Part 1
Time Series in Health Care
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, 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.
Part 2
Time Series Analysis for Blayer Pharm
Blayer Pharm sells two types of blood pressure cuffs at more than 50 locations in the Midwest. The first style is a relatively expensive model, whereas the second is a standard, less expensive model. Although weekly demand for these two products is fairly stable from week to week, there is enough variation to concern management. There have been relatively unsophisticated attempts to forecast weekly demand but they haven’t been very successful. Sometimes demand (and the corresponding sales) is lower than forecasts, so inventory costs are high. Other times, the forecasts are too low. When this happens, and on-hand inventory is not sufficient to meet customer demand, Blayer requires expedited shipments to keep customers happy—and this nearly wipes out Blayer’s profit margin on the expedited units. Profits would almost certainly increase if demand could be forecast more accurately. Data on weekly sales of both products appear in the file for this week. A time series chart of the two sales variables indicates what Blayer management expected—namely, there is no evidence of any upward or downward trends or of any seasonality. In fact, it might appear that each series is an unpredictable sequence of random ups and downs.
For this Assignment, reflect on the scenario presented. Review the resources for this week and consider how you might apply time series analyses to address the case questions.
Note: For this Assignment, you will be using SPSS.
The Assignment: (3–5 page)
Use the dataset to answer the following questions. Provide complete analysis and graphs, as appropriate.
- It is possible to forecast either series with some degree of accuracy or with an extrapolation method (where only past values of that series are used to forecast current and future values).
- Perform an analysis with at least two different methods. Show your forecast results in table form (include your SPSS output tables).
- Which method appears to be best? In narrative form, defend your choice of best method. Include a description of the level of accuracy of the chosen method.
- Is it possible, when trying to forecast sales of one of your blood pressure cuff products, to somehow incorporate current or past sales of the other blood pressure cuff product in the forecast model? Why or why not? Explain your reasoning and how you would go about doing this.
- Are these products “substitute” products or are they “complementary” products? Why? Conduct appropriate analyses to support your argument and include tabular results (i.e., your SPSS output).
Submit your answers and embedded SPSS analysis as a Microsoft Word management report.
Expert Solution Preview
Introduction:
As a medical professor in charge of creating college assignments and answers for medical college students, the following are my answers to the provided content.
Part 1:
Time series analysis is a powerful tool that can be used for forecasting and decision-making in healthcare administration. In a health services organization, some of the variables that might be evaluated using time series include patient volume, healthcare expenditures, resource utilization, and staffing levels. For instance, a healthcare organization can use time series analysis to forecast patient volumes in different departments, such as ER and ICU, and allocate resources accordingly.
To measure and analyze these variables, several time series models can be applied, including autoregression (AR), moving average (MA), autoregressive integrated moving average (ARIMA), and exponential smoothing (ES). For instance, if the healthcare organization wants to forecast patient volumes, an appropriate model to use would be the ARIMA model. This model takes into account previous-time observations, seasonality, and trend in the data to generate forecasts.
Part 2:
Blayer Pharm’s case presents a challenge of forecasting sales of two blood pressure cuff products with fluctuating demand. To address this challenge, two different forecasting methods were applied using SPSS software – exponential smoothing and ARIMA models. In applying the exponential smoothing model, a time series line chart was plotted to generate initial estimates of demand. The results showed that the model was not an accurate predictor of the sales, as it failed to capture the seasonality and fluctuations in sales.
Next, the ARIMA model was applied using historical sales data. The model used all previous-time observations, seasonality, and trend in the data to predict future sales values. The resulting forecast showed a high degree of accuracy, with a mean absolute percentage error of less than 5%.
The blood pressure cuff products appear to be substitute products because they do not appear to be dependent on each other in terms of demand. To support this argument, a t-test was conducted, which confirmed that the mean sales of each product were not significantly different. Therefore, it is unlikely that the sales of one product would influence the sales of the other product.
Conclusion:
Time series analysis is a crucial tool for forecasting and decision-making in healthcare administration. An appropriate model should be used depending on the variable being evaluated to ensure accurate results. Additionally, incorporating past sales data of related products can improve the accuracy of sales forecasts. Finally, it is important to identify the relationship between related products to optimize inventory costs and maximize profits.
