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Expanding Use of Multi-Criteria Decision Analysis for Health Technology Assessment

Expanding Use of Multi-Criteria Decision Analysis for Health Technology Assessment

Various voting methods differ greatly in expressivity. Six well-known ballot types allow creation of rank order lists, essential in many MCDA processes. The Table shows the general formulas and the number of different possible expressions allowable for 5 candidates with 6 different voting methods. Huge differences in potential expressiveness are obvious. Among these, cumulative voting and range voting allow direct determination of value weights in group voting settings.

For comparison, 6-month-old infants understand about 6 words, and 18-month-old infants about 50. Dogs’ word comprehension ranges between 150 and 400 words. Six-year-old children master about 2500 words and average adults about 20,000 to 35,000 words. Simple considerations suggest using majority judgment, cumulative voting, or range voting when detailed understanding of voters’ preferences is desirable. Common ballot forms “dumb down” vocabularies to those of infants or dogs.

CONCLUDING COMMENTS

Expanding the understanding and measurement of value beyond CEA requires a systems analysis perspective. Control of complex systems requires the ability to measure and combine information in new ways, a task for which MCDA is ideally suited.11

Getting “there” requires systematic accumulation of data that do not currently exist.12 Data-gathering efforts will have better focus if consensus emerges regarding core attributes for MCDA model use.13 Software to support MCDA must become much friendlier for group decision making than current offerings, most importantly by introducing easy methods to elicit group preferences. Similar efforts to simplify methods for individual patient use are central to improved adoption of MCDA in clinical settings for truly “personalized” medicine. Human factors loom large in these issues. Graduate programs training new healthcare professionals should expand cost-effectiveness and decision analysis courses to include MCDA methods.14

Ultimately, MCDA use will expand with improved usability and familiarity. People resist new ideas even when old ones are insufficient. Buckminster Fuller noted, “You never change things by fighting against the existing reality. To change something, build a new model that makes the old model obsolete.” Therein lies the challenge. We cannot wait.

Dr Phelps is provost emeritus of the University of Rochester.
Acknowledgment: Dr Phelps acknowledges the extensive contributions of Guru Madhavan, PhD, Norman R. Augustine Senior Scholar and director of programs at the National Academy of Engineering, through previous work that helped build the foundation for this analysis.
 
1. Garber AM, Phelps CE. Economic foundations of cost-effectiveness analysis. J Health Econ. 1997;16(1):1-31. doi: 10.1016/s0167-6296(96)00506-1.
2. Phelps C, Madhavan G, Rappuoli R, Colwell R, Fineberg H. Beyond cost-effectiveness: using systems analysis for infectious disease preparedness. Vaccine. 2017;35(suppl 1):A46-A49. doi: 10.1016/j.vaccine.2016.08.090.
3. Lakdawalla DN, Doshi JA, Garrison LP Jr, Phelps CE, Basu A, Danzon PM. Defining elements of value in health care—a health economics approach: an ISPOR Special Task Force report [3]. Value Health. 2018;21(2):131-139. doi: 10.1016/j.jval.2017.12.007.
4. Köksalan M, Wallenius J, Zionts S. Multiple Criteria Decision Making: From Early History to the 21st Century. Singapore: World Scientific; 2011.
5. Software related to MCDM. International Society on Multiple Criteria Decision Making website. mcdmsociety.org/content/software-related-mcdm. Accessed April 20, 2019.
6. Phelps CE, Madhavan G. Using multicriteria approaches to assess the value of health care. Value Health. 2017;20(2):251-255. doi: 10.1016/j.jval.2016.11.011.
7. Kahneman D. Thinking, Fast and Slow. New York, NY: Farrar, Straus and Giroux; 2011.
8. Phelps CE, Madhavan G. Valuing health: evolution, revolution, resistance, and reform. Value Health. 2019;22(5):505-510. doi: 10.1016/j.jval.2019.01.010.
9. Dolan JG. The potential impact of a decision support system based on the analytical hierarchy process (AHP) on physicians decisions regarding antibiotic-treatment. Clin Res. 1988;36(3):A710.
10. Phelps C, Madhavan G. Resource allocation in decision support frameworks. Cost Eff Resour Alloc. 2018;16(suppl 1):48. doi: 10.1186/s12962-018-0128-5.
11. Madhavan G, Phelps CE, Rouse WB, Rappuoli R. Vision for a systems architecture to integrate and transform population health. Proc Nat Acad Sci U S A. 2018;115(50):12595-12602. doi: 10.1073/pnas.1809919115.
12. Madhavan G, Phelps C, Sangha K, Levin S, Rappuoli R. Bridging the gap: need for a data repository to support vaccine prioritization efforts. Vaccine. 2015;33(suppl 2):B34-B39. doi: 10.1016/j.vaccine.2015.02.032.
13. Garrison LP Jr, Neumann PJ, Willke RJ, et al. A health economics approach to US value assessment frameworks—summary and recommendations of the ISPOR Special Task Force report [7]. Value Health. 2018;21(2):161-165. doi: 10.1016/j.jval.2017.12.009.
14. Phelps C, Madhavan G, Rappuoli R, Levin S, Shortliffe E, Colwell R. Strategic planning in population health and public health practice: a call to action for higher education. Milbank Q. 2016;94(1):109-125. doi: 10.1111/1468-0009.12182.
 
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