characteristics merged from the AHA Annual Survey.

3. AMI measure set Aspirin at arrival Beta blocker at arrival Thrombolysis w/in 30 minutes of arrival PCI w/in 120 minutes ACE/ARB for LVSD Smoking cessation counseling Aspirin at discharge Beta blocker at discharge PN Measure set Initial antibiotic selection Initial antibiotic w/in 4 hours Oxygenation assessment Pneumococcal vaccination Blood culture before antibiotic Influenza vaccination Smoking cessation counseling HQA Condition Specific Quality Measures HF Measure set • LVF assessment • ACE / ARB for LVSD • Smoking cessation counseling • Discharge instructions http://www.cms.hhs.gov/HospitalQualityInits/downloads/HospitalHQA2004_2007200512.pdf

4. Composite Measures • Opportunity Weighted • Sum of numerators / sum of denominators across all measures in the set; with each applicable measure per patient representing an opportunity. • All-or-None • Proportion of patients receiving all applicable processes • Perceived Strengths: • Sensitive to inter-provider variability • Reflection of patients’ interests / desires • System or team approach to improving care. • Patient Percent • The proportion of applicable care processes received by patients. • Similar to opportunity weighted.

5. Example of Opportunity-Weighted Composite Scoring for HF

6. Example of All-or-None Composite Scoring for HF

7. Example of Patient Percent Composite Scoring for HF

8. Why use patient-level composites • To examine differences in care quality by patient characteristics (race/ethnicity, age, gender, primary payer, admission source). • To allow uncommon, but important processes to carry more weight. • To incent excellence.

9. Quality of care provided to individual patients in U.S. hospitals—Results from an analysis of national Hospital Quality Alliance data Christine Vogeli Raymond Kang Mary Beth Landrum Romana Hasnain-Wynia Joel S. Weissman

10. Background • Prior analyses of hospital level HQA data identified hospital characteristics associated with better quality care. • Quality was assessed using composites that approximate the average proportion of processes patients receive. • IOM recommendation => All-or-none composite that determines whether all critical processes provided.

11. Methods • Patient-level composites only: All or none and patient percent • Multivariable models (linear and logistic) to examine independent associations. Adjusted standard errors for clustering within hospitals using GEE. • All-or-none composites stratified by the number of applicable measures. • Sequentially excluding specific measures to asses the contribution of individual measures to the all-or-none composite.

12. Number of applicable measures per patient • The mean number of applicable measures per patient is small • The plurality of HF patients have only 2 applicable HF measures • 40% of AMI patients have only 2 applicable measures

13. Patient-level Composites to Assess Inpatient Care Quality • Room to improve on all-or-none • Less than half of PN inpatients receive all care processes. • Just over half (57%) receive all HF care processes. • 83% receive all AMI care processes

14. All-or-None Performance: Patient Characteristics • Transferred patients more likely to receive all processes • Young (18-34) less likely to receive all HF but more likely to receive all PN • Minorities less likely to receive all PN processes

15. All-or-none performance: Hospital Characteristics • Patients receiving care in non-profit hospitals more likely to receive all processes. • HF patients cared for in 100+ bed hospitals more likely to receive all HF. • PN patients cared for in major teaching hospitals less likely to receive all PN processes

16. All-or-None Performance: Impact of specific measures • AMI: • No specific measure had a large impact. • HF: • Removal of discharge instruction measure for the HF set had the largest impact (all-or-none increased by 27%). • LVF assessment had almost no impact . • PN: • Pneumonia vaccination and antibiotic had the largest impact (all-or-none increased by 9%). • Oxygenation assessment and smoking cessation counseling had almost no impact.

17. Limitations • All or none makes implicit assumption that patients should receive all applicable processes • Changes / updates in measure specifications since 2005: • PN antibiotic timing changed from within 4 to within 6 hours • PCI tightened to w/in 90 minutes

18. Conclusions • Room to improve all-or-none performance • Sensitive to the number and type of applicable measures. • Variation by patient and hospital characteristics. • Well-accepted professional standards.