Hospital Readmissions Research: in search of potentially avoidable costs

1. Hospital Readmissions Research: in search of potentially avoidable costs Bernard Friedman, PhD Center for Delivery, Organization, and Markets AHRQ Conference, 2008

2. Agenda • Brief overview of some AHRQ internal research on readmissions over the past several years. • Then a more detailed presentation on recent work in progress. • If voice doesn’t hold out, there is a poster on this project in the Cafe. • Finally, a few words on how HCUP team is trying to make new tools available to outside analysts to study readmissions.

3. Published Studies • 1.) Joanna Jiang was the lead author at AHRQ on several published studies of diabetes discharges. • One finding was that half of the discharges or hospital costs in a year are for people with multiple discharges for diabetes and its complications. • 2.) I examined (with Joy Basu) all readmissions within 6 months for people with 16 Potentially Preventable initial admissions. • Large variety of principal diagnoses for the RE-admissions • Just the Potentially Preventable RE-admissions within 6 months had a projected national cost of about $1.4 Billion in today’s $. This covered 4 states with 15% of the U.S. population.

4. (cont’d) • 3.) A recently accepted paper (with Joanna Jiang and Anne Elixhauser), “Costly Hospital Readmissions and Complex Chronic Illness” • shows importance of the number of different chronic conditions in predicting readmission rates and annual cost. • 4.) Bill Encinosa and Fred Hellinger recently published “The Impact of Medical Errors on 90 Day Costs and Outcomes: An Examination of Surgical Patients”. • All projects except the last one used our HCUP databases at AHRQ • we receive statewide discharges from 40 Partners, all-payers covered • a dozen Partners have provided encrypted patient identifiers that we refine by checking the age and gender of each supposed re-hospitalization.

5. Do patient safety events contribute to readmissions? • Ongoing study for presentation in more detail. • Under review at a journal. Already had a revision, but we’ll be happy to have more suggestions. • B. Friedman, Joanna Jiang, William Encinosa, Ryan Mutter

6. Objectives • To report 1-month and 3-month hospital readmissions, as well as deaths, after major surgical procedures in adults using a large multi-state and multi-payer database in 2004. • To test whether 9 selected patient safety events contribute to these outcomes after controlling for measures of severity of illness and the presence of unrelated chronic conditions.

7. Background/Motivation • A meta-analysis of small scale studies using clinical chart review found that better quality of care was associated with reduced readmission rates (Ashton, 1997). • Health plans and many patients would benefit from a reduction in safety events and readmissions. • BUT, hospitals and physicians do not always have an incentive to reduce readmissions (especially in Medicare and Medicaid). And there is a question if hospitals yet have adequate incentive to reduce safety events. (Mello et al., 2007)

8. Timeliness • Starting with FY2009, CMS will be collecting data on some safety events and other “never events”. • “Voluntary” to be used for public reporting • Several AHRQ Patient Safety Indicators. Some events measured differently. • when affect Medicare payment? Only postoperative infections so far.

9. Study Design • Healthcare Cost and Utilization inpatient discharge databases for 7 dispersed states: CA, FL, MO, NY, TN, UT, VA in 2004 • Adults in surgical DRGs, not related to pregnancy or delivery • Remove any rehospitalization that was birth-related or due to trauma. • Multinomial logistic regression model for 3 mutually exclusive outcomes: death, readmission, or discharge without readmission. The model yields simultaneously a relative risk of death and a relative risk of a readmission. • Control for: • severity level (using APR-DRG software) • unrelated chronic comorbidities (downloadable software from AHRQ) • payer group • 15 common DRGs at the initial admission

10. Selected Safety Events in Surgical Patients • Excluded safety events with more than a third of instances that were “present on admission” in two states with such data [Houchens, et al., 2008]. Example: Iatrogenic Pneumothorax • Numerator: • Discharges with ICD-9-CM code of 512.1 in any secondary diagnosis field among cases meeting the inclusion and exclusion rules for the denominator. • Denominator: • All surgical discharges age 18 years and older defined by surgical DRGs, subject to exclusions below. • Exclude cases: • MDC 14 (pregnancy, childbirth, and puerperium) • with diagnosis code of chest trauma or pleural effusion • with an ICD-9-CM procedure code of diaphragmatic surgery repair • with any code indicating thoracic surgery, lung or pleural biopsy, or assigned to cardiac surgery DRGs • Full specifications of all Patient Safety Indicators used in study: <http://www.qualityindicators.ahrq.gov/psi_overview.htm>

11. Selected Patient Safety Risks

12. Key Findings • The 3-month readmission rate was less than 17% for those with no safety event but 24.8% when a safety event occurred. • 2/3 of readmissions within 3 months occurred within the first month. • The relative risk ratio for readmission due to any safety event, adjusted for all other factors was 1.20(1.165-1.235), P<.001 • The in-hospital death rate was 1.3% with no safety event but 9.2% with a safety event. RRR=1.654(1.562-1.752), P<.001 • Medicare and Medicaid patients were more likely to have readmissions than privately insured patients: RRR about 1.5 in each case.

13. Multivariate results: Relative Risk Ratios

14. Discussion • Hospital readmissions are one way that safety events can have costly consequences, in addition to deaths or more expense at the initial stay. • A simultaneous multiple-outcome model makes sense (deaths tend to reduce readmissions) and is feasible. • The study suggests that extensive risk adjustment does not eliminate the contribution of safety events to readmissions (surgical patients, at least).

15. Final notes • Although safety events were found to contribute to readmissions, • the problems of effective management of chronic illness are probably a more important determinant of readmissions overall. • This type of research is the tip of the iceberg made possible by a decade of development of safety indicators and risk adjustment by AHRQ staff, contractors and consultants. • Ongoing infrastructure development for outside analysts to use with HCUP databases (Claudia Steiner and ThomsonReuters) • We hope this will make it easier to analyze readmissions for large databases • will require permission from more Partners to release encrypted patient identifiers.