Forecasting cost based on a stochastic utilization plan and a fixed cost function

The application of Markov processes is very useful to health-care problems. The objective of this study is to provide a structured methodology of forecasting cost based upon combining a stochastic model of utilization (Markov Chain) and deterministic cost function. The perspective of the cost in this study is the reimbursement for the services rendered. The data to be used is the OneCare database of claim records of their enrollees over a two-year period of January 1, 1996–December 31, 1997. The model combines a Markov Chain that describes the utilization pattern and its variability where the use of resources by risk groups (age, gender, and diagnosis) will be considered in the process and a cost function determined from a fixed schedule based on real costs or charges for those in the OneCare claims database. The cost function is a secondary application to the model. Goodness-of-fit will be used checked for the model against the traditional method of cost forecasting. ^

'Reform' of the British national health service after the election of a labour government: Understanding the policy development process

The policy development process leading to the Labour government's white paper of December 1997—The new NHS: Modern, Dependable—is the focus of this project and the public policy development literature is used to aid in the understanding of this process. Policy makers who had been involved in the development of the white paper were interviewed in order to acquire a thorough understanding of who was involved in this process and how they produced the white paper. A theoretical framework is used that sorts policy development models into those that focus on knowledge and experience, and those which focus on politics and influence. This framework is central to understanding the evidence gathered from the individuals and associations that participated in this policy development process. The main research question to be asked in this project is to what extent do either of these sets of policy development models aid in understanding and explicating the process by which the Labour government's policies were developed. The interview evidence, along with published evidence, show that a clear pattern of policy change emerged from this policy development process, and the Knowledge-Experience and Politics-Influence policy making models both assist in understanding this process. The early stages of the policy development process were characterized as hierarchical and iterative, yet also very collaborative among those participating, with knowledge and experience being quite prevalent. At every point in the process, however, informal networks of political influence were used and noted to be quite prevalent by all of the individuals interviewed. The later stages of the process then became increasingly noninclusive, with decisions made by a select group of internal and external policy makers. These policy making models became an important tool with which to understand the policy development process. This Knowledge-Experience and Politics-Influence dichotomy of policy development models could therefore be useful in analyzing other types of policy development. ^

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. ^

Predicting the Past and Forecasting the Future

A commentary on Santos' article, "Explaining Scholarship Addressing Hispanic Children’s Issues."