An Examination of the Likelihood of Home Discharge After General Hospitalizations Among Medicaid Recipients

Abstract

Ability to predict discharge destination would be a useful way of optimizing posthospital care. We conducted a cross-sectional, multiple state study of inpatient services to assess the likelihood of home discharges in 2009 among Medicaid enrollees who were discharged following general hospitalizations. Analyses were conducted using hospitalization data from the states of California, Georgia, Michigan, and Mississippi. A total of 33 160 patients were included in the study among which 13 948 (42%) were discharged to their own homes and 19 212 (58%) were discharged to continue with institutional-based treatment. A multiple logistic regression model showed that gender, age, race, and having ambulatory care-sensitive conditions upon admission were significant predictors of home-based discharges. Females were at higher odds of home discharges in the sample (odds ratio [OR] = 1.631; 95% confidence interval [CI], 1.520-1.751), while patients with ambulatory care-sensitive conditions were less likely to get home discharges (OR = 0.739; 95% CI, 0.684-0.798). As the nation engages in the continued effort to improve the effectiveness of the health care system, cost savings are possible if providers and systems of care are able to identify admission factors with greater prospects for in-home services after discharge.

The Effects of Physician Characteristics on Patients’ Hospital Discharge Destination and Length of Stay

“…A common method for analyzing patients' mortality and length of stay within a hospital has been to estimate two separate equations on these two "outcomes"; a regression method for length of stay with patient and hospital characteristics as covariates, and separately, a probit or logit model to estimate a patient's mortality, with length of stay as an independent variable (Gaughan, Gravelle, Santos and Siciliani 2017;Hamilton and Ho 1998;Phillips, Luef and Ritchie 1995;Burns and Wholey 1991). Likewise, models of the patient's discharge destination from the hospital are frequently estimated using a multinomial logit model that allows for an unordered categorical variable (Reineck et al 2015;Mkanta et al 2017;Howrey, Kuo and Goodwin 2011). These models do not account for the duration dependence of the discharge destination on the length of time a patient has spent in the hospital.…”

Trends and best practices in health care cybersecurity insurance policy

“…These financial losses result from penalties, legal fees, class actions, payments for forensic investigation, investments to improve public relations, credit monitoring offered to customers, and other costs related to hiring and training staff 15 . Since most health care organizations are struggling to control their costs, 16 the challenge of cyberattacks may upend any little progress they have made. After a cyberattack on a health care organization, the cost of recovery alone could reach up to $1.7 million 17 .…”