The relationship of insurance status, hospital ownership, and teaching status with interhospital transfers in California in 2000.
ABSTRACT Public hospitals and academic medical centers may admit more poorly insured transfer patients than do other institutions. The authors investigated the relationship of patient insurance status, hospital ownership, and hospital teaching status with interhospital transfers in California.
In 2003, data were derived from the hospital discharge abstract database for the year 2000 from the California Office of Statewide Health Planning and Development. Hospitals were categorized by ownership and teaching status; patients were categorized as being "good" or "poor" payers depending on the level of expected insurance reimbursement. Descriptive and multivariate analyses were used to assess the number of poor payer transfers admitted by each hospital group.
In 2000, there were 58,509 transfer and 2,320,479 direct admissions. All hospital groups admitted a higher percentage of good payer than poor payer transfer patients (85% vs. 15% respectively for all groups combined). Adjusted for total number of admissions and teaching status, the number of poor payer transfer patients admitted to county-owned and University of California hospitals was significantly higher than the statewide average (both p values < .001), while the number admitted to independent teaching hospitals was significantly lower than the statewide average (p < .001). The number of poor payer transfer patients admitted to independent teaching hospitals more closely resembled that of for-profit hospitals than that of University of California teaching hospitals.
In 2000, the likelihood of a hospital admitting a transfer patient appears to have been affected by both the patient's insurance status and the hospital's ownership. In general, good payer patients were more likely to be transferred than were poor payer patients, with poor payer transfer patients more likely to be admitted to publicly owned hospitals.
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ABSTRACT: UK policy promotes third sector organisations as providers of NHS funded health and social care. We examine the evidence for this policy through a systematic literature review. Our results highlight several problems of studies comparing non-profits with other provider forms, questioning their usefulness for drawing lessons outside the place of study. Most studies deem contextual factors and the regulatory framework in which providers operate as much more important than ownership form. We conclude that the literature does not support the policy of a larger role for the third sector in healthcare, let alone a switch to a market-based system.Social Policy and Society 09/2010; 9(04):515 - 526.
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ABSTRACT: Testicular torsion is a true urological emergency. We determined whether a delay in treatment due to hospital transfer or socioeconomic factors would impact the orchiectomy rate in children with this condition. We retrospectively evaluated the records of boys seen at a single institution emergency department who proceeded to surgery for a diagnosis of acute testicular torsion from 2003 to 2008. Charts were reviewed for transfer status, symptom duration, race, insurance presence or absence and distance from the hospital. Orchiectomy specimens were evaluated for histological confirmation of nonviability. We reviewed 97 records. The orchiectomy rate in patients who were vs were not transferred to the emergency department was 47.8% vs 68.9%, respectively (p = 0.07). Symptom duration was greater in the orchiectomy group with a mean difference of 47.9 hours (p <0.01). The mean transfer delay was 1 hour 15 minutes longer in the orchiectomy group (p = 0.01). Boys who underwent orchiectomy were 2.2 years younger than those who avoided orchiectomy (p = 0.01). Multivariate analysis showed that symptom duration and distance from the hospital were the strongest predictors of orchiectomy. Data suggest that torsion is a time dependent event and factors that delay time to treatment lead to poorer outcomes. These factors include distance from the hospital and the time delay associated with hospital transfer.The Journal of urology 10/2010; 184(4 Suppl):1743-7. · 3.75 Impact Factor
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ABSTRACT: Performance assessments based on in-hospital mortality for ICU patients can be affected by discharge practices such that differences in mortality may reflect variation in discharge patterns rather than quality of care. Time-specific mortality rates, such as 30-day mortality, are preferred but are harder to measure. The degree to which the difference between 30-day and in-hospital ICU mortality rates-or "discharge bias"-varies by hospital type is unknown. The aim of this study was to quantify variation in discharge bias across hospitals and determine the hospital characteristics associated with greater discharge bias. Retrospective cohort study. Nonfederal Pennsylvania hospital discharges in 2008. Eligible patients were 18 years old or older and admitted to an ICU. None. We used logistic regression with hospital-level random effects to calculate hospital-specific risk-adjusted 30-day and in-hospital mortality rates. We then calculated discharge bias, defined as the difference between 30-day and in-hospital mortality rates, and used multivariable linear regression to compare discharge bias across hospital types. A total of 43,830 patients and 134 hospitals were included in the analysis. Mean (SD) risk-adjusted hospital-specific in-hospital and 30-day ICU mortality rates were 9.6% (1.3) and 12.7% (1.5), respectively. Hospital-specific discharge biases ranged from -1.3% to 6.6%. Discharge bias was smaller in large hospitals compared with small hospitals, making large hospitals appear comparatively worse from a benchmarking standpoint when using in-hospital mortality instead of 30-day mortality. Discharge practices bias in-hospital ICU mortality measures in a way that disadvantages large hospitals. Accounting for discharge bias will prevent these hospitals from being unfairly disadvantaged in public reporting and pay-for-performance.Critical care medicine 01/2014; · 6.15 Impact Factor