PubMed · 10246956
The source did not provide an abstract. Follow the original record for more information.
Explore connections, maps & timelines
R H Box, C Poe, G Mitchell. 1980. A plan for developing rural hospital shared services.. https://pubmed.ncbi.nlm.nih.gov/10246956/
Cite the original work for its findings. Save a collection to share your selection of sources.
KEEP EXPLORING
Accurate simulation of the temperature distribution in a cooling lake or reservoir is often required for feasibility studies of engineering options that increase the cooling capacity of the waterbody. A three-dimensional hydrodynamic and temperature model has been developed and applied to several cooling lakes in the south-eastern United States. In this paper, the details of the modeling system are presented, along with the application to the Flint Creek Lake.
OBJECTIVE: To develop statistical models for predicting postoperative hospital and ICU stay in pediatric surgical patients based on preoperative clinical characteristics and operative factors related to the degree of surgical stress. We hypothesized that preoperative and operative factors will predict the need for ICU admission and may be used to forecast the length of ICU stay or postoperative hospital stay. DESIGN: Prospective data collection from 1,763 patients. SETTING: Tertiary care children's hospital. PATIENTS AND PARTICIPANTS: All pediatric surgical patients, including those undergoing day surgery. Patients undergoing dental or ophthalmologic surgical procedures were excluded. INTERVENTIONS: None. MEASUREMENTS AND RESULTS: A logistic regression model predicting ICU admission was developed from all patients. Poissonregression models were developed from 1,161 randomly selected patients and validated from the remaining 602 patients. The logistic regression model for ICU admission was highlypredictive (area under the receiver operating characteristics (ROC) curve = 0.981). In the data set used for development of Poisson regression models, significant correlations occurred between the observed and predicted ICU stay (Pearson r = 0.468, p < 0.0001, n = 131) and between the observed and predicted hospital stay for patients undergoing general (r = 0.695, p < 0.0001), orthopedic (r = 0.717, p < 0.0001), cardiothoracic (r = 0.746, p < 0.0001), urologic (r = 0.458, p < 0.0001), otorhinolaryngologic (r = 0.962, p < 0.0001), neurosurgical (r = 0.7084, p < 0.0001) and plastic surgical (r = 0.854, p < 0.0001) procedures. In the validation data set, correlations between predicted and observed hospital stay were significant for general (p < 0.0001), orthopedic (p < 0.0001), cardiothoracic (p = 0.0321) and urologic surgery (p = 0.0383). The Poisson models for length of ICU stay, otorhinolaryngology, neurosurgery or plastic surgery could not be validated because of small numbers of patients. CONCLUSIONS: Preoperative and operative factors may be used to develop statistical models predicting the need for ICU admission in pediatric surgical patients, and hospital stay following general surgical, orthopedic, cardiothoracic and urologic procedures. These statistical models need to be refined and validatedfurther, perhaps using data collection from multiple institutions.
An econometric model estimated the disutility of traveling long distances for depression treatment, and simulations calculated the utility loss associated with selective contracting in rural and urban areas. A representative sample of depression patients (n = 106) and all practicing providers (n = 3,710) in Arkansas were identified and the distances between them were calculated. Using discrete choice analysis, patient preferences for provider type and travel distance were estimated. Simulations calculated the utility loss associated with alternative scenarios of selective contracting. Provider type and distance were significant predictors of provider choice. To equate the utility loss associated with selective contracting in rural and urban areas, a slightly higher proportion of rural physicians and a substantially higher proportion of rural mental health specialists must be contracted. To avoid further reductions in geographic access, managed care organizations should contract with a higher proportion of rural providers than urban providers.