UCSF

TRANSPERS Program on Quantifying the Value of Precision Medicine

A major focus of the TRANSPERS Center is quantifying the value of precision medicine-related technologies and the implications of adoption. An evidence-based economic evaluation can help decision-makers develop coverage policies, program designs, and quality initiatives focused on optimizing health given available treatment options.

Most recently, our cost-effectiveness research has extended to incorporate health disparities to better understand the potential impact of novel technologies on inequities in health outcomes. Distributional cost-effectiveness analysis can help decision-makers prepare for efficient and equitable use with the ultimate goal of improving both population health and reducing health outcome inequities.

Our applied research focuses on health technologies related to screening, diagnosis, and treatment in a variety of diseases. Methods we use include:

  • Evidence synthesis
  • Observational data analysis
  • Distributional cost-effectiveness analysis
  • Stakeholder engagement

Informed by the challenges in applied research projects, we also contribute to methods development to improve approaches to quantify value.

PDF iconPrintable Summary: Quantifying Value (PDF)

Accomplishments

Our publications include studies on health economic simulation modeling and decision analysis, health equity impact evaluation of new treatments, value assessment and decision-making, and value and affordability in Precision Medicine. Our work has been widely cited and used by researchers, payers, industry, organizations, and clinicians. We are continuing to work on quantifying the value of novel genomic technologies as they emerge. Our 2022 publication in Pharmacoeconomics suggests that value assessments of new interventions should include health equity impact. Additional Publications include:

  1. Phillips KA. Introduction to Themed Papers: Methods for Moving Evaluation of Precision Medicine into Practice and Policy. Value Health. 2020;23(5):527-528. https://pubmed.ncbi.nlm.nih.gov/32389216
  2. Marshall DA, Grazziotin L, Regier DA, Wordsworth S, Buchanan J, Phillips KA, Ijzerman M. Addressing Challenges of Economic Evaluation in Precision Medicine Using Dynamic Simulation Modeling. Value Health. 2020;23(5):566-573. https://pubmed.ncbi.nlm.nih.gov/32389221/
  3. Phillips KA. Assessing the Value of Next-Generation Sequencing Technologies: An Introduction. Value Health. 2018; 21(9):1031-1032. https://www.ncbi.nlm.nih.gov/pubmed/30224105
  4. Phillips KA, Deverka PA, Marshall DA, Wordsworth S, Regier DA, Christensen KD, Buchanan J. Methodological Challenges and Solutions for Assessing Economic Value of Next Generation Sequencing Tests. Value Health. 2018; 21(9):1033-1042. https://www.ncbi.nlm.nih.gov/pubmed/30224106
  5. Wordsworth S, Doble B, Payne K, Buchanan J, Marshall DA, McCabe C, Regier DA. Using ‘big’ data in the cost-effectiveness analysis of genomic-based diagnostic tests: challenges and potential solutions. Value Health. 2018; 21(9):1048-1053. https://www.ncbi.nlm.nih.gov/pubmed/30224108
  6. Reiger DA, Weymann D, Buchanan J, Marshall DA, Wordsworth S. Valuation of health and non-health Outcomes from Next-Generation Sequencing: Approaches, Challenges, and Solutions. Value Health 2018; 21(9):1043-1047. https://www.ncbi.nlm.nih.gov/pubmed/30224107
  7. Christensen KD, Phillips KA, Green RC, Dukhovny D. Cost Analyses of Genomic Sequencing – Lessons Learned from the MedSeq Project. Value Health. 2018; 21(9):1054-1061. https://www.ncbi.nlm.nih.gov/pubmed/30224109
  8. Marshall DA, Gonzalez JM, MacDonald KV, et al. Estimating Preferences for Complex Health Technologies: Lessons Learned and Implications for Personalized Medicine. Value Health. 2017;20(1): 32-39. (PMCID: PMC5319756) https://www.ncbi.nlm.nih.gov/pubmed/28212966
  9. Phillips KA, Douglas MP, Trosman JR, et al. "What Goes Around Comes Around": Lessons Learned from Economic Evaluations of Personalized Medicine Applied to Digital Medicine. Value Health. 2017;20(1): 47-53. (PMCID: PMC5319740) https://www.ncbi.nlm.nih.gov/pubmed/28212968
  10. Douglas MP, Ladabaum U, Pletcher MJ, Marshall DA, Phillips KA. Economic evidence on identifying clinically actionable findings with whole-genome sequencing: a scoping review. Genet Med. 2016 Feb;18(2):111-6. (PMCID: PMC4654986) https://www.ncbi.nlm.nih.gov/pubmed/25996638
  11. Phillips KA, Ladabaum U, Pletcher MJ, Marshall DA, Douglas MP. Key emerging themes for assessing the cost-effectiveness of reporting incidental findings. Genet Med. 2015. 17(4):314-5. (PMCID: PMC4395812) https://www.ncbi.nlm.nih.gov/pubmed/25835195
  12. Phillips, KA; Sakowski JA; Trosman, J; Douglas, MP; Liang S; and Neumann, P. The Economic Value of Personalized Medicine: What We Know and What We Need to Know. Genet Med. 2014. Mar;16(3):251-7. (PMCID: PMC3949119) https://www.ncbi.nlm.nih.gov/pubmed/24232413
  13. Phillips, K. A., J. S. Sakowski, S. Y. Liang and N. A. Ponce (2013). "Economic Perspectives on Personalized Health Care and Prevention. ." Forum for Health Economics and Policy. 16(2): S23-S52. http://www.degruyter.com/view/j/fhep.2013.16.issue-2/fhep-2013-0010/fhep-2013-0010.xml?rskey=dVaq6A&result=4
  14. Wang, G., M. Kuppermann, B. Kim, K. A. Phillips and U. Ladabaum (2012). "Influence of patient preferences on the cost-effectiveness of screening for Lynch syndrome." Am J Manag Care 18(5): e179-185. http://www.ncbi.nlm.nih.gov/pubmed/22694112
  15. Wang, G., M. Kuppermann, B. Kim, K. A. Phillips and U. Ladabaum (2012). "Influence of patient preferences on the cost-effectiveness of screening for lynch syndrome." J Oncol Pract 8(3 Suppl): e24s-30s. PMCID: 3348599. http://www.ncbi.nlm.nih.gov/pubmed/22942831
  16. Johnson, F. R., A. F. Mohamed, S. Ozdemir, D. A. Marshall and K. A. Phillips (2011). "How does cost matter in health-care discrete-choice experiments?" Health Econ 20(3): 323-330. PMCID: 3918954. http://www.ncbi.nlm.nih.gov/pubmed/20217834
  17. Ferrusi, I. L., N. B. Leighl, N. A. Kulin and D. A. Marshall (2011). "Do economic evaluations of targeted therapy provide support for decision makers?" Am J Manag Care 17 Suppl 5 Developing: SP61-70. PMCID: 3918963. http://www.ncbi.nlm.nih.gov/pubmed/21711079
  18. Ferrusi, I. L., N. B. Leighl, N. A. Kulin and D. A. Marshall (2011). "Do economic evaluations of targeted therapy provide support for decision makers?" J Oncol Pract 7(3 Suppl): 36s-45s. PMCID: 3092467. http://www.ncbi.nlm.nih.gov/pubmed/21886518
  19. Elkin, E. B., D. A. Marshall, N. A. Kulin, I. L. Ferrusi, M. J. Hassett, U. Ladabaum and K. A. Phillips (2011). "Economic evaluation of targeted cancer interventions: critical review and recommendations." Genet Med 13(10): 853-860. PMCID: 3774033. http://www.ncbi.nlm.nih.gov/pubmed/21637102
  20. Phillips, K. A., D. A. Marshall, J. S. Haas, E. B. Elkin, S. Y. Liang, M. J. Hassett, I. Ferrusi, J. E. Brock and S. L. Van Bebber (2009). "Clinical practice patterns and cost effectiveness of human epidermal growth receptor 2 testing strategies in breast cancer patients." Cancer 115(22): 5166-5174. PMCID: 2783254. http://www.ncbi.nlm.nih.gov/pubmed/19753618
  21. Marshall, D. A. and M. Hux (2009). "Design and analysis issues for economic analysis alongside clinical trials." Med Care 47(7 Suppl 1): S14-20. http://www.ncbi.nlm.nih.gov/pubmed/19536012
  22. Ferrusi, I. L., D. A. Marshall, N. A. Kulin, N. B. Leighl and K. A. Phillips (2009). "Looking back at 10 years of trastuzumab therapy: what is the role of HER2 testing? A systematic review of health economic analyses." Per Med 6(2): 193-215. PMCID: 2910630. http://www.ncbi.nlm.nih.gov/pubmed/20668661
  23. Phillips, K. A., S. Y. Liang and S. Van Bebber (2008). "Challenges to the translation of genomic information into clinical practice and health policy: Utilization, preferences and economic value." Curr Opin Mol Ther 10(3): 260-266. PMCID: 2910510. http://www.ncbi.nlm.nih.gov/pubmed/18535933
  24. Payne, K., W. G. Newman, D. Gurwitz, D. Ibarreta and K. A. Phillips (2008). "TPMT testing in azathioprine: a ‘cost-effective use of healthcare resources’?" Personalized Medicine 6(1): 103-113. http://www.futuremedicine.com/doi/full/10.2217/17410541.6.1.103