THE RELATIONSHIP BETWEEN ATTITUDE ASSESSMENT/ROLE PERCEPTION AND THE FINANCIAL PERFORMANCE OF HOSPITAL DEPARTMENT MANAGERS

Much of the current healthcare financial literature addresses the concern of government officials, the public, and healthcare providers regarding the need for control of health care costs. The literature suggests that attitudes of hospital department managers toward their role in financial management affects their ability to effect favorable financial results.^ There were several objectives of the dissertation: (1) To identify whether or not there exists a relationship between the attitude/role perception of hospital managers and the financial performance of their departments. (2) To compile a descriptive survey data base of key factors identified in the financial literature from individual hospitals. (3) To compile a brief descriptive survey of hospital managers' financial management background and training (both formal and informal). (4) To conduct an attitude assessment/role perception survey regarding the importance or relevance of a suggested financial management role set (i.e., issues discussed in the current literature) as viewed by the selected hospital managers and their matched administrators. (5) To propose plausible theoretical models and statistical tests of seven proposed hypotheses.^ The statistical results of a variety of methods generally suggested, for the sample population, that the null hypothesis should not be rejected concerning the relationships between a department manager's financial attitudes and role perceptions and the resultant financial performance.^ The fact that the results of this study did not suggest that there was a significant relationship which existed between role perception and financial performance does not necessarily indicate that the theories supporting such a relationship in literature are false, not that such a relationship does not exist. Several alternative theories were postulated to explain the apparent lack of statistical relationship, and suggestions for refinement and/or improvement of further research were discussed. ^

The impact of 1995--1996 health sector reforms in the effectiveness of malaria control program in the state of Orissa, India

Malaria poses a significant public health problem worldwide. The World Health Organization indicates that approximately 40% of the world's population and almost 85% of the population from the South–East Asian region is at risk of contracting malaria. India being the most populous country in the region, contributes the highest number of malaria cases and deaths attributed to malaria. Orissa is the state that has the highest number of malaria cases and deaths attributable to malaria. A secondary data analysis was carried out to evaluate the effectiveness of the World bank-assisted Malaria Action Program in the state of Orissa under the health sector reforms of 1995-96. The secondary analysis utilized the government of India's National Anti Malaria Management Information System's (NAMMIS) surveillance data and the National Family Health Survey (NFHS–I and NFHS–II) datasets to compare the malaria mortality and morbidity in the state between 1992-93 and 1998-99. Results revealed no effect of the intervention and indicated an increase of 2.18 times in malaria mortality between 1992-1999 and an increase of 1.53 times in malaria morbidity between 1992-93 and 1998-99 in the state. The difference in the age-adjusted malaria morbidity in the state between the time periods of 1992-93 and 1998-99 proved to be highly significant (t = 4.29 df=16, p<. 0005) whereas the difference between the increase of age-adjusted malaria morbidity during 1992-93 and 1998-99 between Orissa (with intervention) and Bihar (no intervention) proved to be non significant (t=.0471 df=16, p<.50). Factors such as underutilization of World Bank funds for the malaria control program, inadequate health care infrastructure, structural adjustment problems, poor management, poor financial management, parasite resistance to anti-malarial drugs, inadequate supply of drugs and staff shortages may have contributed to the failure of the program in the state.^

A comparison of techniques for forecasting utilization of Veterans Affairs hospitals

This study demonstrated that accurate, short-term forecasts of Veterans Affairs (VA) hospital utilization can be made using the Patient Treatment File (PTF), the inpatient discharge database of the VA. Accurate, short-term forecasts of two years or less can reduce required inventory levels, improve allocation of resources, and are essential for better financial management. These are all necessary achievements in an era of cost-containment.^ Six years of non-psychiatric discharge records were extracted from the PTF and used to calculate four indicators of VA hospital utilization: average length of stay, discharge rate, multi-stay rate (a measure of readmissions) and days of care provided. National and regional levels of these indicators were described and compared for fiscal year 1984 (FY84) to FY89 inclusive.^ Using the observed levels of utilization for the 48 months between FY84 and FY87, five techniques were used to forecast monthly levels of utilization for FY88 and FY89. Forecasts were compared to the observed levels of utilization for these years. Monthly forecasts were also produced for FY90 and FY91.^ Forecasts for days of care provided were not produced. Current inpatients with very long lengths of stay contribute a substantial amount of this indicator and it cannot be accurately calculated.^ During the six year period between FY84 and FY89, average length of stay declined substantially, nationally and regionally. The discharge rate was relatively stable, while the multi-stay rate increased slightly during this period. FY90 and FY91 forecasts show a continued decline in the average length of stay, while the discharge rate is forecast to decline slightly and the multi-stay rate is forecast to increase very slightly.^ Over a 24 month ahead period, all three indicators were forecast within a 10 percent average monthly error. The 12-month ahead forecast errors were slightly lower. Average length of stay was less easily forecast, while the multi-stay rate was the easiest indicator to forecast.^ No single technique performed significantly better as determined by the Mean Absolute Percent Error, a standard measure of error. However, Autoregressive Integrated Moving Average (ARIMA) models performed well overall and are recommended for short-term forecasting of VA hospital utilization. ^