Impact of Global Budget Revenue Policy on Emergency Department Efficiency in the State of Maryland.

INTRODUCTION: On January 1, 2014, the State of Maryland implemented the Global Budget Revenue (GBR) program. We investigate the impact of GBR on length of stay (LOS) for inpatients in emergency departments (ED) in Maryland. METHODS: We used the Hospital Compare data reports from the Centers for Medicare and Medicaid Services (CMS) and CMS Cost Reports Hospital Form 2552-10 from January 1, 2012-March 31, 2016, with GBR hospitals from Maryland and hospitals from West Virginia (WV), Delaware (DE), and Rhode Island (RI). We implemented difference-in-differences analysis and investigated the impact of GBR implementation on the LOS or ED1b scores of Maryland hospitals using a mixed-effects model with a state-level fixed effect, a hospital-level random effect, and state-level heterogeneity. RESULTS: The GBR impact estimator was 9.47 (95% confidence interval [CI], 7.06 to 11.87, p-value<0.001) for Maryland GBR hospitals, which implies, on average, that GBR implementation added 9.47 minutes per year to the time that hospital inpatients spent in the ED in the first two years after GBR implementation. The effect of the total number of hospital beds was 0.21 (95% CI, 0.089 to 0.330, p-value = 0 .001), which suggests that the bigger the hospital, the longer the ED1b score. The state-level fixed effects for WV were -106.96 (95% CI, -175.06 to -38.86, p-value = 0.002), for DE it was 6.51 (95% CI, -8.80 to 21.82, p-value=0.405), and for RI it was -54.48 (95% CI, -82.85 to -26.10, p-value<0.001). CONCLUSION: Our results indicate that GBR implementation has had a statistically significant negative impact on the efficiency measure ED1b of Maryland hospital EDs from January 2014 to April 2016. We also found that the significant state-level fixed effect implies that the same inpatient might experience different ED processing times in each of the four states that we studied.

Impact of Health Policy Changes on Emergency Medicine in Maryland Stratified by Socioeconomic Status.

INTRODUCTION: On January 1, 2014, the financing and delivery of healthcare in the state of Maryland (MD) profoundly changed. The insurance provisions of the Patient Protection and Affordable Care Act (ACA) began implementation and a major revision of MD's Medicare waiver ushered in a Global Budget Revenue (GBR) structure for hospital reimbursement. Our objective was to analyze the impact of these policy changes on emergency department (ED) utilization, hospitalization practices, insurance profiles, and professional revenue. We stratified our analysis by the socioeconomic status (SES) of the ED patient population. METHODS: We collected monthly mean data including patient volume, hospitalization percentages, payer mix, and professional revenue from January 2013 through December 2015 from a convenience sample of 11 EDs in Maryland. Using regression models, we compared each of the variables 18 months after the policy changes and a six-month washout period to the year prior to ACA/GBR implementation. We included the median income of each ED's patient population as an explanatory variable and stratified our results by SES. RESULTS: Our 11 EDs saw an annualized volume of 399,310 patient visits during the study period. This ranged from a mean of 41 daily visits in the lowest volume rural ED to 171 in the highest volume suburban ED. After ACA/GBR, ED volumes were unchanged (95% confidence interval [CI] [-1.58-1.24], p=.817). Hospitalization percentages decreased significantly by 1.9% from 17.2% to 15.3% (95% CI [-2.47%-1.38%], p<.001). The percentage of uninsured patients decreased from 20.4% to 11.9%. This 8.5% change was significant (95% CI [-9.20%-7.80%], p<.001). The professional revenue per relative value unit increased significantly by $3.97 (95% CI [3.20-4.74], p<.001). When stratified by the median patient income of each ED, changes in each outcome were significantly more pronounced in EDs of lower SES. CONCLUSION: Health policy changes at the federal and state levels have resulted in significant changes to emergency medicine practice and finances in MD. Admission and observation percentages have been reduced, fewer patients are uninsured, and professional revenue has increased. All changes are significantly more pronounced in EDs with patients of lower SES.

Drivers of ED efficiency: a statistical and cluster analysis of volume, staffing, and operations.

STUDY OBJECTIVE: The percentage of patients leaving before treatment is completed (LBTC) is an important indicator of emergency department performance. The objective of this study is to identify characteristics of hospital operations that correlate with LBTC rates. METHODS: The Emergency Department Benchmarking Alliance 2012 and 2013 cross-sectional national data sets were analyzed using multiple regression and k-means clustering. Significant operational variables affecting LBTC including annual patient volume, percentage of high-acuity patients, percentage of patients admitted to the hospital, number of beds, academic status, waiting times to see a physician, length of stay (LOS), registered nurse (RN) staffing, and physician staffing were identified. LBTC was regressed onto these variables. Because of the strong correlation between waiting times measured as door to first provider (DTFP), we regressed DTFP onto the remaining predictors. Cluster analysis was applied to the data sets to further analyze the impact of individual predictors on LBTC and DTFP. RESULTS: LOS and the time from DTFP were both strongly associated with LBTC rate (P<.001). Patient volume is not significantly associated with LBTC rate (P=.16). Cluster analysis demonstrates that physician and RN staffing ratios correlate with shorter DTFP and lower LBTC. CONCLUSION: Volume is not the main driver of LBTC. DTFP and LOS are much more strongly associated. We show that operational factors including LOS and physician and RN staffing decisions, factors under the control of hospital and physician executives, correlate with waiting time and, thus, in determining the LBTC rate.

The impact of electronic health record implementation on emergency physician efficiency and patient throughput.

BACKGROUND: In emergency departments (EDs), the implementation of electronic health records (EHRs) has the potential to impact the rapid assessment and management of life threatening conditions. In order to quantify this impact, we studied the implementation of EHRs in the EDs of a two hospital system. METHODS: using a prospective pre-post study design, patient processing metrics were collected for each ED physician at two hospitals for 7 months prior and 10 months post-EHR implementation. Metrics included median patient workup time, median length of stay, and the composite outcome indicator "processing time." RESULTS: median processing time increased immediately post-implementation and then returned to, and surpassed, the baseline level over 10 months. Overall, we see significant decreases in processing time as the number of patients treated increases. CONCLUSIONS: implementation of new EHRs into the ED setting can be expected to cause an initial decrease in efficiency. With adaptation, efficiency should return to baseline levels and may eventually surpass them. IMPLICATIONS: while EDs can expect long term gains from the implementation of EHRs, they should be prepared for initial decreases in efficiency and take preparatory measures to avert adverse effects on the quality of patient care.

The impact of hospital utilization on patient readmission rate.

We investigate the issue of patient readmission at a large academic hospital in the U.S. Specifically, we look for evidence that patients discharged when post-operative unit utilization is high are more likely to be readmitted. After examining data from 7,800 surgeries performed in 2007, we conclude that patients who are discharged from a highly utilized post-operative unit are more likely to be readmitted within 72 h. Each additional bed utilized at time of discharge increases the odds of readmission on average by 0.35% (Odds Ratio = 1.008, 95% CI [1.003, 1.012]). We propose that this effect is due to an increased discharge rate when the unit is highly utilized.

Contribution of interfacility patient movement to overall methicillin-resistant Staphylococcus aureus prevalence levels.

OBJECTIVES: The effect of patient movement between hospitals and long-term care facilities (LTCFs) on methicillin-resistant Staphylococcus aureus (MRSA) prevalence levels is unknown. We investigated these effects to identify scenarios that may lead to increased prevalence in either facility type. METHODS: We used a hybrid simulation model to simulate MRSA transmission among hospitals and LTCFs. Transmission within each facility was determined by mathematical model equations. The model predicted the long-term prevalence of each facility and was used to assess the effects of facility size, patient turnover, and decolonization. RESULTS: Analyses of various healthcare networks suggest that the effect of patients moving from a LTCF to a hospital is negligible unless the patients are consistently admitted to the same unit. In such cases, MRSA prevalence can increase significantly regardless of the endemic level. Hospitals can cause sustained increases in prevalence when transferring patients to LTCFs, where the population size is smaller and patient turnover is less frequent. For 1 particular scenario, the steady-state prevalence of a LTCF increased from 6.9% to 9.4% to 13.8% when the transmission rate of the hospital increased from a low to a high transmission rate. CONCLUSIONS: These results suggest that the relative facility size and the patient discharge rate are 2 key factors that can lead to sustained increases in MRSA prevalence. Consequently, small facilities or those with low turnover rates are especially susceptible to sustaining increased prevalence levels, and they become more so when receiving patients from larger, high-prevalence facilities. Decolonization is an infection-control strategy that can mitigate these effects.

Examining the discharge practices of surgeons at a large medical center.

We investigate the discharge practices at a large medical center. Specifically, we look for indications that patients are being discharged sooner because of hospital bed-capacity constraints. Using survival analysis techniques, we find statistically significant evidence to indicate that surgeons adjust their discharge practices to accommodate the surgical schedule and number of available recovery beds. We find higher discharge rates on days when utilization is high. We also find an increased discharge rate on days when more surgeries are scheduled. Our findings suggest that discharge decisions are made with bed-capacity constraints in mind. We discuss possible explanations for this, as well as the medical and managerial implications of our findings.