New reimbursement models to promote better patient outcomes and overall value in laboratory medicine and healthcare
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Tommaso Trenti
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Anna Maria Petrini
Abstract
The most widespread healthcare reimbursement models, including diagnostic laboratory services, are Fee-for-Service, Reference Pricing and Diagnosis-Related Groups. Within these models healthcare providers are remunerated for each specific service or procedure they operate. Healthcare payers are increasingly exploring alternative models, such as bundled payments or value-based reimbursement to encourage value of patient care rather than the simple amount of delivered services. These alternative models are advised, as they are more efficient in promoting cost-effective, high-quality laboratory testing, thereby improving patient health outcomes. If outcomes-based evaluation is a pillar in a new vision of “Value-Based Healthcare”, an active policy of Value-Based Reimbursement in laboratory medicine will assure both an efficiency-based sustainability and a high-quality effectiveness-based diagnostic activity. This review aims to evaluate current and alternative reimbursement models, to support a wider agenda in encouraging more Value-Based Healthcare and Value-Based Reimbursement in laboratory medicine.
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Research ethics: Not applicable.
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Informed consent: Not applicable.
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Author contributions: The authors have accepted responsibility for the entire content of this manuscript and approved its submission.
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Competing interests: The authors state no conflict of interest.
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Research funding: None declared.
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Data availability: Not applicable.
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© 2024 Walter de Gruyter GmbH, Berlin/Boston
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Articles in the same Issue
- Frontmatter
- Editorials
- EFLM European Urinalysis Guideline
- Clinical Chemistry Laboratory Medicine in the post-acute COVID-19 era
- EFLM Guideline
- The EFLM European Urinalysis Guideline 2023
- Review
- Approaching sustainability in Laboratory Medicine
- Opinion Papers
- New reimbursement models to promote better patient outcomes and overall value in laboratory medicine and healthcare
- Screening for sickle cell disease: focus on newborn investigations
- Genetics and Molecular Diagnostics
- The role of Killer immunoglobulin-like receptors (KIRs) in the genetic susceptibility to non-celiac wheat sensitivity (NCWS)
- General Clinical Chemistry and Laboratory Medicine
- Understanding the limitations of your assay using EQA data with serum creatinine as an example
- Add-on testing: stability assessment of 63 biochemical analytes in centrifuged and capped samples stored at 16 °C
- Smartphone swabs as an emerging tool for toxicology testing: a proof-of-concept study in a nightclub
- Allergy: Evaluation of 16 years (2007–2022) results of the shared external quality assessment program in Belgium, Finland, Portugal and The Netherlands
- Cancer Diagnostics
- Monoclonal whole IgG impairs both fibrin and thrombin formation: hemostasis and surface plasmon resonance studies
- Cardiovascular Diseases
- Patients with antiphospholipid syndrome and a first venous or arterial thrombotic event: clinical characteristics, antibody profiles and estimate of the risk of recurrence
- Letters to the Editor
- Familial dysalbuminemic hyperthyroxinemia coexisting with a Grave’s disease: a Belgian case report
- Diagnostic challenge between a frequent polygenic hypocholesterolemia and an unusual Smith Lemli Opitz syndrome related to bi-allelic DHCR7 mutations
- First reported co-occurrence of Philadelphia chromosome-positive B-cell acute lymphoblastic leukemia with pseudo Chediak-Higashi anomaly and complex karyotype
- Facing the new IVD Regulation 2017/746: Contract Research Organizations (CROs), key partners of IVDs manufacturers for compliance
- Comprehensive analysis and clinical case studies on pseudoeosinophilia: insights and implications – unraveling the complexity: analytical approaches and clinical significance
- Misdiagnosis of type 2B von Willebrand disease as immune thrombocytopenia in a thrombocytopenic patient
- Biological matrices, reagents and turnaround-time: the full-circle of artificial intelligence in the pre-analytical Phase. Comment on Turcic A, et al., Machine learning to optimize cerebrospinal fluid dilution for analysis of MRZH reaction. CCLM 2024;62:436–41
