Ensemble-based methods for forecasting census in hospital units

BACKGROUND: The ability to accurately forecast census counts in hospital departments has considerable implications for hospital resource allocation. In recent years several different methods have been proposed forecasting census counts, however many of these approaches do not use available patient-specific information. METHODS: In this paper we present an ensemble-based methodology for forecasting the census under a framework that simultaneously incorporates both (i) arrival trends over time and (ii) patient-specific baseline and time-varying information. The proposed model for predicting census has three components, namely: current census count, number of daily arrivals and number of daily departures. To model the number of daily arrivals, we use a seasonality adjusted Poisson Autoregressive (PAR) model where the parameter estimates are obtained via conditional maximum likelihood. The number of daily departures is predicted by modeling the probability of departure from the census using logistic regression models that are adjusted for the amount of time spent in the census and incorporate both patient-specific baseline and time varying patient-specific covariate information. We illustrate our approach using neonatal intensive care unit (NICU) data collected at Women & Infants Hospital, Providence RI, which consists of 1001 consecutive NICU admissions between April 1st 2008 and March 31st 2009. RESULTS: Our results demonstrate statistically significant improved prediction accuracy for 3, 5, and 7 day census forecasts and increased precision of our forecasting model compared to a forecasting approach that ignores patient-specific information. CONCLUSIONS: Forecasting models that utilize patient-specific baseline and time-varying information make the most of data typically available and have the capacity to substantially improve census forecasts

Who Treats Patients with Diabetes and Compensated Cirrhosis

Increasingly, patients with multiple chronic conditions are being managed in patient-centered medical homes (PCMH) that coordinate primary and specialty care. However, little is known about the types of providers treating complex patients with diabetes and compensated cirrhosis.We examined the mix of physician specialties who see patients dually-diagnosed with diabetes and compensated cirrhosis.Retrospective cross-sectional study using 2000-2013 MarketScan® Commercial Claims and Encounters and Medicare Supplemental Databases.We identified 22,516 adults (≥ 18 years) dually-diagnosed with diabetes and compensated cirrhosis. Patients with decompensated cirrhosis, HIV/AIDS, or liver transplantation prior to dual diagnosis were excluded.Physician mix categories: patients were assigned to one of four physician mix categories: primary care physicians (PCP) with no gastroenterologists (GI) or endocrinologists (ENDO); GI/ENDO with no PCP; PCP and GI/ENDO; and neither PCP nor GI/ENDO. Health care utilization: annual physician visits and health care expenditures were assessed by four physician mix categories.Throughout the 14 years of study, 92% of patients visited PCPs (54% with GI/ENDO and 39% with no GI/ENDO). The percentage who visited PCPs without GI/ENDO decreased 22% (from 63% to 49%), while patients who also visited GI/ENDO increased 71% (from 25% to 42%).This is the first large nationally representative study to document the types of physicians seen by patients dually-diagnosed with diabetes and cirrhosis. A large proportion of these complex patients only visited PCPs, but there was a trend toward greater specialty care. The trend toward co-management by both PCPs and GI/ENDOs suggests that PCMH initiatives will be important for these complex patients. Documenting patterns of primary and specialty care is the first step toward improved care coordination