The Effects of Global Budgeting on Emergency Department Admission - PowerPoint PPT Presentation

1 The Effects of Global Budgeting on Emergency Department Admission Rates Jessica E. Galarraga, MD, MPH Department of Health Services Research, MedStar Health Research Institute @JGalarragaMD

Acknowledgements 2 • Key Collaborators: • John P. Sverha, MD • Bernard Black, PhD • Daniel L. Lemkin, MD • Laura Pimentel, MD • Jesse M. Pines, MD, MBA, MSCE • Arvind Venkat, MD • William J. Frohna, MD

Financial Disclosures 3 • No financial conflicts of interest • Grant Funding • Emergency Medicine Foundation Health Policy Research Scholar Award • MedStar Health Research Institute New Investigator Award

Maryland’s All -Payer Global Budget Revenue Model 4 • In 2014, Maryland launched a statewide reform that replaced fee-for- service hospital payments with a population-based payment model – a “global budget revenue” (GBR) model. 1-4 • Hospitals have a fixed revenue target, independent of patient volume or services provided, tied to all-payer quality metrics. 1,5 • Readmission Reduction Inventive Program • Maryland Hospital Acquired Conditions Program • Potentially Avoidable Utilization Savings Policy • We examined the effects of GBR on emergency department (ED) admission rates.

Methods – Study Population 5 • Retrospective study of all-payer medical record and billing data • January 1, 2012 to December 31, 2015 • Hospital-based adult ED encounters from Maryland and other Medicaid Expansion states – 10 Non-Maryland matched sites – 10 Maryland GBR sites – 5 Maryland Total Patient Revenue (TPR) sites • Exclusions: – Elopement, leaving AMA, left without being seen, expiration, and psychiatric encounters • Final Study Sample: • 3,175,210 ED encounters: – 1,397,560 GBR, 1,471,331 Non-Maryland matches, and 306,319 TPR • Mean Unadjusted ED Hospitalization Rate: 20.9% (95% CI: 17.8, 24.0)

Methods – Study Variables 6 • ED hospitalization rate: # hospital stays (inpatient and observation) / # ED visits • GBR adoption: start date of hospital’s GBR contract to account for 6 -month transition period from January-July 2014 • Subgroup Analysis by: • Ambulatory Care Sensitive Conditions (ACSCs) – Defined by 11 out of 13 AHRQ Prevention Quality Indicators 6 Excludes low birth weight (not relevant for adults) and lower extremity • amputations among diabetics (relates to hospital inpatient course) • Primary Clinical Diagnosis – Based on the ICD-9/10-CM diagnosis categorized using multi-level clinical classification software (MCCS) codes

Methods - Analysis 7 • Difference-in-differences analysis using hospital-fixed effect regression • Leads and Lags analysis to examine temporal trends • Regressions included hospital and year fixed effects, clustering of standard errors on hospital-level, and adjustment for: • Patient Factors: Age, Sex, Insurance Status • Encounter Factors: Primary Diagnosis (Clinical Classification Software Category) 7,8 • Hospital Factors: Annual ED volume, Trauma Level, Number of Hospital Beds, Teaching Status • Community Factors: Per Capita Income, Primary Care Provider to Population Ratio, Metropolitan Residency

8 Figure 1 . Monthly levels of risk-adjusted ED hospitalization rates relative to the global budget revenue (GBR) model implementation date in 2014 (month 0). Vertical lines show 95% CIs, with standard errors clustered on the hospital level.

Table 1. Difference-in-differences (DiD) analysis of ED admission and transfer rates in GBR versus Non- 9 Maryland and TPR hospitals. ED Hospitalization Rate PreGBR PostGBR Absolute Rate Difference Regression-based (2012-2013) (2014-2015) [95% CI] DiD [95% CI] GBR vs. Non-MD Matches † Admission from ED GBR 20.1% 19.3% -0.8% (-1.03, -0.58) ** -0.5% (-0.72, -0.39) ** Non-MD Matches 21.3% 21.0% -0.3% (-0.47,-0.03) * Transfer from ED to Another Hospital GBR 1.6% 1.6% -0.0% ( -0.06, 0.06) +0.1% (0.08, 0.17) ** Non-MD Matches 0.4% 0.3% -0.1% (-0.19,-0.07) ** GBR vs. TPR ‡ Admission from ED 20.1% 19.3% -0.8% (-1.03, -0.58) ** GBR -1.9% (-2.27, -1.80) ** TPR 16.1% 17.2% +1.1% (0.83, 1.46) ** Linear probability model. Dependent variable is ED admission (0-1), including inpatient and observation stays. Predictor variable for regressions is GBR dummy * post dummy (=1 for 2014-2015). Regressions include hospital and year fixed effects, primary clinical condition, and patient, hospital, and community factors. Standard errors are clustered on the hospital level. † GBR hospitals are hospitals subject to Maryland global budget revenue (GBR) program starting in 2014. Non-MD matches are matched hospitals in Medicaid expansion states other than Maryland. ‡ TPR hospitals are five rural Maryland hospitals included in total patient revenue (TPR) pilot program starting 2010. * Hospitalization rate difference with p-value < .05, ** Hospitalization rate difference with p-value < .0001

Table 2. Difference-in-differences (DiD) analysis of ED admissions for ambulatory-case-sensitive conditions (ACSC) versus other conditions, in GBR versus Non-Maryland and TPR hospitals. 10 ED Hospitalization Rate PreGBR PostGBR Absolute Rate Difference Regression-based DiD (2012-2013) (2014-2015) [95% CI] [95% CI] GBR vs. Non-MD Matches † ACSC GBR 36.4% 35.1% -1.3% (-2.45, -0.06) ** -1.1% (-2.13, -0.57) ** 49.1% 48.9% Non-MD Matches -0.2% (-1.36, 1.05) Title Arial Bold – 34pt Non-ACSC GBR 19.4% 18.6% -0.8% (-1.06,-0.61) * -0.5% (-0.8, -0.4) * Non-MD Matches 20.3% 20.0% -0.3% (-0.39,-0.05) * font GBR vs. TPR ‡ ACSC Presenter’s Name Subtitle Arial – 25pt font GBR 36.4% 35.1% -1.2% (-2.45, -0.06) ** +1.0% (-0.95,1.71) Office or Department Name TPR 38.6% 36.4% -2.2% ( -3.69,-0.76) ** Non-ACSC GBR 19.4% 18.6% -0.8% (-1.06,-0.61) * -2.1% (-2.57, -1.99) * TPR 15.1% 16.4% +1.3% (1.02, 1.65 ) * Linear probability model. Dependent variable is ED admission (0-1), including inpatient and observation stays. Predictor variable for regressions is GBR dummy * post dummy (=1 for 2014-2015). Regressions include hospital and year fixed effects, primary clinical condition, and patient, hospital, and community factors. Standard errors are clustered on the hospital level. † GBR hospitals are hospitals subject to Maryland global budget revenue (GBR) program starting in 2014. Non-MD matches are matched hospitals in Medicaid expansion states other than Maryland. ‡ TPR hospitals are five rural Maryland hospitals included in total patient revenue (TPR) pilot program starting 2010. * Hospitalization rate difference with p-value < .05, ** Hospitalization rate difference with p-value < .0001

Table 3. Difference-in-differences (DiD) analysis of ED hospitalizations in GBR versus Non-Maryland control hospitals by primary diagnosis, 2012-2015. 11 ED Hospitalization rate Regression-based PreGBR PostGBR Absolute Rate DiD [95% CI] MCCS Category Name (MCCS Code) ‡ (2012-2013) (2014-2015) Difference [95% CI] Diseases of the heart (7.2) GBR 41.0% 39.8% -1.2% (-2.3,0.0) ** +0.7% (-1.5, 0.2) Non-MD Matches 44.7% 42.8% -1.9% (-3.0,0.7) ** p = .1106 Symptoms, signs, and ill-defined conditions (17.1) Title Arial Bold – 34pt GBR 15.1% 15.4% +0.5% (-0.07, 9.7) +0.3% (-2.1,0.8 ) Non-MD Matches 11.7% 11.5% p =.0894 -0.2% (-1.8,0.4) Lower respiratory disease (8.8) *** font GBR 28.7% 28.1% -1.45% (-2.8 -0.1) -0.6% (-2.4,-1.2) ** Non-MD Matches 21.9% 22.8% +0.8% (-1.0,2.7) p =.0309 Presenter’s Name Subtitle Arial – 25pt font COPD and bronchiectasis (8.2) Office or Department Name GBR 40.2% 40.6% +0.9% (-1.2, 3.1) +0.4% (-2.3,3.1) Non-MD Matches 72.3% 71.8% p = .3877 -0.5% (-3.3,2.2) Cerebrovascular disease (7.3) GBR 78.7% 79.0% -1.1% (-3.4, 1.1) +0.3% (-2.4,3.1) Non-MD Matches 83.0% 84.4% +1.4% (-1.4,4.3) p =.3205 We show results for the top 10 most common clinical conditions among ED admissions. GBR includes encounters exposed to global budget revenue (GBR) implementation in Maryland hospitals. Non-MD matches includes encounters in matched hospitals located in Medicaid expansion states external to Maryland, not exposed to GBR. * Hospitalization rate difference with p < .0001, ** Hospitalization rate difference with p < .05, *** DiD result statistically significant, p < .05. ‡ MCCS = Multi-level clinical classification software category.