Development of the PICCOTEAM Reference Case for Economic Evaluation of Precision Medicine
Background: Current economic evaluations (EEs) of precision medicine (PM) often adhere to generic reference cases (RC) which overlook the unique healthcare paradigms of PM. This study aimed to develop an RC to standardize the conduct and reporting of EEs of PM. Methods: A working group comprising 5 core health economists, 22 PM experts, and research staff from Singapore, Thailand, and Australia who were actively engaged in EE and clinical PM implementation. The RC development comprised four stages: (1) Expert consultation shaping the RC’s scope and structure across nine domains: Population, Intervention, Comparator, Cost, Outcome, Time, Equity and ethics, Adaptability, Modelling (i.e., "PICCOTEAM" framework); (2) A comprehensive literature review on current PM EE approaches and challenges; (3) Obtaining expert consensus and drafting recommendations; (4) A workshop for RC refinement based on stakeholder feedback on relevance and feasibility. Following an experts' workshop, consensus was reached to tailor PM recommendations for screening, diagnosis, and pharmacogenomics, market-access, and early EEs. Results: The PICCOTEAM RC offers 46 recommendations for conventional EEs to guide PM reimbursement, emphasizing expert engagement, iterative study processes, disease-specific outcomes, decision uncertainty analyses, and equity considerations. Additionally, 30 recommendations are provided for early-stage evaluation to enhance PM’s positioning and value proposition, mitigating uncertainty, equity, and ethical issues. Conclusion: The PICCOTEAM RC offers a standardized process to conduct and report diverse PM EEs. This will serve as guidance for health departments, researchers, clinicians, editors, and reviewers. Pilot testing and continuous updates are recommended for ongoing relevance and applicability of this RC.
Authors
- Dimple Butani
- Wenjia Chen (ORCID: https://orcid.org/0000-0001-8201-7145)
- Alec Morton (ORCID: https://orcid.org/0000-0003-3803-8517)
- Vorasuk Shotelersuk (ORCID: https://orcid.org/0000-0002-1856-0589)
- Janet Bouttell (ORCID: https://orcid.org/0000-0002-4568-5483)
- Laura Huey Mien Lim (ORCID: https://orcid.org/0009-0005-5384-8632)
- Paul Scuffham (ORCID: https://orcid.org/0000-0001-5931-642X)
- Yi Wang (ORCID: https://orcid.org/0000-0003-1934-9926)
- Yot Teerawattananon (ORCID: https://orcid.org/0000-0003-2217-2930)
- Hwee Lin Wee (ORCID: https://orcid.org/0000-0002-7150-1801)
- Joanne Ngeow (ORCID: https://orcid.org/0000-0003-1558-3627)
- Janneke P.C. Grutters (ORCID: https://orcid.org/0000-0002-2579-1561)
- Yah Ru Juang
- Yue Zhang (ORCID: https://orcid.org/0000-0002-4124-4816)
Institutions
- Griffith University (AU)
- Thai Red Cross Society (TH)
- Ministry of Public Health (TH)
- Nottingham University Hospitals NHS Trust (GB)
- Radboud University Nijmegen (NL)
- Chulalongkorn University (TH)
- National University of Singapore (SG)
- Nanyang Technological University (SG)
- University of Strathclyde (GB)
- Health Intervention and Technology Assessment Program (TH)
- King Chulalongkorn Memorial Hospital (TH)
- Radboud University Medical Center (NL)
- Duke-NUS Medical School (SG)
- National Cancer Centre Singapore (SG)
- National University Health System (SG)
- University of Glasgow (GB)
Publication Details
- Journal
- International Journal of Health Policy and Management
- Published
- 2025-08-04
- DOI
- https://doi.org/10.34172/ijhpm.8756
- Primary Topic
- Artificial Intelligence in Healthcare and Education
- Type
- article
- Field-Weighted Citation Impact
- 0.00
Funders
- Health Systems Research Institute