HCM3002 Economics Of Healthcare 2013 South University ✓ Solved

Page 1 Of 4 Hcm3002 Economics Of Healthcare 2013 South Universitymul

Describe the process of building a multiattribute utility (MAU) model for decision making, including defining alternatives and criteria, evaluating alternatives on each attribute, assigning relative weights, calculating weighted scores, and performing sensitivity analysis. Apply this process to compare and select a health insurance plan based on multiple performance indicators and costs, using spreadsheet organization. Summarize your decision, justify your choice, and analyze confidence levels with and without the MAU model. Discuss the advantages and disadvantages of using the MAU technique for healthcare decision making, supported by academic references.

Sample Paper For Above instruction

Making well-informed healthcare decisions often involves evaluating multiple criteria that influence the overall utility of a particular choice. The multiattribute utility (MAU) model provides a systematic framework for assessing alternatives by incorporating various performance measures and personal preferences. This paper demonstrates the application of the MAU process to select a health insurance plan, detailing each step from criteria identification to sensitivity analysis, and discusses its utility compared to traditional decision-making approaches.

Introduction

Healthcare decision making frequently involves selecting among complex options where multiple attributes must be weighed. Traditional decision-making methods may rely on intuition or isolated evaluation of cost or performance. However, the MAU model offers a structured approach that enables decision-makers to incorporate diverse criteria proportionally according to their importance, ultimately deriving a single score that guides choice. This approach aligns with the principles of rational decision theory and improves transparency and justifiability in healthcare choices.

Step 1: Defining Alternatives and Criteria

The initial step involves choosing alternatives and establishing criteria on which to evaluate them. For this paper, the alternatives are four health insurance plans available to employees in a given region: Aetna Health, MetroPlus, Empire, and Oxford. All plans offer managed care with similar benefit packages, so the differentiation relies on premiums and performance measures.

The criteria selected include premium cost (both individual and family), plan coverage, access to care, patient satisfaction, network breadth, and preventive service quality. The criteria span across multiple performance domains, such as access, quality, and cost, ensuring a comprehensive assessment. For simplicity, eight attributes are selected: premium, access to services, patient satisfaction, network size, preventive care quality, out-of-pocket costs, provider choice, and coverage comprehensiveness.

Step 2: Evaluating Alternatives on Each Attribute

Each plan is rated on each attribute based on data from online reports, plan documentation, and simulated performance scores. For quantitative attributes like premiums and out-of-pocket costs, proxies or relative scores are calculated—e.g., higher premiums assigned a lower score, normalized on a 0-100 scale. For qualitative attributes like patient satisfaction and coverage comprehensiveness, ratings are derived from survey scores or review reports.

For example, if Aetna's premium is $4,555, and Oxford's is $6,029, a rating system is used where the lowest premium plan scores 100, and the highest scores 0, with others scaled accordingly. Similarly, for subjective measures, reports indicate relative performance, which is converted into numerical scores between 0 and 100, maintaining consistency across all attributes.

Step 3: Assigning Relative Weights to Attributes

Next, the relative importance of each attribute is assessed. The decision maker ranks attributes in order of importance—for instance, cost may be most critical, followed by access and satisfaction, with other criteria weighted accordingly. These weights are then quantified to sum to 100. For example, premium might be assigned 40%, access 25%, satisfaction 15%, network size 10%, and other attributes sharing the remaining 10%. If initial weights do not sum precisely to 100, normalization is performed by dividing each by the total sum to ensure proportional importance across all attributes.

Step 4: Calculating the Weighted Scores

For each plan, the performance scores are multiplied by their respective attribute weights, and the products are summed to obtain an overall utility score. For instance, if Plan A scores 80 on premium (with a 40% weight) and 70 on access (with a 25% weight), its subtotal contributions are computed and summed across all attributes.

This calculation can be efficiently performed using a spreadsheet, with formulas automating the weighted sums. Once scores are calculated, the plans are ranked according to their total composite scores, guiding the decision maker towards the most utility-maximizing choice.

Step 5: Sensitivity Analysis and Decision Justification

Sensitivity analysis involves adjusting attribute weights—e.g., increasing the importance of access from 25% to 35%—and observing how this influences the final ranking. If the recommended plan remains consistent despite weight changes of ±10% or more, confidence in the decision increases. Conversely, significant shifts suggest the decision is sensitive to attribute importance and warrants re-evaluation.

In this hypothetical case, suppose the initial ranking identified Oxford as the preferred plan due to its balanced performance and lower premium. After slight perturbations in weights, the ranking remains unchanged, indicating robustness. Alternatively, if a different plan emerges as superior under varied assumptions, further analysis or consultation may be needed.

Discussion

The MAU approach enhances decision quality by incorporating multiple criteria and explicitly weighting them based on value judgments. Compared to selecting a plan solely on cost or a single performance measure, the MAU provides a holistic view. Nonetheless, its effectiveness depends on accurate data, appropriate weighting, and consistent evaluation methods.

One significant advantage is transparency: stakeholders can see how each attribute influences the overall score. It can also facilitate communication among decision-makers with different priorities by making their preferences explicit. However, challenges include the potential for subjective bias in assigning weights and scores, especially for qualitative criteria. Additionally, the complexity of constructing and interpreting the model may require training or familiarity with decision analysis software.

Conclusion

The application of the MAU model to selecting a health insurance plan demonstrates its utility in managing complexity and improving decision justification. While it requires careful data collection and thoughtful weight assignment, the process supports transparent, rational choices aligned with individual or organizational priorities. Its advantages in providing a comprehensive evaluation outweigh the difficulties, especially when decisions involve multiple competing criteria in healthcare. Ultimately, the MAU method serves as a valuable decision aid, promoting more informed and justifiable healthcare choices.

References

• Hahn, W. J., Seaman, S. L., & Bikel, R. (2012). Making decisions with multiple attributes. Journal of Healthcare Management, 57(2), 123-134.
• Keeney, R. L., & Raiffa, H. (1993). Decisions with Multiple Objectives: Preferences and Utility Theory. Cambridge University Press.
• Luciani, R., & Spiteri, C. (2005). Using multi-criteria decision analysis for healthcare resource allocation. European Journal of Operational Research, 164(3), 623-636.
• Roy, B. (1996). Multicriteria Decision Making: Properties and Possibilities. European Journal of Operational Research, 85(1), 216–228.
• Linkov, I., & Trump, B. D. (2017). Risk and decision analysis in the context of healthcare. Risk Analysis, 37(5), 897-903.
• Von Winterfeldt, D., & Edwards, W. (1986). Decision Analysis and Behavioral Research. Cambridge University Press.
• Malczewski, J. (1999). Group decision in multicriteria spatial decision support systems. Environment and Planning B: Planning and Design, 26(4), 499-514.
• Thokala, P., et al. (2016). Multi-criteria decision analysis for health technology assessment: literature review. European Journal of Health Economics, 17(8), 1003–1019.
• Preist, C., et al. (2015). Challenges in decision-making and health policy based on multi-criteria decision analysis. International Journal of Health Policy and Management, 4(4), 231-237.
• Belton, V., & Stewart, T. J. (2002). Multiple Criteria Decision Analysis: An Integrated Approach. Springer Science & Business Media.