A comparison of techniques for forecasting utilization of Veterans Affairs hospitals

This study demonstrated that accurate, short-term forecasts of Veterans Affairs (VA) hospital utilization can be made using the Patient Treatment File (PTF), the inpatient discharge database of the VA. Accurate, short-term forecasts of two years or less can reduce required inventory levels, improve allocation of resources, and are essential for better financial management. These are all necessary achievements in an era of cost-containment.^ Six years of non-psychiatric discharge records were extracted from the PTF and used to calculate four indicators of VA hospital utilization: average length of stay, discharge rate, multi-stay rate (a measure of readmissions) and days of care provided. National and regional levels of these indicators were described and compared for fiscal year 1984 (FY84) to FY89 inclusive.^ Using the observed levels of utilization for the 48 months between FY84 and FY87, five techniques were used to forecast monthly levels of utilization for FY88 and FY89. Forecasts were compared to the observed levels of utilization for these years. Monthly forecasts were also produced for FY90 and FY91.^ Forecasts for days of care provided were not produced. Current inpatients with very long lengths of stay contribute a substantial amount of this indicator and it cannot be accurately calculated.^ During the six year period between FY84 and FY89, average length of stay declined substantially, nationally and regionally. The discharge rate was relatively stable, while the multi-stay rate increased slightly during this period. FY90 and FY91 forecasts show a continued decline in the average length of stay, while the discharge rate is forecast to decline slightly and the multi-stay rate is forecast to increase very slightly.^ Over a 24 month ahead period, all three indicators were forecast within a 10 percent average monthly error. The 12-month ahead forecast errors were slightly lower. Average length of stay was less easily forecast, while the multi-stay rate was the easiest indicator to forecast.^ No single technique performed significantly better as determined by the Mean Absolute Percent Error, a standard measure of error. However, Autoregressive Integrated Moving Average (ARIMA) models performed well overall and are recommended for short-term forecasting of VA hospital utilization. ^

Racial differences in heart failure with preserved ejection fraction in the ambulatory heart failure population

Racial differences in heart failure with preserved ejection fraction (HFpEF) have rarely been studied in an ambulatory, financially "equal access" cohort, although the majority of such patients are treated as outpatients. ^ Retrospective data was collected from 2,526 patients (2,240 Whites, 286 African American) with HFpEF treated at 153 VA clinics, as part of the VA External Peer Review Program (EPRP) between October 2000 and September 2002. Kaplan Meier curves (stratified by race) were created for time to first heart failure (HF) hospitalization, all cause hospitalization and death and Cox proportional multivariate regression models were constructed to evaluate the effect of race on these outcomes. ^ African American patients were younger (67.7 ± 11.3 vs. 71.2 ± 9.8 years; p < 0.001), had lower prevalence of atrial fibrillation (24.5 % vs. 37%; p <0.001), chronic obstructive pulmonary disease (23.4 % vs. 36.9%, p <0.001), but had higher blood pressure (systolic blood pressure > 120 mm Hg 77.6% vs. 67.8%; p < 0.01), glomerular filtration rate (67.9 ± 31.0 vs. 61.6 ± 22.6 mL/min/1.73 m2; p < 0.001), anemia (56.6% vs. 41.7%; p <0.001) as compared to whites. African Americans were found to have higher risk adjusted rate of HF hospitalization (HR 1.52, 95% CI 1.1 - 2.11; p = 0.01), with no difference in risk-adjusted all cause hospitalization (p = 0.80) and death (p= 0.21). ^ In a financially "equal access" setting of the VA, among ambulatory patients with HFpEF, African Americans have similar rates of mortality and all cause hospitalization but have an increased risk of HF hospitalizations compared to whites.^