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  • Sustainable Value: How HEOR is Transforming Carbon Accountability in Pharmaceutical Supply Chains

    Sustainable Value: How HEOR is Transforming Carbon Accountability in Pharmaceutical Supply Chains

    Health economics and outcomes research (HEOR) is rapidly advancing from a discipline focused on cost and health outcomes comparison to one that can measure and evaluate the environmental impact of pharmaceutical products and the supporting supply chains. As healthcare systems make up for an estimated 4-5% of global greenhouse gas emissions with the pharmaceutical industry accounting for a significant share,(1) HEOR provides a standardized framework that connects decarbonisation efforts with clinical, economic, and population health outcomes. By integrating greenhouse gas emissions into conventional value frameworks to integrate, resource use, and waste along with QALYs and budget impact, HEOR disseminates environmental sustainability and patient value in a common language for collective assessment.(1, 2)

    At the basis of this transformation is the HEOR’s ability to robustly measure environmental effects by incorporating life cycle assessment (LCA) into economic evaluations.(3) These methods help assess emissions across supply-chains of raw material extraction, active pharmaceutical ingredient manufacturing, formulation, packaging, logistics, and end-of-life disposal. Findings from LCA-based studies have shown these emissions to be concentrated early in the value chain, especially in API production and other energy-intensive processes. By converting these emissions into comparable units, such as CO2 equivalents per defined daily dose or treatment course, HEOR facilitates stage-specific environmental footprints to match with validated cost and outcome measurements, enabling direct comparisons across therapeutic options.(4, 5)

    HEOR provides a systematic pathway for integrating environmental impacts into health technology assessment (HTA) and payer decision-making, domains that have conventionally focused on clinical efficacy and cost.(6, 7) Increasingly, value frameworks are being recognized to integrate environmental externalities either as additional outcomes, modifiers to cost-effectiveness ratios, or clearly weighted criteria within multicriteria decision evaluations. Methodological advances are now exploring how climate-related damages or health co-benefits of mitigation could be monetised and embedded into cost-benefit analyses, facilitating environmental impacts to be valued alongside conventional health outcomes more transparently and consistently.(1, 2, 4)

    With quantification and valuation, HEOR can prioritize carbon-reduction methodologies across pharmaceutical supply chains by recognizing interventions that offer the greatest emissions reduction per unit of cost or per unit of health benefit maintained. As pharma supply-chain activities result in a hefty healthcare carbon footprint, the potential for mitigation is significant. HEOR can help compare strategies, including cleaner solvents, continuous manufacturing, energy-efficient production lines, optimised cold-chain logistics, and sustainable packaging, both with regards to carbon reduction potential and their impact on medicine prices, affordability, and accessibility.(8, 9)

    On a broader level, HEOR supports strategic planning by prioritizing supply-chain decarbonisation in the bigger scheme of sustainable and climate-resilient healthcare. Higher disease burden and health system costs resulting from climate change have compelled positioning greener pharmaceutical production and logistics as preventive investments. By associating climate-sensitive disease projections with examples of technology adoption, pricing, and supply-chain configuration, HEOR facilitates decision-makers to comprehend trade-offs between short-term investments in greener technologies and long-term benefits in reduced emissions and avoided morbidity and mortality.(1, 8, 10)

    HEOR can facilitate the alignment of incentives among manufacturers, payers, and regulators for converting low-carbon supply chains into sources of competitive advantage rather than a perceived cost burden. Examples from markets including environmental criteria in procurement policies have shown that sustainability can be incorporated into reimbursement and tendering without compromising access. However, strong evidence is required to ensure that requirements remain proportionate and equitable. By determining how environmental metrics influence prices, volumes, and patient outcomes, HEOR enables the development of contracts, payment models, and regulatory pathways to reward decarbonisation while maintaining the core objectives of safety, efficacy, and timely patient access to essential medicines.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. Or Z, Seppanen AV. The role of the health sector in tackling climate change: A narrative review. Health Policy. 2024; 143:105053.
    2. Goldman E, Hamilton L, Dehipawala S, et al. Attitudes and Perceptions on Environmental Sustainability Efforts in the Life Sciences Industry: A Cross-Sectional Survey From HEOR and Industry Professionals. Value Health. 2024; 27(12):S458.
    3. Chen Z, Lian J, Zhu H, et al. Application of Life Cycle Assessment in the pharmaceutical industry: A critical review. Journal of Cleaner Production. 2024; 459:142550.
    4. Williams JT, Bell KJL, Morton RL, et al. Methods to Include Environmental Impacts in Health Economic Evaluations and Health Technology Assessments: A Scoping Review. Value Health. 2024; 27(6): 794-804.
    5. Henshner M. Incorporating environmental impacts into the economic evaluation of health care systems: Perspectives from ecological economics. Resources, Conservation and Recycling. 2020; 154:104623.
    6. McAlister S, Morton RL, Barratt A. Incorporating carbon into health care: adding carbon emissions to health technology assessments. Lancet Planet Health. 2022; 6(12):e993-e999.
    7. Kingma SL, van Bree EM, Rutten-van Mölken MPMH, IJzerman MJ. Exploring Methods to Include Carbon Emissions into an HTA: The Case of Remote Patient Monitoring. Value Health. 2025; S1098-3015(25)05693-1.
    8. A framework for the quantification and economic valuation of health outcomes originating from health and non-health climate change mitigation and adaptation action. 2023. Accessed online on 9th December 2025 at: https://iris.who.int/server/api/core/bitstreams/e2f1790f-3bb1-41e3-8d87-f4d4d85b3dbf/content
    9. Dehipawala S, Goldman E, Hwang E, et al. The Pharmaceutical Industry’s Carbon Footprint and Current Mitigation Strategies: A Literature Review. ISPOR. Accessed online on 9th December 2025 at: https://www.ispor.org/docs/default-source/intl2023/ispor23dehipawalaposter126398-pdf.pdf?sfvrsn=8d02de2b_0
    10. Henshner M. Climate change, health and sustainable healthcare: The role of health economics. Health Economics. 2023; 32:985–992.

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  • The PALISADE Checklist: A Framework for Trustworthy Machine Learning in HEOR

    The PALISADE Checklist: A Framework for Trustworthy Machine Learning in HEOR
    The PALISADE Checklist: A Framework for Trustworthy Machine Learning in HEOR

    Machine learning (ML) is revolutionizing healthcare by supporting smarter decisions and deeper insights, particularly in Health Economics and Outcomes Research (HEOR), where data-led findings impact real-world healthcare policies.[1] However, growing ML adoption is raising concerns about its reliability, transparency, and ethical use. To address these, the PALISADE Checklist provides a well-defined framework for implementing ML responsibly and reliably in HEOR.[2, 3] The framework ensures responsible adoption of ML by assessing its Purpose, Appropriateness, Limitations, Implementation, Sensitivity and Specificity, Algorithm characteristics, Data characteristics, and Explainability; thereby getting its name- the PALISADE checklist.[3]

    The PALISADE Checklist is an innovative framework presented by the ISPOR Machine Learning Task Force to channel the responsible and dependable use of ML in HEOR. It takes into consideration five applications of ML methods that are crucial to HEOR; viz. 1) ML-assisted cohort selection, 2) feature selection, 3) predictive analytics, 4) causal inference, and 5) health economic evaluation, and reflection on transparency and explainability.[3] The rapid adoption of ML techniques in healthcare has necessitated a well-defined methodology to ensure transparency, consistency, and ethical foundation of these methods. PALISADE helps address ML-driven challenges by providing an extensive, standardized set of factors for researchers, analysts, and stakeholders to assess ML applications in HEOR contexts.[2, 3]

    Fundamentally, the PALISADE checklist offers prompts that ML developers can use to constitute their thinking about how the suitability of ML methods can be conveyed to stakeholders and healthcare decision makers. The checklist supports comprehensibility by urging practitioners to clearly define the objectives for using ML in a given HEOR study; asking whether the ML model serves prediction, categorization, or extrapolation, and whether its objective aligns with the wider goals of the undergoing health research. This clarity of intention is crucial for accountability and also for allowing regulators, payers, and policy makers to elucidate results meaningfully as they appropriately apply them in decision-making.[3]

    Another basic element of the framework is the focus on methodological relevance. ML is not a one-size-fits-all approach, and the checklist stipulates a thorough justification for choosing ML over conventional statistical methods. Researchers must weigh apparent advantages of ML pertaining to accuracy, scalability, or insight, along with the suitability of available data. This promotes the selection of ML not just as a trend, but as a carefully chosen instrument for enhancing the quality and applicability of HEOR studies.[3]

    The PALISADE checklist also underscores the inherent limitations and risks in ML applications. It heavily focuses on the importance of recognizing model uncertainty, overfitting, bias, and data quality concerns that could impact the end results. The checklist urges users to openly reveal these limitations, thus reinforcing the integrity of the research and also, training stakeholders to interpret results with caution. Such transparency is crucial in HEOR, where policy decisions influence patient care and overall public health.[3]

    Executional considerations also play a key role in the PALISADE checklist. It encourages researchers to contemplate how ML models will work in real-world settings, beyond controlled environments or highly curated datasets. This consists of evaluating whether the model’s extrapolations are generalizable across populations and time periods, and whether the working infrastructure is in place to incorporate the model into standard healthcare decision-making. Practical feasibility is equally important as theoretical execution for applying ML in HEOR.[3]

    The framework, along with the performance metrics like accuracy or precision, clearly includes comprehensibility as an important element. Healthcare stakeholders, right from clinicians to patients to policymakers, should be capable at understanding the specific prediction made by a particular model. PALISADE supports models that provide comprehensibility outputs, while also warranting additional tools and documentation to convert complex algorithms into coherent logic. This helps lower the risk of misappropriated findings and build trust in ML tools.[3]

    The final constituents of the checklist address data and algorithm features. Researchers are encouraged to document the origin, quality, and representativeness of instructions, authentication, and test datasets. Similarly, PALISADE warrants an exhaustive clarification of the algorithm’s structure, parameters, and tuning decisions. These factors are significant for reproducibility, one of the key factors of scientific accuracy, confirming that findings can be corroborated or improved upon by future researchers.[3]

    Finally, the PALISADE Checklist is more than a framework as it warrants accountability in the application of ML in HEOR. By integrating ethical principles, scientific robustness, and practical insight into each phase of ML execution, it helps facilitate the powerful utilization of ML both innovatively and responsibly.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. Dasari M, Dasari P, Fossati S, et al. Applications of Artificial Intelligence and Machine Learning in Health Economics and Outcomes Research: A Targeted Literature Review. Value in Health. 2024; 27(12). (ISPOR Europe 2024, Barcelona, Spain).
    2. ISPOR. Global Expert Panel Identifies 5 Areas Where Machine Learning Could Enhance Health Economics and Outcomes Research – A Good Practices Report of the ISPOR Machine Learning Task Force. July 2022. Available online at: https://www.ispor.org/heor-resources/news-top/news/view/2022/07/05/global-expert-panel-identifies-5-areas-where-machine-learning-could-enhance-health-economics-and-outcomes-research
    3. Padula WV, Kreif N, Vanness DJ, et al. Machine Learning Methods in Health Economics and Outcomes Research-The PALISADE Checklist: A Good Practices Report of an ISPOR Task Force. Value Health. 2022 Jul;25(7):1063-1080.

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  • Advancing Health Equity through Health Economics and Outcomes Research

    Advancing Health Equity through Health Economics and Outcomes Research

    In a world where access to healthcare should be a universal right, the persistent disparities in health outcomes starkly remind us that health equity remains an elusive goal. Health equity is defined as the absence of unfair and avoidable differences in health among population groups defined socially, economically, demographically, or geographically. This concept underscores the need to ensure that every individual has a fair opportunity to achieve their highest possible level of health, unimpeded by systemic barriers or socioeconomic disadvantages.[1]

    Health Economics and Outcomes Research (HEOR) examines the cost-effectiveness, value, and real-world outcomes of healthcare interventions, enabling data-driven resource allocation. HEOR is pivotal in advancing health equity by analyzing the economic and clinical outcomes of healthcare interventions. HEOR evaluates the value of medical treatments and services, providing data-driven insights that inform decisions about the allocation of healthcare resources. By intertwining health equity with health economics, HEOR helps identify which interventions deliver the most significant benefits across diverse populations, guiding efforts to reduce disparities and improve overall health outcomes.[2]

    The COVID-19 pandemic showed how social determinants of health, such as income, occupation, and access to technology, impact health equity. Higher-income individuals often had the advantage of working from home, reducing their exposure to the virus and benefiting from better internet connectivity, which enabled access to virtual healthcare. In contrast, essential workers in lower-paying jobs faced greater exposure and limited healthcare access, leading to worse outcomes. These disparities highlight the urgent need to reevaluate healthcare practices and structures to address inequities effectively.[3-5]

    HEOR plays a critical role in this re-evaluation by incorporating cost-effectiveness analysis (CEA) to determine which healthcare interventions provide the most value. CEA can also include equity-weighted analyses that prioritize interventions benefitting disadvantaged populations. CEA compares the costs and outcomes of various strategies, enabling policymakers to allocate resources efficiently, especially in settings with limited healthcare budgets. For example, analyzing cancer screening programs’ cost-effectiveness can identify the most beneficial approach for underserved communities, ensuring that resources are directed where they can achieve the greatest impact.[6]

    Furthermore, HEOR’s focus on real-world evidence (RWE) extends beyond controlled clinical trials to understand how treatments perform in everyday practice. This evidence is essential for addressing health disparities, as it reflects the diverse experiences of different patient populations. For instance, RWE during the pandemic showed how vaccine hesitancy and limited access impacted vaccination rates in underserved communities, guiding targeted outreach campaigns. Studies might show that a particular medication is less effective in certain ethnic groups due to genetic variations or disparities in healthcare access, prompting the development of tailored strategies to improve outcomes for these groups.[7]

    Patient-reported outcomes (PROs) and electronic PROs (ePROs) are vital components of HEOR that enhance health equity by capturing patients’ perspectives on their health status, quality of life, and treatment satisfaction. Incorporating PROs and ePROs ensures that the voices of all patients, including those from marginalized groups, are considered in healthcare decision-making. This approach helps uncover specific challenges faced by different populations and informs the design of more inclusive and effective interventions.[8]

    HEOR also influences policy decisions by providing evidence on the broader social determinants of health, such as education, housing, and employment. Highlighting the impact of these factors on health outcomes allows HEOR to advocate for integrated policies that address these root causes of inequity. For instance, research showing that stable housing reduces emergency room visits has prompted healthcare systems to invest in housing assistance programs. In addition, research might demonstrate that improving access to quality education and stable housing can lead to better health outcomes and reduced healthcare costs, supporting the case for holistic approaches that combine social and health policies.[5]

    The pharmaceutical industry benefits significantly from integrating health equity into HEOR. By including diverse populations in clinical trials and subsequent research, companies can develop treatments that are effective across different demographic groups. Failing to include diverse populations can lead to treatments that are less effective or even harmful for certain groups, underscoring the importance of inclusive research practices. This approach not only improves health outcomes but also enhances market access and ensures compliance with regulatory requirements focused on diversity and inclusion.[7]

    In conclusion, advancing health equity through HEOR is both a moral imperative and a strategic necessity for optimizing healthcare delivery and outcomes. By embedding equity considerations into economic evaluations, RWE, PROs, and policy research, HEOR can guide the development of more inclusive healthcare strategies. This comprehensive approach ensures that all individuals, irrespective of their background, have access to the care they need to lead healthy and fulfilling lives. Moving forward, it is essential for all healthcare stakeholders to prioritize health equity in their research and decision-making processes, paving the way for a fairer and healthier future for all.

    Become A Certified HEOR Professional – Enrol yourself here!

    References:

    1. Health equity. World Health Organisation. Available from: https://www.who.int/health-topics/health-equity#tab=tab_1
    2. Fautrel B. SP0124 Health economics and health equity: two complementary disciplines.2017;76:31.
    3. Closing the Gap in a Generation: Health Equity Through Action on the Social Determinants of Health. Final Report of the Commission on Social Determinants of Health. World Health Organization; 2008:1 -256. Available from: https://apps.who.int/iris/bitstream/handle/10665/43943/9789241563703_eng.pdf;jsessionid=365271ACE2052888542881700EEDCA8B?sequence=1.
    4. Burström B, Tao W. Social determinants of health and inequalities in COVID-19. European journal of public health. 2020 Aug 1;30(4):617-8.
    5. Thomas R, Chalkidou K. Cost–effectiveness analysis. InHealth system efficiency: How to make measurement matter for policy and management. 2016. European Observatory on Health Systems and Policies.
    6. Fendrick AM, ISPOR. Real-World Evidence: Additional Tool to Support Clinical Decision Making. Available from: https://www.ispor.org/docs/default-source/strategic-initiatives/ispor-rwe-byline-article_10-25-21.pdf?sfvrsn=687e4bc8_0
    7. Rosenberg SS, Carson BB, Kang A, Lee TH, Pandey R, Rizzo EJ. The Impact of Digital Health Technologies on Health Equity: Designing Research to Capture Patient-Reported Outcomes. ISPOR value & outcomes spotlight. Available from: https://www.ispor.org/publications/journals/value-outcomes-spotlight/vos-archives/issue/view/addressing-assessment-and-access-issues-for-rare-diseases/the-impact-of-digital-health-technologies-on-health-equity-designing-research-to-capture-patient-reported-outcomes.

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  • When is the Ideal Moment to Start HEOR Activities Within Product Development?

    When is the Ideal Moment to Start HEOR Activities Within Product Development?
    When is the Ideal Moment to Start HEOR Activities Within Product Development?

    Health Economics and Outcomes Research (HEOR) has grown into a vital domain in the health and care industry. It plays a key role by offering vital insights into how drugs in the market affect both health and finances. With the means of data driven and evidence-based insights, it plays a critical role in the development and dissemination of new healthcare products by providing important findings regarding clinical development strategies, informing regulatory decisions, and supporting market access. Medical affairs teams in pharmaceutical companies are often concerned about the ideal time for beginning HEOR activities within the product development cycle. The simple answer to this question is: the earlier, the better.[1]

    One can consider engaging in HEOR activities as early as the preclinical or discovery phases of product development. This way, HEOR insights can contribute significantly to the foundational understanding of the economic and clinical landscape. It has been observed that early HEOR involvement can result in more informed decision-making, and influence stakeholder engagement and investment decisions. HEOR helps identify and address potential market access barriers. By understanding the economic landscape and the outcomes that matter most to payers and patients, developers can tailor their products to meet these needs.[2]

    HEOR activities can further strengthen evidence generation in Phase I and II clinical trials. This period is considered critical for gathering preliminary data on safety, efficacy, and health-related quality of life (HRQoL) outcomes. Complementing this data with observations from HEOR studies can help refine economic models and identify the most relevant clinical endpoints. Additionally, HEOR data in early phases can be very useful to inform trial design in Phase III studies, ensuring that all necessary economic and QOL endpoints are captured, to support future health technology assessments (HTAs).[3]

    Advancing to Phase III clinical trials marks a very important milestone in the development of a new pharmaceutical product. At this stage, HEOR has a critical role of providing comprehensive data and evidence needed to confirm the product’s value proposition in the market. This is the phase where the most extensive and rigorous data on efficacy, safety, and outcomes are collected. HEOR activities in Phase III should define clinical endpoints, finalize economic models, undertake full cost-effectiveness analyses, and prepare dossiers required for regulatory submission and reimbursement applications. Embedding HEOR insights into Phase III activities can help predict and prevent potential concerns from regulatory agencies and payers. It showcases the value of the product not only through its clinical efficacy but also through the economic and patient-centered outcomes.[4,5]

    Following Phase III and the acquisition of marketing authorization, the product is launched in the market and becomes available for prescription to patients in the real-world setting. Throughout this stage, the accumulating real-world evidence (RWE) further helps in refining the value of the product by evaluating the efficacy and performance of the product in the diverse patient population. This evidence further substantiates the ongoing research by providing invaluable and robust insights into long-term outcomes, adherence patterns, and overall economic impact of the product. Continuous HEOR efforts post-launch help to support ongoing market access, reimbursement renewals, and potential label expansions. They also provide feedback that can be used to optimize the product’s use and improve patient outcomes over time.[5]

    In summary, HEOR is one continuous process that evolves over the whole cycle of product development. Early initiation of HEOR activities during the development process embeds economic and patient outcome considerations within the strategy of the product from the very beginning. This method not only increases the chances of a drug receiving the appropriate regulatory and reimbursement approval but also guarantees that new therapies are beneficial to both the patient and the medical system. Such proactive HEOR strategy, will put the pharmaceutical companies in a position to better navigate the complexities associated with market access, and to ensure that innovative therapies reach the patients who deserve them most.

    Become A Certified HEOR Professional – Enrol yourself here!

    References:

    1. Holtorf AP, Brixner D, Bellows B, Keskinaslan A, Dye J, Oderda G. Current and future use of HEOR data in healthcare decision-making in the United States and in emerging markets. American health & drug benefits. 2012 Nov;5(7):428.
    2. Van Nooten F, Holmstrom S, Green J, Wiklund I, Odeyemi IA, Wilcox TK. Health economics and outcomes research within drug development: challenges and opportunities for reimbursement and market access within biopharma research. Drug discovery today. 2012 Jun 1;17(11-12):615-22.
    3. Zou KH, Baker CL, Cappelleri JC, Chambers RB. Data Sources for Health Economics and Outcomes Research. InStatistical Topics in Health Economics and Outcomes Research 2017 Nov 22 (pp. 1-13). Chapman and Hall/CRC.
    4. Garrison Jr LP, Neumann PJ, Erickson P, Marshall D, Mullins CD. Using real‐world data for coverage and payment decisions: the ISPOR real‐world data task force report. Value in health. 2007 Sep;10(5):326-35.
    5. Chou JW, Portelli A, Cournoyer A. De-risking Market Launch: Leveraging HEOR Evidence Planning to Support Informed Payer Decisions. J Clin Pathways. 2022;8(8):63-65.

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  • Modelling Health Inequalities in HEOR: A Path to Equitable Healthcare

    Modelling Health Inequalities in HEOR: A Path to Equitable Healthcare
    Modelling Health Inequalities in HEOR: A Path to Equitable Healthcare

    In the complex tapestry of healthcare, there exist easily recognizable inequities in access, treatment options, and outcomes. They manifest in various ways: from life expectancy to the burden of diseases and the quality of care delivered. Sometimes, these inequalities are associated with multiple factors, such as socioeconomic status, race, gender, age, geographic location, employment status, and income level. By and large, Health Economics and Outcomes Research (HEOR) seeks to unlock the complex relationship between economics, healthcare outcomes, and social drivers of health. With a set of powerful and sophisticated tools in modeling and analyses at its disposal, HEOR works to grasp the underlying causes of these inequities and proposes effective interventions.[1,2]

    Health inequalities are not only a matter of statistics; but are also profound reflections of systemic injustices pointing towards broader issues of social justice and systemic discrimination. In this case, such disparities – whether in life expectancy, burden of disease, or access to healthcare – are all borne disproportionately by the marginalized communities. Since the seminal study in 2010 that established the connection between social status and health, health inequalities have garnered significant attention.[3] However, as of 2024, these inequalities not only persisted but, in some aspects of health had actually increased. A study in 2020 concluded that inequality has increased considerably over time: with life expectancy improving for the top 60% of the earners while stagnating for the poorest 40%. Moreover, the life expectancy for women has declined in the bottom five deciles of deprivation – a very certain and grave indication of the growing divide between the rich and the poor.[4]

    These observations emphasize the imperative to address the escalating health inequalities in our society.[5] HEOR plays a crucial role in this effort by allowing us to delve deeper into the roots of these inequalities and offer data-driven solutions. HEOR’s arsenal of modeling techniques offers a window into the complex dynamics of health inequalities. From economic modeling to predictive analytics and simulation methods, these tools enable researchers to dissect the multifaceted nature of disparities.[3]

    Models, such as economic models, can be used to screen interventions for cost-effectiveness while considering health outcomes at the same time. Cost-effectiveness analysis (CEA) and cost-utility analysis (CUA) are the two commonly used methods that see which of the chosen approaches is going to give better value for money in the reduction of health disparities.[6] Models of this nature can be used to show how the limited resources can be better allocated so as to have a more positive effect on health in disadvantaged communities. Another key tool involves predictive models that allow data to project health outcomes by social determinants like income and education. This kind of model can identify which groups are at a higher risk of health problems and would be helpful in the development of targeted interventions. Simulation models go a step further by allowing researchers to test different scenarios for their potential impact on health inequalities. This helps policymakers understand the potential outcomes of different policies or programs before they are implemented.[7-9]

    The application of HEOR modelling extends beyond theoretical abstraction, translating into tangible actions to combat health inequalities. These models look underneath the aggregate data and give the outline of disparities, pinpointing exactly where interventions are most urgently needed. Armed with this information, policymakers can craft evidence-based policies that address the root causes of health inequities, allocate resources equitably, and ultimately reduce health disparities. Furthermore, HEOR models serve as a litmus test for the effectiveness of interventions, enabling continuous refinement and optimization of strategies to narrow the health gap.[9]

    The path toward health equity is not without obstacles. Data limitations and related variables can affect the accuracy of models, leading to incomplete or skewed analyses. Moreover, social determinants of health are complex, often interacting in ways that are difficult to predict. Biases are another factor that can reflect differences in the final result.[10,11]

    Some of these challenges can be tackled with advances in technology. Artificial intelligence and machine learning provide new ways of analyzing large datasets to uncover complex patterns that could potentially lead to more effective solutions for health inequalities.[9] However, at the same time with these advancements, there is a need for far greater commitment to ethical practices and a focus on fairness and equity.[12,13]

    In conclusion, modeling health inequalities in HEOR is essential to creating a more equitable healthcare system. By using models to understand and address health disparities, we can navigate this labyrinth, paving the way for a future where resources are allocated based on need and every individual has an equal opportunity to thrive and achieve better health outcomes.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. Islam MM. Social determinants of health and related inequalities: confusion and implications. Frontiers in public health. 2019 Feb 8;7:414037.
    2. Singu S, Acharya A, Challagundla K, Byrareddy SN. Impact of social determinants of health on the emerging COVID-19 pandemic in the United States. Frontiers in public health. 2020 Jul 21;8:564623.
    3. Marmot M. Fair society, healthy lives. Fair society, healthy lives. 2010. https://www.instituteofhealthequity.org/resources-reports/fair-society-healthy-lives-the-marmot-review/fair-society-healthy-lives-full-report-pdf
    4. Marmot, Michael. 2020. “Health equity in England: the Marmot review 10 years on.” BMJ 368.
    5. Mahase E. A decade on from Marmot, why are health inequalities widening?. BMJ: British Medical Journal (Internet). 2019 Jun 17;365:l4251.
    6. Cookson R, Mirelman AJ, Griffin S, Asaria M, Dawkins B, Norheim OF, Verguet S, Culyer AJ. Using cost-effectiveness analysis to address health equity concerns. Value in Health. 2017 Feb 1;20(2):206-12.
    7. Rojas JC, Fahrenbach J, Makhni S, Cook SC, Williams JS, Umscheid CA, Chin MH. Framework for integrating equity into machine learning models: a case study. Chest. 2022 Jun 1;161(6):1621-7.
    8. Wolfson MC. POHEM: a framework for understanding and modelling the health of human populations. World health statistics quarterly 1994; 47 (3/4): 157-176. 1994.
    9. Smith BT, Smith PM, Harper S, Manuel DG, Mustard CA. Reducing social inequalities in health: the role of simulation modelling in chronic disease epidemiology to evaluate the impact of population health interventions. J Epidemiol Community Health. 2014 Apr 1;68(4):384-9.
    10. Perrin E, Ver Ploeg M, editors. Eliminating health disparities: measurement and data needs.
    11. Bilheimer LT, Klein RJ. Data and measurement issues in the analysis of health disparities. Health services research. 2010 Oct;45(5p2):1489-507.
    12. Haseltine WA. Can Artificial Intelligence Help Eliminate Health Disparities? 2024 https://www.insideprecisionmedicine.com/topics/can-artificial-intelligence-help-eliminate-health-disparities
    13. Chen IY, Joshi S, Ghassemi M. Treating health disparities with artificial intelligence. Nature medicine. 2020 Jan;26(1):16-7.

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  • The Integration of Artificial Intelligence Solutions in Medical Affairs

    The Integration of Artificial Intelligence Solutions in Medical Affairs

    In the era of digital transformation, artificial intelligence (AI) acts as a catalyst to revolutionize the landscape of medical affairs. AI has the potential to disrupt the way a medical affairs department in a pharmaceutical company functions, by virtue of its capabilities in functions as diverse as data handling, data analysis, literature review, information retrieval, and cleaning. In fact, AI can also help medical affairs professionals focus their KOL engagement activities.

    Given the ability AI has to process vast amounts of data, uncover hidden patterns, and automate complex tasks, it is not surprising that AI has already started to transform the clinical trial data analytic landscape. Further, by the capacity to handle and analyze big data, AI has made it possible to assimilate vast amounts of real-world data (RWD) from various sources, including claims data, electronic health records (EHRs), registries, and social media as well, and to generate real-world evidence (RWE) through the analysis of RWD. This capability of handling big data has empowered medical affairs teams to discern elusive patterns and trends, thereby elevating decision-making capabilities.[1,2]

    Next, clinical trials have been known to face challenges such as prolonged recruitment times and suboptimal design. Here, AI algorithms prove instrumental, facilitating the identification of suitable patient populations, predicting enrolment rates, and optimizing protocols with efficiency and ethical considerations. This not only expedites trial completion but also fast-tracks the development of life-saving medications. [3]

    AI also plays a pivotal role in publication planning and medical writing. AI algorithms, in this context, conduct an automated literature review to identify gaps and opportunities for new publications. This ensures the informed strategic planning of publications that contribute meaningfully to scientific discourse. In medical writing, AI-powered tools enhance efficiency and contribute to the overall quality and value of publications by analyzing language patterns to meet both scientific and regulatory standards.[3]

    AI can also contribute to regulatory affairs by identifying and facilitating essential documentation. Further, it can also facilitate communication with relevant stakeholders, thereby ensuring smooth interaction between different departments leading to regulatory submission. This can ensure improved adherence to evolving guidelines and freeing resources for strategic efforts.[4]

    Moving on, AI also has an important role to play in enhancing KOL (key opinion leader) engagement strategies by helping in the refinement of communication and fostering effective dialogue between pharmaceutical companies and stakeholders. Through efficient social network analysis, AI identifies relevant KOLs, ensuring focused efforts for successful communication and market access. By using AI, it is also possible to craft targeted communication campaigns aligned with KOL expertise. AI-powered chatbots can enhance KOL engagement by providing them with on-demand access to essential information. AI can also facilitate planning and executing CMEs, matching the interest and expertise of KOLs with the CME topics. AI can further enhance the effectiveness of CMEs by aligning content with individual learning styles and preferences, ultimately contributing to the professional development of healthcare professionals and strengthening connections with influential leaders in the field.[5,6]

    Analysis of complex datasets leading to predictive modeling using AI can enable a thorough assessment of market dynamics, allowing pharmaceutical companies to make strategic decisions that optimize their market presence. AI also plays a crucial role in pricing strategies, dynamically adjusting them based on the intricate dynamics of healthcare systems, ensuring competitiveness and responsible access to innovative healthcare solutions.[7]

    Health Economics and Outcomes Research (HEOR) significantly benefits from AI’s capabilities. AI-powered systematic literature reviews can significantly shorten the time required for completion of the review, thereby enhancing the speed of market access. AI-driven models facilitate the assessment of cost-effectiveness and budget impact, empowering decision-makers with crucial insights that shape market access and reimbursement strategies.[7]

    Patient support programs are also revolutionized by AI, offering sophisticated and responsive personalized support. AI’s continuous monitoring contributes to enhanced patient adherence and outcomes, fostering a proactive approach to healthcare management for improved patient experiences and overall health outcomes.[8]

    In competitive intelligence, AI stands as a game-changer, continuously monitoring competitor activities and providing nuanced insights based on the competitive landscape. This empowers organizations to anticipate market shifts, identify strategic opportunities, and position themselves effectively within the dynamic healthcare ecosystem. Continuous monitoring of competitor activities, insights gained, and a proactive approach shaped by AI-driven analysis position pharmaceutical companies strategically in the marketplace.[8]

    AI has a role in social media analysis as well. By scanning social media for sentiment analysis, AI identifies emerging trends and unmet needs. Armed with these insights, medical affairs teams can deliver highly relevant information through tailored communication strategies, fostering deeper relationships and trust with both healthcare professionals and patients alike. Additionally, AI plays a pivotal role in forecasting drug demand, ensuring consistent supply chain logistics. This predictive capability minimizes stockouts and overstocks, contributing to a seamless healthcare system where patients have reliable access to the medications they need. Furthermore, AI facilitates communication through telemedicine platforms, breaking down geographical barriers and improving patient access to care.[9]

    Beyond the above-mentioned use cases, AI has been evolving and finding new applications to enhance the efficiency of a medical affairs department. Implementing AI solutions in medical affairs is not merely an upgrade but a strategic leap toward optimized performance and enhanced patient care. While challenges exist, careful planning, a focus on ethical considerations, and proactive change management can pave the way for successful integration and unlock the transformative potential of AI in this critical domain. By leveraging the power of AI, medical affairs teams can gain deeper insights, make data-driven decisions, and ultimately contribute to a more efficient, personalized, and effective healthcare system for all.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. Bedenkov A, Moreno C, Agustin L, et al. Customer centricity in medical affairs needs human-centric artificial intelligence. Pharmaceutical Medicine. 2021 Jan;35(1):21-9.
    2. Yang H, Khatry DB. Reinventing Medical Affairs in the Era of Big Data and Analytics. InData Science, I, and Machine Learning in Drug Development 2022 Oct 3 (pp. 245-263). Chapman and Hall/CRC.
    3. Mayorga-Ruiz I, Jiménez-Pastor A, Fos-Guarinos B, et al. The role of AI in clinical trials. Artificial Intelligence in Medical Imaging: Opportunities, applications and risks. 2019:231-43.
    4. Khalifa AA, Ibrahim MA. Artificial intelligence (AI) and ChatGPT involvement in scientific and medical writing, a new concern for researchers. A scoping review. Arab Gulf Journal of Scientific Research. 2024 Jan 4.
    5. Patil RS, Kulkarni SB, Gaikwad VL. Artificial intelligence in pharmaceutical regulatory affairs. Drug Discovery Today. 2023 Jul 12:103700.
    6. Bedenkov A, Rajadhyaksha V, Beekman M, et al. Developing medical affairs leaders who create the future. Pharmaceutical medicine. 2020 Oct;34(5):301-7.
    7. Farah L, Borget I, Martelli N. International Market Access Strategies for Artificial Intelligence–Based Medical Devices: Can We Standardize the Process to Faster Patient Access? Mayo Clinic Proceedings: Digital Health. 2023 Sep 1;1(3):406-12.
    8. Hoffman FP, Freyn SL. The future of competitive intelligence in an AI-enabled world. International Journal of Value Chain Management. 2019;10(4):275-89.
    9. Roski J, Gillingham BL, Just E, et al. Implementing and scaling artificial intelligence solutions: considerations for policy makers and decision makers. Health Affairs Forefront. 2018.

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  • Using Synthetic Controls in Oncology Real-World Data Studies for Treatment Insights

    Using Synthetic Controls in Oncology Real-World Data Studies for Treatment Insights

    In the ever-evolving landscape of healthcare, Real-World Data (RWD) studies have emerged as a pivotal tool in shaping treatment strategies and enhancing patient outcomes. For the Health Economics and Outcomes Research (HEOR) industry, these studies hold a special significance, providing valuable insights into the real-world effectiveness of treatments. In recent times, synthetic controls in oncology RWD studies have gained momentum, offering a novel approach to accelerate the development of new treatments and broaden our understanding of their impacts.[1]

    The essence of oncology research lies in its constant pursuit of more effective and targeted treatments for a range of malignancies. Traditional Randomized Clinical Trials (RCTs), while crucial, often have limitations that restrict their ability to mirror real-world scenarios comprehensively. This is where RWD studies step in, utilizing data collected from routine clinical practice to bridge the gap between RCTs and real-life patient experiences. However, challenges such as confounding variables and lack of randomization persist in these studies, prompting the exploration of innovative methodologies like synthetic controls.[1,2]

    Synthetic controls, in essence, involve the creation of a hypothetical control group that mirrors the characteristics of the treatment group. By leveraging historical patient data, demographic information, disease progression, and other relevant factors, researchers can construct a comparable control arm. This approach, rooted in advanced statistical techniques, provides a powerful tool to estimate treatment effects and mitigate biases that might arise in traditional observational studies.[5]

    Synthetic control arms are a variant of external control arms, and represent an inventive strategy where researchers create a virtual or synthetic control group by harnessing existing data, rather than enlisting fresh participants for the control cohort. The formulation of a synthetic control arm entails evaluating patient information contained within pre-existing datasets, like electronic health records, which is rendered anonymous and stripped of any personally identifiable details. These synthetic controls replicate real patients who would conventionally be enrolled as part of the trial’s control group.[5]

    The application of synthetic controls in oncology RWD studies offers several key advantages to the HEOR industry. It expedites the evaluation of new treatments by reducing the time required for traditional RCTs. This acceleration is paramount in oncology, where swift access to effective treatments can significantly impact patient outcomes and quality of life. Moreover, synthetic controls enable researchers to glean insights from real-world patient populations that might have been excluded from traditional clinical trials due to stringent eligibility criteria. This inclusivity not only enhances the generalizability of study findings but also provides a more holistic understanding of treatment efficacy across diverse patient demographics. Next, by harnessing the richness of RWD, synthetic controls facilitate the assessment of treatment effects in various subpopulations, shedding light on the intricate interplay between treatments and patient characteristics.[2-5]

    As the HEOR industry delves deeper into the utilization of synthetic controls for oncology RWD studies, it is imperative to acknowledge the challenges that accompany this innovative approach. Rigorous validation and robust sensitivity analyses are paramount to ensure the credibility of synthetic control results. Transparency in methodology and data sources is equally vital to establish trust among stakeholders and foster the adoption of this methodology in regulatory decision-making.[3]

    In conclusion, the integration of synthetic controls in oncology RWD studies presents a promising avenue for the HEOR industry to enhance treatment development and expedite knowledge acquisition. This methodology’s ability to replicate a control group closely resembling the treatment group regarding relevant variables addresses some of the limitations inherent in observational studies. As the healthcare landscape continues to evolve, embracing innovative methodologies like synthetic controls has become necessary to drive advancements in oncology treatments and ultimately improve patient outcomes.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. Prasad V. Reliable, cheap, fast and few: What is the best study for assessing medical practices? Randomized controlled trials or synthetic control arms?. European journal of clinical investigation. 2021 Aug 1;51(8):e13580.
    2. Yap TA, Jacobs I, Baumfeld Andre E, et al. Application of Real-World Data to External Control Groups in Oncology Clinical Trial Drug Development. Front Oncol. 2022 Jan 6;11:695936.
    3. Thorlund K, Dron L, Park JJH, Mills EJ. Synthetic and External Controls in Clinical Trials – A Primer for Researchers. Clin Epidemiol. 2020 May 8;12:457-467.
    4. Greshock J, Lewi M, Hartog B, Tendler C. Harnessing real-world evidence for the development of novel cancer therapies. Trends in Cancer. 2020 Nov 1;6(11):907-9.
    5. Banerjee R, Midha S, Kelkar AH, Goodman A, Prasad V, Mohyuddin GR. Synthetic control arms in studies of multiple myeloma and diffuse large B-cell lymphoma. British journal of haematology. 2022 Mar 1;196(5):1274-7.

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  • The Real World Evidence Registry by ISPOR: an Initiative to Enhance Transparency

    The Real World Evidence Registry by ISPOR: an Initiative to Enhance Transparency

    The world today is observing an exponential growth in the volume and variety of the real-world data (RWD). Thanks to the technological advancements and the rise in the use of integrated electronic medical records (EMRs), RWD is ever more accessible and applicable in the regulatory domain as well as outcomes research. The evidence from randomized controlled trials (RCTs) is still undoubtedly the gold standard for assessing treatment efficacy; however, the interest and potential for adapting RWD into real-world evidence (RWE) is on the rise. This can prove extremely beneficial to make informed healthcare decisions. (1)

    RWE has several advantages over RCT findings, particularly in research to aid decision making for healthcare delivery. These advantages include the availability of well-timed data at reasonable cost, large sample sizes enabling analyses of subgroups and less common effects, and the overall better representation of the real-world practices and behaviours. (1) Nonetheless, RWE has several concerns questioning its credibility, including data quality, biases – thanks to lack of randomization, and possibly false results owing to data mining. Some other major challenges, as highlighted by the USFDA, include inconsistency in sources and formats, different nature of source data captured by different regions, differences in terminology and exchange, different methods used to build datasets for aggregation, and differences in overall data quality. (2) These are the challenges that have haltered the progress of RWE in healthcare despite its significant data capabilities. (1) In the same context, USFDA has acknowledged the need for standardizing RWD for healthcare decision making. As a result, a draft guidance has been recently released for the industry, outlining USFDA’s requirements from the sponsors for submission of drug and biological product study data by RWD sources. (2)

    Acknowledging similar concerns over RWD quality, the International Society for Pharmacoeconomics and Outcomes Research (ISPOR), in collaboration with the International Society for Pharmacoepidemiology, the Duke-Margolis Center for Health Policy, and the National Pharmaceutical Council, has recently launched the Real-World Evidence Transparency Initiative with the purpose to promote health economics and outcomes research (HEOR) excellence to improve global healthcare decision making. Additionally, it will help instituting a culture of transparency for analysis and reporting of hypotheses to evaluate RWE studies on treatment effects. (3)

    In order to further improve transparency and credibility, the Real-World Evidence Transparency Initiative, on October 26th 2021, launched the Real-World Evidence Registry. (4, 5) The registry will offer a fit-for-purpose platform to the researchers to prospectively register their study designs before starting data collection. (4) The registry will implement open, centralized workflows that will enhance collaboration and facilitate the transparency needed to promote the trust in the study results. (4, 5)

    The RWE registry is a streamlined registration website, especially for RWE studies conducting the hypothesis evaluation of treatment effects (HETE studies) using secondary data. This searchable platform will provide a place for pre-registration of studies that may not need registration for regulatory purposes, but benefit from the accuracy of transparent study methods and also provide a reference (such as a URL or doi) to share with the involved stakeholders, such as peer reviewers, assessors, or other decision makers. (4)

    With the growing adaptation of RWE studies alongside RCTs, the launch of the registry could not have happened at a more opportune time. We hope that researchers optimize this resource and this move helps improving the transparency and credibility of RWD and thus, RWE studies. At Marksman Healthcare, we are well equipped to provide services in this domain, including RWE study protocol development, study/protocol registration in the RWE registry, RWD analysis, and RWE publication support, among others.

    Become A Certified HEOR Professional – Enrol yourself here!

    References

    1. Real World Evidence. ISPOR Strategic Initiatives. Available at: https://www.ispor.org/strategic-initiatives/real-world-evidence
    2. FDA drafts data standards guidance for RWD. October 2021. Available at: https://www.raps.org/news-and-articles/news-articles/2021/10/fda-drafts-data-standards-guidance-for-rwd
    3. Real-World Evidence Transparency Initiative. ISPOR Strategic Initiatives. Available at: https://www.ispor.org/strategic-initiatives/real-world-evidence/real-world-evidence-transparency-initiative?utm_medium=press_release&utm_source=public&utm_campaign=general_ispor&utm_content=press_release_oct26&utm_term=rwe_registry
    4. Real-World Evidence Registry. Available at: https://www.ispor.org/strategic-initiatives/real-world-evidence/real-world-evidence-registry
    5. New Real-World Evidence Registry Launches. October 26 2021. Available at: https://www.newswise.com/articles/new-real-world-evidence-registry-launches

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  • Generating Scientific Evidence (Efficacy/Safety/Cost Data) from India

    Generating Scientific Evidence (Efficacy/Safety/Cost Data) from India

    Every country exercises strict control on medicines’ market access. Typically, this requires successful completion and adequate presentation of results from phase I through phase III clinical trials, bringing forward the findings of medicine’s safety and efficacy. The USFDA approves approximately 40 new medicines for the US market each year through this process. (1) In India, this number is more than 100 new medicines annually; however, there is not enough published evidence on submitted applications or summaries of approved medicines. Therefore, concerns are being raised about the safety and efficacy around medicine approvals in India in the absence of appropriate clinical trials. (2,3)

    For instance, a recent study, which evaluated the clinical evidence on the safety and efficacy of the most common metformin fixed dose combinations (FDCs) for T2DM in India, has highlighted the growing national and international concerns about the Indian drug regulatory system. Findings from this study further show high numbers of unapproved medicines and their irrational combinations floating in the market. This study has assessed the basis of efficacy and safety of top-selling metformin FDCs in India against four WHO criteria from clinical trial guidelines for the approval of FDCs. In India, only five FDCs have been approved by the Central Drugs Standard Control Organization (CDSCO); while, in reality, the Indian FDC-diabetes market contributes to the two-third of all diabetes medicine sales. (4) Furthermore, evaluation of published and unpublished clinical trials of these approved FDCs seemed to show underpowered and poor quality evidence of safety and efficacy for the treatment of T2DM. (5)

    The overall lack of available India-specific evidence heightens the need for its generation by publishing the unpublished trial results with Indian patients. India has in place the only required registration with Clinical Trials Registry – India, the national clinical trials database, since 2009. Moreover, the unpublished trials listed in this registry merely provide basic trial information with no results or outcomes reported. The lack of trials on Indian patients, in particular, is of concern, considering CDSCO’s guidelines for drug approvals acknowledge the importance of conducting trials on the Indian population to determine safety and efficacy.4

    Additionally, the Government, with an aim to achieve Universal Health Coverage (UHC) in order to reduce huge out-of-pocket (OOP) health expenditure and ensure affordable access to essential health care for the entire population, has identified a key priority of ensuring value for money in the health budget. This requires a systematic process for generating policy-relevant evidence that can inform policy decisions regarding health resource allocation, i.e. clinical effectiveness studies, cost-effectiveness studies, budget impact studies, along with ethical, social and political feasibility studies. (6) Needless to say that the healthcare payers, regulatory authorities, and health technology assessment (HTA) agencies also make decisions on relative efficacy of the new products based on evidence generated from clinical trials. (7)

    In most western countries along with the United States, the consumer rarely pays for the product—the payer is generally a third-party private or governmental insurer. Before approving a new medical entity (medicines/medical technologies) for reimbursement, private and governmental payers analyze clinical and economic data to determine the clinical value and cost-effectiveness of the new product as compared with currently available treatments. (8) Indian health system, on the other hand, is characterized by a vast but under-utilized public health infrastructure and a largely unregulated private market catering to greater need for curative action; where high OOP health expenditures hinder access to healthcare. (9)

    We believe it is high time even insurance companies start asking for robust evidence in order to provide reimbursement of better healthcare technologies and easier access to care. India needs to bring about a major reform in its health insurance policies, wherein a keen eye for detail is given to the published trial data on safety and efficacy of a drug or relevant evidence about a medical technology.

    Become an Certified HEOR Professional – Enrol yourself here!

    References:

    1. U.S. Food and Drug Administation. New Drugs at FDA: CDER’s New Molecular Entities and New Therapeutic Biological Products.
    2. Ministry of Health and Family Welfare, Department of Health and Family Welfare. Gazette of India, 10 March 2016. New Delhi, 2016.
    3. McGettigan P, et al. Regulatory upheaval and irrational medicines in India: a study of fixed-dose combination drugs. PLoS Med 2015; 12:e1001826.
    4. Evans V, et al. Adequacy of clinical trial evidence of metformin fixed-dose combinations for the treatment of type 2 diabetes mellitus in India. BMJ Glob Health 2018; 3:e000263.
    5. Shimpi RD, et al. Comparison of effect of metformin in combination with glimepiride and glibenclamide on glycaemic control in patient with Type 2 diabetes mellitus. Int J PharmTech Res 2009; 1:50–61
    6. Prinja S, et al. Health Technology Assessment for Policy Making in India: Current Scenario and Way Forward. Pharmacoecon Open 2018; 2(1):1-3. 
    7. Dang A, et al. Real world evidence: An Indian perspective. Perspect Clin Res 2016; 7:156:160.
    8. Gold M. Getting reimbursement for your product in the United States. June, 2003. 
    9. Prinja S, et al. Universal Health Insurance in India: Ensuring Equity, Efficiency, and Quality. Indian Journal of Community Medicine : Official Publication of Indian Association of Preventive & Social Medicine. 2012; 37(3):142-149.

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