Predicting length of stay for Medicare patients at a teaching hospital

Abstract

This research examines how the patients' characteristics and clinical indicators affect length of stay for the top five Diagnosis-Related-Groups (DRGs) for Medicare patients at a teaching hospital in the United States. The top DRGs were selected on the basis of volume per year. Teaching hospitals in the United States devote a significant amount of their resources to research and teaching, while providing treatment for patients. The ability to predict length of stay can substantially improve a teaching hospital's capacity utilization, while ensuring that resources are available to meet the health care needs of the Medicare population. Multiple regression models are developed to predict the length of stay using the patients' characteristics and clinical indicators as independent variables. The results indicate that approximately 60 percent (R(2)) of the variance in the length of stay is explained by the patients' characteristics and clinical indicators for these DRGs. The Mortality and Severity indices are found to be the strongest predictors for length of stay in all DRGs. Other patients' characteristics and clinical indicators such as age, gender, race/ethnicity, marital status, admission type and admission source are also significant predictors for some DRGs. In addition, most of these variables affect the length of stay in the same manner as shown in previous studies, even though the previous studies do not have the DRG specificity of this study.

Predictors of High Profit and High Deficit Outliers under SwissDRG of a Tertiary Care Center

“…MaWHinney et al found urgency/emergency admissions topredict longer length of stay and higher inpatient cost for a cohort of 2,481 cardiac patients. Apart from disease severity indicies, age and gender, Omachonu et al found mortality, ethnicity, martial status, emergency admission and admission from within a hospital or from another hospital to be important predictors for inpatient length of stay [ 33 ] and can therefore be assumed to also be important predictors for inpatient costs. Emergency admissions as well as admission from other care providers are thus important variables predicting cost.…”

A Panel Data Analysis of Hospital Variations in Length of Stay for Hip Replacements: Private Versus Public

“…When the finances are tight, the large differences cry out for attention. we can see, v h in major providers and teaching hospitals are higher than others, which can be explained partially by the higher cost of labor, more complex case mix (Freitas et al, 2012) and greater obligation of medical graduate education (Omachonu et al, 2004).…”

Modelling Hospital Admission and Length of Stay by Means of Generalised Count Data Models

“…age, sex, marital status, habits of smoking or alcohol consumption, health status at admission (e.g. Omachonu et al , ; Chang et al , ; Walczak et al , ; Marshall et al , ; Bithell and Devlin, ; Robinson et al , ); or (iii) evaluate factors based upon information of prior diseases and socioeconomic variables to identify agents at high risk of admission to hospital or post‐acute care facilities (e.g. Simonet et al , ; Donnan et al , ; Billings et al , ; Landi et al , ; Riphahn et al , ; Prieger, ; Smith et al , ; Marcantonio et al , ; Geil et al , ; Deb and Trivedi, ).…”