3 resultados para regularly entered default judgment set aside without costs

em DigitalCommons@The Texas Medical Center


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A sample of 157 AIDS patients 17 years of age or over were followed for six months from the date of hospital discharge to derive average total cost of medical care, utilization and satisfaction with care. Those referred for home care follow-up after discharge from the hospital were compared with those who did not receive home care.^ The average total cost of medical care for all patients was $34,984. Home care patient costs averaged \$29,614 while patients with no home care averaged $37,091. Private hospital patients had average costs of \$50,650 compared with $25,494 for public hospital patients. Hospital days for the six months period averaged 23.9 per patient for the no home care group and 18.5 days for home care group. Patient satisfaction with care was higher in the home care group than no home care group, with a mean score of 68.2 compared with 61.1.^ Other health services information indicated that 98% of the private hospital patients had insurance while only 2% of public hospital patients had coverage. The time between the initial date of diagnosis with AIDS and admission to the study was longer for private hospital patients, survival time over the study period was shorter, and the number of hospitalizations prior to entering the study was higher for private hospital patients. These results suggest that patients treated in the private hospital were sicker than public hospital patients, which may explain their higher average total cost. Statistical analyses showed that cost and utilization have no significant relationship with home care or no home care when controlling for indicators of the severity of illness and treatment in public or private hospital.^ In future studies, selecting a matched group of patients from the same hospital and following them for nine months to one year would be helpful in making a more realistic comparison of the cost effectiveness of home care. ^

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Using stress and coping as a unifying theoretical concept, a series of five models was developed in order to synthesize the survey questions and to classify information. These models identified the question, listed the research study, described measurements, listed workplace data, and listed industry and national reference data.^ A set of 38 instrument questions was developed within the five coping correlate categories. In addition, a set of 22 stress symptoms was also developed. The study was conducted within two groups, police and professors, on a large university campus. The groups were selected because their occupations were diverse, but they were a part of the same macroenvironment. The premise was that police officers would be more highly stressed than professors.^ Of a total study group of 80, there were 37 respondents. The difference in the mean stress responses was observable between the two groups. Not only were the responses similar within each group, but the stress level of response was also similar within each group. While the response to the survey instrument was good, only 3 respondents answered the stress symptom survey properly. It was determined that none of the 37 respondents believed that they were ill. This perception of being well was also evidenced by the grand mean of the stress scores of 2.76 (3.0 = moderate stress). This also caused fewer independent variables to be entered in the multiple regression model.^ The survey instrument was carefully designed to be universal. Universality is the ability to transcend occupational or regional definitions as applied to stress. It is the ability to measure responses within broad categories such as physiological, emotional, behavioral, social, and cognitive functions without losing the ability to measure the detail within the individual questions, or the relationships between questions and categories.^ Replication is much easier to achieve with standardized categories, questions, and measurement procedures such as those developed for the universal survey instrument. Because the survey instrument is universal it can be used as an analytical device, an assessment device, a basic tool for planning and a follow-up instrument to measure individual response to planned reductions in occupational stress. (Abstract shortened with permission of author.) ^

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The purpose of this study was to assess the impact of the Arkansas Long-Term Care Demonstration Project upon Arkansas' Medicaid expenditures and upon the clients it serves. A Retrospective Medicaid expenditure study component used analyses of variance techniques to test for the Project's effects upon aggregated expenditures for 28 demonstration and control counties representing 25 percent of the State's population over four years, 1979-1982.^ A second approach to the study question utilized a 1982 prospective sample of 458 demonstration and control clients from the same 28 counties. The disability level or need for care of each patient was established a priori. The extent to which an individual's variation in Medicaid utilization and costs was explained by patient need, presence or absence of the channeling project's placement decision or some other patient characteristic was examined by multiple regression analysis. Long-term and acute care Medicaid, Medicare, third party, self-pay and the grand total of all Medicaid claims were analyzed for project effects and explanatory relationships.^ The main project effect was to increase personal care costs without reducing nursing home or acute care costs (Prospective Study). Expansion of clients appeared to occur in personal care (Prospective Study) and minimum care nursing home (Retrospective Study) for the project areas. Cost-shifting between Medicaid and Medicare in the project areas and two different patterns of utilization in the North and South projects tended to offset each other such that no differences in total costs between the project areas and demonstration areas occurred. The project was significant ((beta) = .22, p < .001) only for personal care costs. The explanatory power of this personal care regression model (R('2) = .36) was comparable to other reported health services utilization models. Other variables (Medicare buy-in, level of disability, Social Security Supplemental Income (SSI), net monthly income, North/South areas and age) explained more variation in the other twelve cost regression models. ^