Pam is a healthcare administration leader for a large network of hospitals and health service centers that is attempting to predict future healthcare utilization at their centers over the next 5 years. She obtains data regarding patient use across the hospital network and health service centers and projects forward over the next 5 years to determine which areas might experience continued growth. After applying her time series model, she is able to demonstrate that, indeed, the hospital network and health service centers will experience significant growth. As she prepares to share her results and findings with the board, she also considers advocating for the development of a new regional health service center to fill one of the areas that will experience the most growth according to her forecast projections.
As a current or future healthcare administration leader, you may be asked to assess strategic planning and decision making using time series analysis.
For this Discussion, review the resources for this week, and reflect on time series models and forecasting. Think about how you might implement these methods for healthcare administration practice.
Post a description of some variables that you might evaluate using time series in your health services organization or one with which you are familiar. Then, explain what types of models might be most appropriate to measure, and analyze these variables. Be specific, and provide examples.
Expert Solution Preview
Introduction:
As a medical professor in charge of creating college assignments and answers for medical college students, I am familiar with the use of time series analysis and forecasting in healthcare administration. In this scenario, Pam, a healthcare administration leader, uses time series models to predict future healthcare utilization and growth in her hospital network and health service centers. In this answer, I will provide a description of some variables that might be evaluated using time series in a health services organization, as well as the types of models that might be most appropriate for measuring and analyzing these variables.
Answer:
In a health services organization, variables that might be evaluated using time series include patient volume, revenue, and expenses. For example, a hospital might track the number of patients visiting the emergency department each month or the amount of revenue generated from elective surgeries over time. These variables can be analyzed using time series models to identify trends, patterns, and seasonality.
The most appropriate models for measuring and analyzing these variables depend on the data and the research question. For example, the ARIMA (autoregressive integrated moving average) model is commonly used for time series analysis in healthcare administration. This model is useful for forecasting future values based on past observations and can accommodate trends, seasonality, and non-stationary data.
Another model that might be appropriate for analyzing time series data in healthcare administration is the exponential smoothing model. This model is also useful for forecasting future values and is commonly used for short-term forecasting with little noise in the data.
For example, suppose a hospital wanted to forecast the number of patients visiting the emergency department over the next six months to allocate resources effectively. In that case, the ARIMA model might be more appropriate due to the long-term forecast and the possible impact of trends and seasonal patterns. Alternatively, if the hospital wanted to forecast the number of elective surgeries over the next month, the exponential smoothing model might be more appropriate due to the short-term forecast and the little noise in the data.
In conclusion, time series analysis and forecasting can be valuable tools for healthcare administration leaders to predict future growth and allocate resources effectively. Variables such as patient volume, revenue, and expenses can be analyzed using different models, depending on the data and the research question. The ARIMA and exponential smoothing models are two examples of models that might be appropriate for measuring and analyzing time series data.
