Clinical Benefit Assessment vs Cost-Effectiveness in Healthcare Assignment

Introduction

This assessment aims to conduct a comparative analysis between clinical benefit assessment and clinical cost-effectiveness and critically analyse their implication for coverages. Comparative clinical benefit assessment can be defined as a model that ranks new interventions based on their comparative benefits. It is an important HTA or Health Technology Assessment model, which signifies the healthcare system's objectives and priorities. On the other hand, clinical cost-effectiveness analysis or CEA is a method that compares the cost and effectiveness of different medical interventions to help decide which care to offer to service users. Therefore, comparative clinical benefit assessment and clinical cost-effectiveness analysis are considered as two critical frameworks in health technology assessment or ATA which guide decision-making regarding healthcare intervention. Both frameworks are important to ensure quality-adjusted life years of the service users. Understanding the differences and implications of these two frameworks is important for policymakers and payers as they influence coverage decisions and resource allocation within the healthcare system.

Discussion

Comparative Clinical Benefit Assessment

 Comparative Clinical Benefit Assessment or CCBA is a process used to evaluate the effectiveness of new medical treatments and technologies compared to existing ones. This assessment is used to focus on the clinical outcomes and benefits of the new intervention relative to current standard treatments. The main purpose of this assessment is to determine whether a new treatment can offer significant clinical advantages over existing options. This comparison includes improvement in survival rates, quality of life, and reduction of symptoms. According to Sertkaya & Franz, (2022), comparative clinical benefit assessment primarily examines the relative effectiveness of new treatment compared to existing therapies, emphasizing clinical outcomes such as survival rates, quality of life, and overall patient benefits. This assessment focuses on measuring clinical effectiveness, which refers to how well a treatment works in real-world settings. It considers various outcomes including mortality rates, morbidity, adverse events, and overall quality of life. According to Hoffman et al. (2015), this assessment often includes randomized controlled trials or RCTs, meta-analysis and real-world evidence to compare the interventions head-to-head. For example, to evaluate new cancer drugs, through this assessment, the care professionals can measure the effectiveness of the drug in terms of its overall survival rates, progression-free survival rates pr PFS, and reduction rate of tumour size to ensure the benefits. As per the article by Cherny et al. (2019), before marketing approval, the clinical benefits of a drug used to be evaluated by using clinical trials designed to demonstrate superiority, which means to check whether the drug is better than the standard therapy or not rather than one designed to demonstrate non-inferiority (i.e. the drug is not worse than the standard therapy). For any drugs, superiority trials need large patient enrollment which makes those drugs more costly. As per this article, in some cases, such as AM drugs designed for treating highly resistant infection, superiority trials seem to be unethical to conduct due to the lack of its users. Another prime example of CBA is in the field of oncology, where new immunotherapeutic drugs are compared to existing chemotherapy regimens (Bento, 2019). For example, nivolumab, an immune checkpoint inhibitor was assessed for its clinical benefits compared to docetaxel in patients with non0small cell lung cancer (NSCLC). Clinical trials demonstrate that nivolumab can improve the overall survival rate and have fewer adverse impacts in comparison to chemotherapy. The Comparative Clinical Benefit Assessment thus highlight the superior clinical outcomes of nivolumab and justifies its adoption as a preferred treatment option from the clinical perception. According to (), Clinical Benefit Assessment plays a critical role in guiding coverage decisions by providing robust evidence of clinical superiority (Board on Health Sciences Policy, & Committee on Strategies for Responsible Sharing of Clinical Trial Data, 2015). For example, pharmaceutical Benefit Decisions can be included in this discussion. In Germany, the Federal Joint Committee or G-BA uses Clinical Benefit Assessment to identify the added clinical benefits of newly designed drugs over existing therapies as a part of the AMNOG process. A positive clinical benefit leads to price negotiations with insurers, often facilitating broader patient access (Dungan & Verma, 2023). On the other hand, in the United Kingdom, the National Institute for Health and Care Excellence or NICE evaluates devices like continuous glucose monitoring system or CGMS which completely depends on clinical outcomes such as providing information about reduced hypoglycemic events in diabetes management (NHS, UK, 2023). The methodology typically includes conducting systematic reviews and meta-analyses of clinical trials to determine whether newly designed drugs or treatments can offer significant benefits in comparison to current standard therapies. For example, if a newly designed drug for diabetes management is introduced, comparative clinical benefit assessment would involve the evaluation of its efficiency against established treatments like metamorphic or insulin. Another notable example of using Clinical Benefit Assessment in the clinical trial method is CAR-T cell therapies for certain types of cancer (Jogalekar et al., 2022). These therapies have shown significant improvement in patient outcomes compared to the traditional chemotherapy. The Institute for Clinical and Economic Review or ICER evaluates these treatments by comparing their effectiveness in extending survival rates and improving the quality of life against existing options. This type of assessment can help in determining whether the new therapy can offer enough benefits to warrant its use in clinical practices. The findings from comparative clinical benefit assessment can directly influence the coverage decision made by insurers and healthcare authorities. If a new treatment is deemed to offer substantial added benefits over existing therapies, it may receive favourable coverage decisions, leading to broader access for patients. conversely, if the evidence suggests that a treatment does not provide any significant improvement, it may be denied coverage. Comparative Clinical Benefit Assessment or CBCA plays an important role in shaping the healthcare coverage decision by evaluating the relative efficacy, safety, and cost-effectiveness of medical intervention. By systematically comparing the new treatments to existing standards of care, CBCA provides evidence-based insights that can guide policymakers and insurers in determining reimbursement policies (Hoffman et al., 2015). High-value interventions that demonstrate significant clinical benefits like improved survival rate, and improvement of quality of life seem to receive coverages to enhance the patient access towards innovative and productive therapies, and medications. However, treatments with limited or marginal benefits can face restricted coverages, which can raise ethical concerns about access for patients with unmet medical needs. Furthermore, the Comparative Clinical Benefit Assessment ensures efficient resource allocation in the healthcare system balancing innovation with affordability, through challenges such as variability in assessment frameworks.

Clinical Cost Effectiveness

Clinical Cost Effectiveness is a method for comparing the costs and outcomes of different medical treatments that help in deciding which one can offer. More specifically, the purpose of Clinical Cost Effectiveness is to determine if the value of the treatment justifies its cost. According to an article by Kinchin et al. (2023), Clinical Cost Effectiveness refers to the evaluation of the costs and health outcomes of medical interventions to determine the best use of limited healthcare resources. This analysis can help in making informed decisions about which treatment can provide the most benefit for their cost. Therefore, it can be stated that Clinical Cost-Effectiveness is a fundamental principle in modern healthcare, ensuring that resources are allocated to interventions that can provide the greatest health benefits relative to their costs. As the healthcare system faces increased financial pressure due to the ageing population, technological advancements, and the rising cost of healthcare services, and treatment processes, evaluation of the cost-effectiveness of clinical intervention has now become essential for sustainable decision-making. Clinical cost-effectiveness evaluates the value of a healthcare intervention by comparing the treatment cost or the cost of any healthcare facility to the expected health outcomes (Stenberg et al., 2021). This comparison often uses measurements like QALYs or quality-adjusted life years or disability-adjusted life years or DALYs to quantify the benefits of treatment in terms of both quantity and quality of life. The cost-effectiveness of an intervention is typically expressed as an incremental cost-effectiveness ratio which calculated are:

If the Incremental cost-effectiveness ratio or ICER falls under the predetermined willingness-to-pay threshold, the intervention is considered as cost-effective (Pron et al., 2023). These thresholds vary across countries, reflected in differences between social values and healthcare budgets. The calculation of clinical cost-effectiveness includes cost-effectiveness analysis or CEA which compares the relative cost and outcomes or effects at different courses of action. It uses metrics like the Incremental Cost-Effectiveness Ratio or ICER which represents the cost per additional unit of health benefit, including Quality-Adjusted Life Year or QALY. Another measurement method includes Quality Adjusted Life Year or QALY which combines the life expectancy with the quality of life during those years (Carlson et al., 2020). It is used to assess the value for money of a medical intervention. Organisations like NICE or the National Institute for Health and Care Excellence use cost-effectiveness analysis to develop clinical guidelines. With these guidelines, NICE can ensure that healthcare resources are being used optimally to maximize the health outcomes and their benefits. There are several methodologies that are being employed to assess clinical cost-effectiveness, for example, CMA or Coost-Minimisation Analysis, which is used when two or more interventions yield equivalent outcomes focusing solely on cost comparison (Tonin et al., 2021). On the other hand, another methodology is Cost-Utility Analysis or CUA, which is a subset of CEA that incorporates patient preferences and quality of life into the assessment (Brent, 2023). Similarly, CBA or Cost-Benefit Analysis is another methodology that expresses both the costs and benefits in monetary terms, facilitating direct comparison across the sectors. Cost-effectiveness Analysis or CEA informs a wide range of healthcare decisions, from reimbursement policies and drug pricing to public health intervention and set up of clinical guidelines (Strand et al., 2022). For example, in the pharmaceutical field, cost-effectiveness studies guide decisions on whether to include new drugs in national formularies. In the oncology field, where treatments are often expensive, cost-effectiveness analysis can help in prioritising therapies with substantial survival benefits and improved quality of life. Vaccination campaigns, smoking cessation programs, and screening initiatives are often assessed for cost-effectiveness to ensure the optimal allocation of resources. For instance, cost-effectiveness evaluations have demonstrated the value of the HPV vaccine in preventing cervical cancer (Kinchin et al., 2023). Professional organisations and healthcare authorities use cost-effectiveness evidence to develop clinical guidelines, ensuring that recommended treatments can provide good value for money to their service users. However, despite the importance of cost-effectiveness, assessing clinical cost-effectiveness presents several challenges. In cost-effectiveness analysis, data limitation is considered as one of the challenges, reliable cost and outcome data are often unavailable, particularly for the new intervention or in the case of low-resource settings (Werner, 2021). Ethical consideration is also considered as another challenge related to cost-effectiveness. The decision based on cost-effectiveness sometimes coincides with ethical principles, like equity and access. For example, treatments for rare disorders often have high ICERs, potentially limiting access for vulnerable populations. Differences in willingness-to-pay thresholds across the countries complicate the generalizability of cost-effectiveness findings. Lastly, the traditional cost-effectiveness analysis may not sometimes capture the patient preferences and social values, which necessitates inclusive approaches (Avanceña & Prosser, 2021). The integration of cost-effectiveness into healthcare policy has profound implications for resource allocation, access to care, and systematic sustainability. Transparent cost-effectiveness thresholds incentivize pharmaceutical companies and device manufacturers to develop interventions that deliver meaningful health benefits (Frech III et al., 2022). In low-and middle-income countries, cost-effectiveness analysis is considered to be valuable for optimizing limited resources, guiding investments in essential healthcare services, and improving population health outcomes. To measure the clinical Cost-Effectiveness Analysis, this approach typically includes calculating the incremental cost-effectiveness ratio or ICER which compares the difference in costs between two interventions divided by the difference in their effectiveness (often measured in QALYs). The formula is:

ICER= (C1-C0)/ (E1-E0), where, C1 represents the cost of new intervention, and C0 represents the cost of comparator. The E1 in this formulation refers to the effectiveness of the new intervention, whereas E0 refers to the effectiveness of the comparator.

To carry out a discussion on measuring the Clinical Cost-Effectiveness, focus can be given on the evaluation of new hepatitis C treatment like the use and implementation of sofosbuvir in comparison to older therapies. The cost-effectiveness analysis would assess not only the price of sofosbuvir but also its effectiveness in curing hepatitis C compared to the older medication (Boyer et al., 2022). Therefore, cost-effectiveness analysis plays an important role in health policy decisions regarding reimbursement. Payers often set thresholds for ICER values; if a new treatment’s ICER falls below this threshold, it may be approved for coverage. For example, many health systems consider an ICER below $50,000 per QALY gained as accepted for inclusion in formularies.

The below table will highlight the key differences between Comparative Clinical Benefits Assessment and Clinical Cost-Effectiveness Analysis or CEA in healthcare setup.

As a whole, comparative clinical benefit assessment or CBA and Clinical Cost-Effectiveness Analysis or CEA are integral yet distinct components of health technology assessment, which differ from each other in terms of their focus, methodology, and application in coverage decisions. Comparative Clinical Benefit Assessment or CBA primarily evaluates the clinical outcomes of the intervention and measures the effectiveness of new treatment interventions and newly designed drugs, their survival rate, effectiveness and safety level on service users, and improvement in patient quality of life (Vaduganathan et al., 2020). The measurement relies on metrics like mortality rates, progression-free survival, and symptom alleviation based on randomized controlled trial methods, and real-world evidence. In contrast, Clinical Effectiveness Assessment or CEA focuses on economic efficacy and assesses whether the cost of an intervention is proportional to the health benefits (Avanceña & Prosser, 2021). Using the incremental cost-effectiveness ratio or ICER, CEA often measures the outcomes in terms of Quality-Adjusted Life Years or QALYs. For example, the cost-effectiveness evaluation of SGLT1 inhibitors in diabetes management demonstrates its value by reducing cardiovascular complications within an affordable cost threshold (Sandhu & Zheng, 2024). The methodology for these two assessment processes is also different. The Cost Benefit Analysis prioritises the patient-centric, outcome-based evidence to meet the medical necessity, whereas the CEA or Cost-Effective Analysis integrates the cost data with clinical outcomes by using economic models (El Ojeil, 2024). These distinctions have significant implications for coverage: while CBA helps in prioritizing the treatment based on clinical superiority, the CEA ensures the effective allocation of healthcare resources. However, the CBA may favour costly interventions without considering the economic constraints, whereas the CEA might limit access to life-saving innovations that exceed the pre-defined thresholds. Together, these assessments provide a balanced framework for decision-making (Werner, 2021). CBA identifies interventions with the greatest potential for improving health outcomes, and Cost Effectiveness Analysis ensures that such interventions align with budgetary realities. Policymakers must use both tools synergistically to optimize patient care while maintaining economic sustainability.

Comparative Clinical Benefit Assessment or CBA evaluates the clinical outcomes of an intervention compared to existing alternatives, focusing on metrics like efficacy, safety, and quality of life (Brent, 2023). This assessment established whether the treatment can offer superior health benefits over existing, and available options. Therefore, Clinical Benefit Assessment can provide robust evidence regarding the clinical values of treatments. More specifically, insurers can use the findings from clinical benefit assessment for the identification of interventions that can improve health outcomes and ensure coverage for effective and innovative therapies. For example, in the case of the administration of a cancer drug, clinical benefit assessment can provide information about its significant survival benefits in comparison to existing treatments which in turn can approve the medical coverages for the access. Coverage policies often prioritise the treatments that can address unmet medical needs and can offer substantial clinical improvements. Clinical Benefit Assessment ensures that interventions deemed necessary for patient care are supported by the evidence. More specifically, treatments that fail to meet the set-up clinical benefit assessment might face restricted coverage and exclusion (Bertram et al., 2021). On the other hand, Cost-Effectiveness Analysis evaluates the balance between the cost of medical intervention and its health outcomes, typically measured as the cost per quality-adjusted life year or disability-adjusted life year (Carlson et al., 2020). Insurers and policymakers use the cost-effectiveness data to prioritise the funding for treatments that deliver significant health benefits relative to their costs. Interventions with a favourable cost-effectiveness ratio are more likely to be included in coverage plans, which ensure the effective use of limited healthcare resources.

Conclusion

To conclude this assessment, it can be stated that comparative clinical benefit assessment and clinical cost-effectiveness analysis are two essential frameworks in health technology assessment, which play distinct years complementary roles in healthcare decision-making. Comparative clinical benefit assessment focuses on evaluating the relative effectiveness of new interventions against existing treatments, emphasizing clinical outcomes like survival and quality of life. Such an approach can directly influence coverage decisions by highlighting the treatments that offer substantial clinical coverage. On the other hand, as per the discussion, it can be concluded that cost-effective analysis evaluates the economic effectiveness of intervention by comparing the cost of health outcomes guiding resource allocation and reimbursement policies based on values for money. Therefore, despite the differences both frameworks are important to measure the effectiveness of the treatment process in the medical or healthcare field.

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References

Avanceña, A. L., & Prosser, L. A. (2021). Examining equity effects of health interventions in cost-effectiveness analysis: a systematic review. Value in Health, 24(1), 136-143. https://www.sciencedirect.com/science/article/pii/S1098301520344545

Avanceña, A. L., & Prosser, L. A. (2021). Examining equity effects of health interventions in cost-effectiveness analysis: a systematic review. Value in Health, 24(1), 136-143.https://www.sciencedirect.com/science/article/pii/S1098301520344545

Bento, M. B. O. R. D. S. (2019). Systematic Review of Pharmacoeconomic Studies on Immuno-Oncology: Assessment of the cost-effectiveness of Immuno-oncology medicines used in the treatment of Advanced Melanoma.https://repositorio.ul.pt/handle/10451/43325

Bertram, M. Y., Lauer, J. A., Stenberg, K., & Edejer, T. T. T. (2021). Methods for the economic evaluation of health care interventions for priority setting in the health system: an update from WHO CHOICE. International Journal of Health Policy and Management, 10(11), 673.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278384/

Board on Health Sciences Policy, & Committee on Strategies for Responsible Sharing of Clinical Trial Data. (2015). Sharing clinical trial data: maximizing benefits, minimizing risk. National Academies Press.https://books.google.com/books?hl=en&lr=&id=GJ7_CAAAQBAJ&oi=fnd&pg=PT14&dq=+Clinical+Benefit+Assessment+plays+a+critical+role+in+guiding+coverage+decisions+by+providing+robust+evidence+of+clinical+superiority.&ots=-Eo5BRt15C&sig=tvXrHfnftiu1fJSuIsEkkSz09ac

Boyer, S., Baudoin, M., Nishimwe, M. L., Santos, M., Lemoine, M., Maradan, G., ... & Lacombe, K. (2022). Cost-utility analysis of four WHO-recommended sofosbuvir-based regimens for the treatment of chronic hepatitis C in sub-Saharan Africa. BMC Health Services Research, 22(1), 303.https://link.springer.com/article/10.1186/s12913-021-07289-0

Brent, R. (2023). Cost-Benefit Analysis Versus Cost-Effectiveness Analysis from a Societal Perspective in Healthcare. International Journal of Environmental Research and Public Health, 20(5), 4637. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10001534/

Brent, R. J. (2023). Cost-benefit analysis versus cost-effectiveness analysis from a societal perspective in Healthcare. International Journal of Environmental Research and Public Health, 20(5), 4637.https://www.mdpi.com/1660-4601/20/5/4637

Carlson, J. J., Brouwer, E. D., Kim, E., Wright, P., & McQueen, R. B. (2020). Alternative approaches to quality-adjusted life-year estimation within standard cost-effectiveness models: literature review, feasibility assessment, and impact evaluation. Value in Health, 23(12), 1523-1533.https://www.sciencedirect.com/science/article/pii/S1098301520343497

Carlson, J. J., Brouwer, E. D., Kim, E., Wright, P., & McQueen, R. B. (2020). Alternative approaches to quality-adjusted life-year estimation within standard cost-effectiveness models: literature review, feasibility assessment, and impact evaluation. Value in Health, 23(12), 1523-1533.https://www.sciencedirect.com/science/article/pii/S1098301520343497

Cherny, N. I., De Vries, E. G., Dafni, U., Garrett-Mayer, E., McKernin, S. E., Piccart, M., ... & Schilsky, R. L. (2019). Comparative assessment of clinical benefit using the ESMO-magnitude of clinical benefit scale version 1.1 and the ASCO value framework net health benefit score. Journal of Clinical Oncology, 37(4), 336-349.https://ascopubs.org/doi/abs/10.1200/JCO.18.00729

Dungan, K., & Verma, N. (2023, January 10). Monitoring Technologies – Continuous Glucose Monitoring, Mobile Technology, Biomarkers of Glycemic Control. Nih.gov; MDText.com, Inc. https://www.ncbi.nlm.nih.gov/books/NBK279046/

El Ojeil, R. (2024). Pioneering Value-Based Healthcare in MENA. Health Policy.https://marketaccesstoday.com/pioneering-value-based-healthcare-in-mena/

Frech III, H. E., Pauly, M. V., Comanor, W. S., & Martinez, J. R. (2022). Costs and benefits of branded drugs: insights from cost-effectiveness research. Journal of Benefit-Cost Analysis, 13(2), 166-181.https://www.cambridge.org/core/journals/journal-of-benefit-cost-analysis/article/costs-and-benefits-of-branded-drugs-insights-from-costeffectiveness-research/50B74320F4FC716FA2A470BDB510646E

Hoffman, A. S., Cambrosio, A., & Battista, R. N. (2015). Comparative Effectiveness Research in Health Technology Assessment. Springer EBooks, 1–38. https://doi.org/10.1007/978-1-4899-7586-7_5-1

Jogalekar, M. P., Rajendran, R. L., Khan, F., Dmello, C., Gangadaran, P., & Ahn, B.-C. (2022). CAR T-Cell-Based gene therapy for cancers: new perspectives, challenges, and clinical developments. Frontiers in Immunology, 13. https://doi.org/10.3389/fimmu.2022.925985

Kinchin, I., Edwards, L., Hosie, A., Agar, M., Mitchell, E., & Trepel, D. (2023). Cost‐effectiveness of clinical interventions for delirium: a systematic literature review of economic evaluations. Acta Psychiatrica Scandinavica, 147(5), 430-459.https://onlinelibrary.wiley.com/doi/abs/10.1111/acps.13457

Kinchin, I., Edwards, L., Hosie, A., Agar, M., Mitchell, E., & Trepel, D. (2023). Cost‐effectiveness of clinical interventions for delirium: a systematic literature review of economic evaluations. Acta Psychiatrica Scandinavica, 147(5), 430-459. https://onlinelibrary.wiley.com/doi/abs/10.1111/acps.13457

NHS, UK. (2023). A pan-London implementation document for continuous glucose sensors for adults with type 1 diabetes: written pathway. https://www.england.nhs.uk/london/wp-content/uploads/sites/8/2022/10/London-type-1-diabetes-CGM-access-written-pathway-LCEG-approved-Oct-2022-1.pdf

Pron, G., Hwang, M., Nasralla, M., Smith, R., Cheung, A., & Murphy, K. (2023). Cost-effectiveness and willing-to-pay thresholds for vertebral augmentation of osteoporotic vertebral fractures, what are they based on: a systematic review. BMJ open, 13(7), e062832.https://bmjopen.bmj.com/content/13/7/e062832.abstract

Sandhu, A. T., & Zheng, J. (2024). Sodium-Glucose Cotransporter Inhibitors in Heart Failure: Access, Economics, and Clinical Promise. Heart Failure, 12(9), 1611-1613. https://www.jacc.org/doi/full/10.1016/j.jchf.2024.05.011

Sertkaya, A., & Franz, C. (2022, December 16). EVALUATION OF COMPARATIVE ADDED CLINICAL BENEFIT. Nih.gov; Office of the Assistant Secretary for Planning and Evaluation (ASPE). https://www.ncbi.nlm.nih.gov/books/NBK603194/

Stenberg, K., Watts, R., Bertram, M. Y., Engesveen, K., Maliqi, B., Say, L., & Hutubessy, R. (2021). Cost-effectiveness of interventions to improve maternal, newborn and child health outcomes: a WHO-CHOICE analysis for Eastern sub-Saharan Africa and South-East Asia. International journal of health policy and management, 10(11), 706.https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9278381/

Tonin, F. S., Aznar-Lou, I., Pontinha, V. M., Pontarolo, R., & Fernandez-Llimos, F. (2021). Principles of pharmacoeconomic analysis: the case of pharmacist-led interventions. Pharmacy Practice, 19(1), 2302. https://doi.org/10.18549/pharmpract.2021.1.2302

Vaduganathan, M., Claggett, B. L., Jhund, P. S., Cunningham, J. W., Ferreira, J. P., Zannad, F., ... & Solomon, S. D. (2020). Estimating lifetime benefits of comprehensive disease-modifying pharmacological therapies in patients with heart failure with reduced ejection fraction: a comparative analysis of three randomised controlled trials. The Lancet, 396(10244), 121-128. https://www.thelancet.com/journals/lancet/article/PIIS0140-6736(20)30748-0/abstract

Werner, K. (2021). Assessing the cost-effectiveness of facility-based emergency care in low resource settings.https://open.uct.ac.za/handle/11427/36234