Search Results

You are looking at 1 - 8 of 8 items for :

  • "hospital ownership" x
Clear All
Full access

Ashish Sonig, Imad Saeed Khan, Rishi Wadhwa, Jai Deep Thakur and Anil Nanda

in various regions. 2) Hospital ownership. The data were analyzed based on the following parameters: government, private and non-profit, private, and investor owned. We collapsed the categories into government and private. 3) Hospital location. The location of a hospital was divided into rural and urban areas. 4) Hospital bedsize. In the NIS database, hospitals are classified on the basis of bedsize as small, medium, and large. The details are provided in Table 1 . Different regions have different definitions of hospital bedsize. 5) Teaching status of the hospital

Full access

.3171/2017.4.FOC-LSRSabstracts 2017.4.FOC-LSRSABSTRACTS Abstract Poster 07. Hospital Ownership and Teaching Status Affects Perioperative Outcomes Following Lumbar Spinal Fusion Wesley Durand , ScB 1 , Joseph Johnson , ScB (2018) 2 , Neill Li , BS, MD 3 , JaeWon Yang , BA 1 , Adam Eltorai , BA 4 , J. Mason DePasse , MD 5 , and Alan Daniels , MD 6 1 Brown University, Warren Alpert Medical School, Providence, RI 2 Brown University, Providence, RI 3 Department of Orthopaedics, Warren

Restricted access

Whitney E. Muhlestein, Dallin S. Akagi, Amy R. McManus and Lola B. Chambless

as a variable. Analysis of ensemble 2 showed that nonelective admission, non-Southern hospital geography, the presence of any postoperative complication, non-white race, and surgery at private investor hospital most strongly predict higher total charges. Given their importance in ensemble 1, it is likely that admission type, hospital region, and patient race exert their influence on ensemble predictions at least partially independent of their influence on LOS. Hospital ownership type was the 6th most important variable in ensemble 1, so we were also unsurprised to

Free access

San Diego, CA • April 13–17, 2019

. Multivariate regression adjusted for patient age, sex, race, insurance, income, severity of illness, length of stay, emergency admission, wage index, hospital ownership, location/teaching status, hospital region, and DRG weights. We created a model for centralization of neurosurgical care, where non-emergency admissions with minor risk of mortality were considered as transfer candidates. Results 12,129,029 total admissions underwent neurosurgery from 2002 to 2014, with 59.6% treated at high-volume hospitals. Patients at high-volume centers were more likely to privately

Restricted access

Jonathan Dallas, Chevis N. Shannon and Christopher M. Bonfield

Collection After the study cohort was identified, data pertaining to each patient were collected. Available sociodemographic characteristics included age, sex, race, primary insurance, income quartile, and National Center for Health Statistics (NCHS) status (a system that details the urban/rural classification of a patient’s household). Baseline hospital characteristics included bedsize, urban/rural and teaching status, US census region, and hospital ownership; annual NMS fusion volume was subsequently calculated based on the number of times an individual hospital was

Restricted access

Jonathan Dallas, Chevis N. Shannon and Christopher M. Bonfield

Collection After the study cohort was identified, data pertaining to each patient were collected. Available sociodemographic characteristics included age, sex, race, primary insurance, income quartile, and National Center for Health Statistics (NCHS) status (a system that details the urban/rural classification of a patient’s household). Baseline hospital characteristics included bedsize, urban/rural and teaching status, US census region, and hospital ownership; annual NMS fusion volume was subsequently calculated based on the number of times an individual hospital was

Free access

Oliver Y. Tang, James S. Yoon, Anna R. Kimata and Michael T. Lawton

.4%)  Black 11,625 (14.2%)  Hispanic 16,062 (19.7%)  Asian 1,979 (2.4%)  Native American 868 (1.1%)  Other 4,192 (5.1%) Insurance status  Medicaid 40,376 (38.2%)  Private insurance 53,925 (51.0%)  Self-pay 5,493 (5.2%)  No charge 238 (0.2%)  Other 5,729 (5.4%) Income level of zip code  Quartile 1 (bottom) 28,359 (29.8%)  Quartile 2 24,940 (26.2%)  Quartile 3 22,744 (23.9%)  Quartile 4 (top) 19,231 (20.2%) Hospital ownership  Government, nonfederal 21,012 (20.3%)  Private, nonprofit 76,673 (74.1%)  Private, for-profit 5,759 (5.6%) Hospital location & teaching status  Rural 2

Restricted access

Ryan G. Chiu, Blake E. Murphy, David M. Rosenberg, Amy Q. Zhu and Ankit I. Mehta

nonprofit hospitals in the setting of ICH. Methods Data Source The National Inpatient Sample (NIS) is the largest all-payer inpatient care database in the United States, containing data from approximately 8 million unique hospitalizations annually across a 20% sample of US hospitals. 14 , 21 NIS core files (containing variables associated with inpatient care) and severity measures files (patient morbidity information) for the years 2012 to 2016 were merged and then combined with hospital weights files, which contained information regarding hospital ownership. Discharges