Data Model Case 8: Dallas Health Network Activity-Based Cost

Data Modelcase 8 Dallas Health Network Activity Based Costing Abc2

Data Model CASE 8 DALLAS HEALTH NETWORK: Activity-Based Costing (ABC) 2/18/19 Excel Spreadsheet Updates/Model Generation This case illustrates the use of activity-based costing to estimate the costs associated with two alternative approaches to providing ultrasound services at three clinic locations. The worksheet uses both operating and capital (equipment) cost input data to estimate both operating and total costs for the two alternatives. The model consists of a complete base case analysis--no changes need to be made to the existing MODEL-GENERATED DATA section. However, values in the INPUT DATA section of the student spreadsheet have been replaced by zeros. Students must select appropriate input values and enter them into the cells with values colored red.

After this is done, any error cells will be corrected and the base case solution will appear. The KEY OUTPUT section includes the most important output from the MODEL-GENERATED DATA section.

Assignment Instructions

Estimate the base case cost of Alternatives 1 and 2 regarding the provision of ultrasound services.

Determine which alternative has the lower total cost.

Redo the analysis assuming that the per unit supplies cost; billing and collection cost; general administration cost; and transportation, setup, and breakdown costs are higher than the base case values by 10 percent. Then, redo the analysis again assuming these costs are 20 percent higher than the base case values.

Return to the base case. Calculate what value for transportation and setup costs would make the costs of the two alternatives the same.

Using all base case data, assume that a 5 percent discount is available if three machines are purchased. Assess the effect on costs and identify what discount amount would make the two alternatives equal in costs.

Redo the base case analysis assuming a useful life of 3 years. Then, assume a life of 7 years. Discuss how these changes affect your evaluation.

Evaluate whether the analyses from questions 3 through 6 influence your decision on which alternative has the lowest cost.

Identify subjective factors that could influence the choice between the two alternatives.

State your final decision based on the analysis.

Reflect on three key learning points from this case.

Sample Paper For Above instruction

The case of Dallas Health Network’s activity-based costing for ultrasound services presents a comprehensive analysis of two potential approaches to delivering outpatient ultrasound care across three clinics. This examination not only underscores the importance of precise cost estimation but also highlights how varying assumptions impact managerial decision-making regarding resource allocation and service delivery models.

In estimating the base case costs for Alternatives 1 and 2, it is essential to accurately compile both operational and capital expenses. Operating costs include appointment scheduling, patient check-in and check-out, ultrasound testing, film processing, reading, billing, and general administration. Capital costs involve the purchase of ultrasound machines, transportation, setup, and breakdown costs, as well as maintenance and depreciation over the useful life of the equipment. The initial challenge is to input realistic data into the model, replacing default zeros with informed estimates based on historical data, industry standards, and expert opinions.

The analysis reveals that the lower total cost alternative hinges on the precise allocation of fixed versus variable costs. When costs such as supplies, billing, administration, and transportation are increased by 10 and then 20 percent, the sensitivity of the model becomes evident. Cost increases impact the total expenditure significantly, which could shift the preference from one alternative to another, especially if one model is more sensitive to such changes. These scenarios demonstrate the importance of conducting sensitivity analyses to understand how cost fluctuations influence decision-making and to identify critical cost drivers.

The next layer of analysis involves introducing a cost equivalence scenario based on transportation and setup costs. By modeling varying transportation expenses, one can find the threshold at which both alternatives incur equivalent costs, providing invaluable insight into negotiations or cost-control strategies. For example, a reduction in transportation costs might make the more capital-intensive alternative equally attractive, emphasizing the importance of logistics management in healthcare delivery.

Further, the incorporation of purchase discounts for multiple machines shows how bulk procurement can influence economic decisions. Applying a 5 percent discount on equipment costs, contingent on the purchase of three units, can alter the cost calculus, potentially favoring one option over another. Calculating the discount amount necessary to equalize costs informs negotiations and procurement strategies, emphasizing the value of economies of scale.

The useful life of equipment significantly affects depreciation and maintenance costs. Shortening the life to 3 years increases annual costs, potentially favoring alternatives with lower upfront investments. Conversely, extending the lifespan to 7 years dilutes the depreciation expense across more years, possibly favoring models with higher initial capital expenditure but lower annual costs. These variations highlight crucial factors in capital budgeting and long-term planning in healthcare facilities.

Multiple analyses, including sensitivity to costs, discount effects, and equipment lifespan, influence the preferred alternative. A comprehensive evaluation considers both quantitative data and qualitative factors, such as ease of implementation, staff skill requirements, and patient access considerations. Sometimes, subjective factors like vendor relationships or operational convenience may outweigh purely financial metrics. Therefore, decision-makers should balance empirical findings with strategic objectives and stakeholder interests.

Based on the cumulative analysis, the final decision to select the most cost-effective approach should incorporate not only the computed costs but also subjective factors such as reliability, scalability, and service quality. The choice might favor a higher initial capital investment if it results in lower operating costs over the equipment’s lifespan, especially when long-term savings and improved patient care are considered. Conversely, a more conservative approach might prioritize minimal upfront costs, accepting higher ongoing expenses.

From this case, three key learning points emerge. First, activity-based costing provides a nuanced perspective on resource utilization, enabling better decision-making. Second, sensitivity analysis is critical for understanding how assumptions influence outcomes and for preparing contingency plans. Third, integrating financial modeling with strategic and operational considerations ensures more resilient healthcare management decisions. Ultimately, these lessons underscore the importance of detailed cost analysis and strategic planning in optimizing healthcare service delivery.

References

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