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  • Integrating Health Equity into Economic Decision-Making

    Integrating Health Equity into Economic Decision-Making

    For any health program, it is essential to check not just the total health gain but also who is receiving those gains; as fair decisions depend on both. Typical cost-effectiveness analysis (CEA) emphasizes efficacy by comparing costs and quality-adjusted life years (QALYs), indirectly considering all QALYs as equal in spite of whether they apply to the rich or poor, the healthy or severely ill, thus inadvertently increasing existing health disparities. Alternatively, equity-based approaches can make exact value decisions about minimizing inequitable and avoidable health gaps, using ethical theories, including egalitarianism and prioritarianism to validate giving extra importance to improvements in disadvantaged groups.(1-3)

    ​To implement equity factors, analysts first disaggregate costs and health outcomes by equity-relevant categories, including socioeconomic status, ethnicity, gender, region, or baseline health. This is done with the intention of estimating and not assuming the distributional effects of interventions. Extended or “equity-informative” CEA then informs subgroup-specific findings and inequality parameters, combined with standard incremental cost-effectiveness ratios (ICERs), enabling decision-makers to assess who benefits and who loses choosing different policy options without condensing everything into a single number. This descriptive step is often possible with fewer data enhancements and already offers a clearer base for deliberation about equity-efficiency trade-offs.(1)

    ​Distributional cost-effectiveness analysis (DCEA) specifically focuses on inequality in a social welfare framework that values both total health and its distribution. DCEA turns intervention costs into health opportunity costs and demonstrates alternative options for changing the distribution of lifetime health across groups, condensing the result with indices, including the equally distributed health that drops as inequality increases. By changing inequality aversion metrics, DCEA shows when a slightly less efficient intervention may be socially preferable since it provides larger gains to the worst off.(2)

    ​Equity weights applied to QALYs or disability-adjusted life years (DALYs) are another way to reinforce support for equity, by attributing higher weights to health gains for people who are poorer, more severely ill, or have larger lifetime health deficits. These weights can be obtained from empirical studies of public preferences or from ethical reasoning. However, they pose practical and ethical questions about how to draw and validate the chosen parameters in a clear, legitimate manner. Even when not used routinely as decision rules, equity-weighted analyses can explain how different social value findings would change intervention rankings.(4)

    ​Most health technology assessment (HTA) recommendations continue to prefer standard CEA and refer to equity only in broad terms, leaving distributional concerns to informal discussions rather than precise modelling. However, evidence suggests that incorporating equity considerations is not only feasible but also useful for decisions on screening, vaccination, and service delivery, for e.g., in low- and middle-income countries seeking universal health coverage.(1, 5) As methods, including extended CEA, DCEA, and equity weighting, are more widely applied, economic evaluation can transform into a more accessible, value-aware tool that facilitates transparent balancing of efficacy and equity in health policy.

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    References

    1. Muir JM, Radhakrishnan A, Ozer Stillman I, Sarri G. Health Equity Considerations in Cost-Effectiveness Analysis: Insights from an Umbrella Review. Clinicoecon Outcomes Res. 2024; 16:581-596.
    2. Asaria M, Griffin S, Cookson R. Distributional Cost-Effectiveness Analysis: A Tutorial. Med Decis Making. 2016; 36(1):8-19.
    3. Sivanantham P, Anandraj J, Ravel V, et al. Equity Considerations in Health Economic Evaluations: A Systematic Review of WHO South-East Asia Region Countries. WHO South-East Asia Journal of Public Health. 2024; 13(2): 69-77.
    4. Sassi F, Archard L, Le Grand J. Equity and the economic evaluation of healthcare. Health Technol Assess 2001; 5(3).
    5. Methods for the development of NICE public health guidance (third edition). 2012. [Accessed online on 22nd December 2025]. Available at: https://www.nice.org.uk/process/pmg4/chapter/incorporating-health-economics

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  • Turning Insight into Adoption: Essentials of Effective Product Launches

    Turning Insight into Adoption: Essentials of Effective Product Launches

    A successful pharmaceutical product launch is a closely managed process that transforms clinical evidence into real‑world approval across regulators, payers, prescribers, and patients, rather than a one‑off promotional campaign.(1) It begins early in product development with a clear understanding of the treatment pathway and unmet needs, so the product’s value proposition precisely clarifies which patients benefit most, how outcomes improve against standard of care, and why is that significant clinically and economically.(1-3)

    ​Cross‑functional preparation of medical, regulatory, commercial, market access, and manufacturing teams helps align labelling, evidence, pricing assumptions, and supply with the time of approval. This collective planning towards the shared purpose of product launch ensures consistency of the claims made to clinicians and payers with the data and the final outcome, thus minimizing any sudden surprises about safety information, reimbursement, or stock availability.(1, 2, 4)

    ​After this, market access and pricing strategy evaluate whether a clinically strong product can actually reach patients at scale.(5) Value dossiers and health‑economic models must convert trial endpoints into payer‑relevant outcomes, including lower hospitalisations, reduced complication rates, or long‑term cost offsets, for reimbursement decisions and formulary inclusion to support prescriber goals. Careful use of patient access programs or outcomes‑based agreements can also alleviate payer concerns in high‑cost or high‑uncertainty groups.(6)

    ​Stakeholder‑specific communication helps connect data with everyday clinical decisions. For prescribers, scientific communication through peer‑reviewed publications, congress presentations, and medical education clarifies positioning, dosing, safety, and how the product aligns with the guidelines. For patients and caregivers, precise, compliant materials help explain benefits, risks, and adherence expectations in relevant language, facilitated by helplines or digital tools where appropriate.(7)

    Several pharmaceutical launches have shown how incorporating evidence, access, and stakeholder engagement have immensely changed outcomes. For instance, evaluations of successful specialty drug launches emphasize early engagement of market access teams as it facilitates the alignment of clinical development with payer needs. This has reportedly built strong contracts and affordability programs for faster uptake and fewer access barriers, even at comparatively high prices. However, post‑marketing surveillance studies have shown inadequate planning for real‑world safety data to result in product re-labelling or withdrawals, highlighting the need of integrating continuous evidence generation and pharmacovigilance into the core launch strategy early on.(4, 7, 8, 9)

    ​In conclusion, an efficient product launch must treat the first months in the market as a continuous learning process. Real‑world evidence from post-marketing studies, registries, and prescription patterns must be reincorporated into updated guidance, enhanced messaging, and access negotiations, while safety and efficacy data in routine practice helps adjust the initial positioning.(1) Organizations that imbibe this learning‑oriented, patient‑centred approach into every launch achieve a continuous capacity to improve both commercial performance and health impact over time.

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    References

    1. Naidoo P, Bouharati C, Rambiritch V, Jose N, Karamchand S, Chilton R, Leisegang R. Real-world evidence and product development: Opportunities, challenges and risk mitigation. Wien Klin Wochenschr. 2021 Aug;133(15-16):840-846.
    2. Strongin RJ. Pharmaceutical Marketplace Dynamics [Internet]. Washington (DC): National Health Policy Forum; 2000 May 31. (Issue Brief, No. 755.) [Accessed online on 22nd December 2025] Available from: https://www.ncbi.nlm.nih.gov/books/NBK559751/
    3. Årdal C, Lopert R, Mestre-Ferrandiz J. Overview of the Market for Novel Medicines in the WHO European Region [Internet]. Copenhagen Ø, Denmark: World Health Organization; 2022. 3 The pharmaceutical sector: an evolving market. [Accessed online on 22nd December 2025] Available from: https://www.ncbi.nlm.nih.gov/books/NBK587857/
    4. Alexander S. 2021. New Product Launch Success: A Literature Review. Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis. 2021; 69(1):151-176.
    5. Ntais C, Talias MA, Fanourgiakis J, Kontodimopoulos N. Managing Pharmaceutical Costs in Health Systems: A Review of Affordability, Accessibility and Sustainability Strategies. J Mark Access Health Policy. 2024 Dec 10;12(4):403-414.
    6. Goodman C, Karnes E, Faulkner E, et al. Cost-effectiveness Considerations in the Approval and Adoption of New Health Technologies. Department of Health and Human Services Assistant Secretary for Planning and Evaluation. 2007. [Accessed online on 22nd December 2025] Available from: https://aspe.hhs.gov/sites/default/files/private/pdf/75096/report.pdf
    7. Gonella S, Di Giulio P, Brofferio L, Riva-Rovedda F, Cotogni P, Dimonte V. Stakeholders’ Perspective on the Key Features of Printed Educational Resources to Improve the Quality of Clinical Communication. Healthcare (Basel). 2024 Feb 4;12(3):398.
    8. Büssgen M, Stargardt T. Changes in launch delay and availability of pharmaceuticals in 30 European markets over the past two decades. BMC Health Serv Res. 2022 Nov 30;22(1):1457.
    9. Verma DD. A comprehensive review of phase IV trials and post-marketing surveillance. International Journal of Pharmacology and Clinical Research 2025; 7(2):207-214.

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  • AI-driven Sentiment Analysis of Patient-Reported Outcomes

    AI-driven Sentiment Analysis of Patient-Reported Outcomes

    Using artificial intelligence (AI) for sentiment analysis of patient-reported outcomes (PROs) is shaping the interpretation of patient voice in the healthcare systems by revolutionizing free-text narratives into systematic signals about emotion, burden, and lived experience. While conventional PRO instruments offer standard measurements, they often condense complex experiences into fixed scales. AI-driven natural language processing (NLP) enables systematic analysis of patient narratives captured through open-ended responses, diaries, and digital tools, thus enhancing understanding beyond statistical scores.(1, 2)

    Sentiment analysis identifies patient language by emotional tone and intensity and associates those emotions to particular elements of care, such as symptoms, communication, or treatment burden. This approach supports usual PRO measures (PROMs) and patient-reported experience measures (PREMs) by adding emotional and contextual gravity, showing how patients experience care rather than just how they rate it. Consequently, sentiment-derived insights can help explain why similar PRO scores may reflect very different patient experiences.(3, 4)

    Sentiment analysis has an important ability to reveal dissatisfaction and distress that may be masked by ceiling effects in standard rating measures. Patients usually report high scores while reporting important concerns in free-text comments, especially around communication and care coordination. NLP-enabled sentiment parameters can distinguish between these details to offer more actionable insight into areas requiring most improvements.(2-4)

    Machine learning developments have enhanced the accuracy of sentiment analysis in healthcare, with transformer-based models adjusted to patient and clinical language. These models are often better than rule-based approaches at capturing context, politeness, and domain-specific terminology, facilitating more consistent identification of subtle emotions, like anxiety, frustration, or disengagement. Therefore, sentiment analysis has become more reliable for use in research and routine care settings.(5, 6)

    Integrating AI-driven sentiment analysis into PRO workflows can support earlier detection of deterioration of quality of life, emotional distress, or disengagement from treatment. Monitoring changes in sentiment over time enables clinicians and health systems to determine emerging issues before they appear in conventional indicators, supporting more timely, patient-centred interventions and quality improvement efforts.(1, 2, 4)

    In practice, AI-driven PRO and PREM systems increasingly combine open-text collection with automated sentiment analysis and visualisation to guide clinical team. Evidence from large patient-experience datasets shows that sentiment scores derived from narratives can be linked meaningfully with validated quality measures, highlighting their value as complementary indicators of care quality.(1, 7)

    Simultaneously, responsible use of sentiment analysis needs careful attention to bias, transparency, and governance. Models may misread culturally specific language or underrepresent certain patient groups, highlighting the need for diverse training data, validation against human judgement, and clear communication about the analysis of patient narratives. Sentiment analysis should complement, not replace, clinical interpretation.(8)

    Largely, integrating AI-enabled sentiment analysis as a continuous listening layer within digital PRO systems provides a scalable way to capture not only the scores but also the experiences of patients. When applied carefully and assessed rigorously, this approach can support patient-centred evidence generation and strengthen care decisions to better reflect patients’ real-world experiences.

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    References

    1. van Buchem MM, Neve OM, Kant IMJ, et al. Analyzing patient experiences using natural language processing: development and validation of the artificial intelligence patient reported experience measure (AI-PREM). BMC Med Inform Decis Mak. 2022 Jul 15;22(1):183.
    2. Khanbhai M, Warren L, Symons J, et al. Using natural language processing to understand, facilitate and maintain continuity in patient experience across transitions of care. International Journal of Medical Informatics. 2022; 157:104642.
    3. Wójcik Z, Dimitrova V, Warrington L, et al. Using artificial intelligence to predict patient outcomes from patient-reported outcome measures: a scoping review. Health Qual Life Outcomes. 2025 Apr 11;23(1):37.
    4. Azarpey A, Thomas J, Ring D, Franko O. Natural Language Processing of Sentiments Identified in Patient Comments Associated with Less Than Top-Rated Care. J Patient Exp. 2025 Mar 21;12:23743735251323677.
    5. Nerella S, Bandyopadhyay S, Zhang J, et al. Transformers and large language models in healthcare: A review. Artif Intell Med. 2024 Aug;154:102900.
    6. Chaudhary A, Pokhrel S, Ganesan S, Shah PB. Drug Review Sentiment Analysis: Applying Transformer-Based Models for Enhanced Healthcare. Journal of Data Science and Intelligent Systems. 2025; 1-11.
    7. Greaves F, Ramirez-Cano D, Millett C, et al. Use of sentiment analysis for capturing patient experience from free-text comments posted online. J Med Internet Res. 2013 Nov 1;15(11):e239.
    8. Saha S. Advancements In Sentiment Analysis: Techniques, Applications, And Future Directions. IJCSPUB. 2024; 14(4):126-159.

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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.

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    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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  • Developing an RWE-Driven Strategy for Formulary and Access Decisions

    Developing an RWE-Driven Strategy for Formulary and Access Decisions

    Strategy developments for providing clinical and economic evidence to support formulary decisions are increasingly adopting carefully planned real-world evidence (RWE). As payers move toward value-based assessments, evidence submissions must go beyond efficacy in trial settings to show treatment impact in routine practice and on resource utilization, and alignment with decision criteria revolving around budget, access, and unmet clinical needs. RWE helps bridge these gaps by supporting the value narrative in real-world behaviours of patients, providers, and systems.(1, 2)

    Clarity on the specific formulary questions the submission aims to impact is the foundation of an effective strategy. Payers are usually interested in knowing where a treatment fits in the pathway, which populations benefit most, how it compares with existing options, and what monitoring or authorization conditions may be necessary.(3) Early strategizing around these questions ensures that RWE generation emphasizes outcomes that may directly impact formulary and pharmacy and therapeutics (P&T) committee decision-making, including comparative effectiveness, safety in different populations, adherence, maintenance, and utilisation patterns.(3-5)

    Selection and validation of applicable real-world data (RWD) sources is the next crucial step.(4) Every source, including claims databases, electronic health records (EHRs), registries, and digital health platforms, offers unique strengths. However, the emphasis should be on ensuring that selected datasets represent actual clinical practice, offer meaningful endpoints, and link to healthcare utilisation and costs. Performing feasibility evaluations, determining data completeness and representativeness, and transparently documenting data provenance reinforce the methodological defensibility of analyses and help payers examine external validity and potential bias.(4)

    Once relevant data sources are chosen, the focus moves to developing fit-for-purpose analyses to support the clinical trial package. Thorough observational methods, including matching techniques, confounding adjustment, and sensitivity analyses, facilitate evaluation of real-world efficacy and safety. Incorporating these results into cost-effectiveness and budget impact models enables reflection of real-world utilisation, uptake, discontinuation patterns into economic evaluations, which further benefits designs. The aim is to convert complex analytics into clear, decision-ready insights that show clinical value, total cost of care propositions, and the expected budget impact under realistic implementation scenarios.(4, 5)

    Ultimately, an efficient submission depends on well-communicated evidence. A comprehensive value story seeks to bridge the gap between payer expectations and disease burden, clinical and patient-relevant outcomes, and economic performance. Communicating RWE within the structure of a standard dossier format, supplementing with clear summaries, transparent assumptions, and specific acknowledgement of limitations, improves credibility. Outlining results around real clinical and operational challenges, such as lower hospitalisations, optimized care pathways, or enhanced adherence, helps place the findings within system preferences rather than abstract metrics.(3-5)

    Finally, an impactful strategy identifies that RWE is not a one-time input but part of continuous evidence generation throughout the product lifecycle. Early and proactive engagement with payers, HTA authorities, clinicians, and patient groups helps facilitate alignment on evidence requirements before the analyses begin. Also, a plan for apprising evidence as new data accumulates supports commitment to monitoring real-world performance and adapting value communications over time. This lifecycle-oriented methodology prioritizes RWE as a dynamic decision support tool that strengthens formulary considerations long after initial submission.

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    References

    1. Alipour-Haris G, Liu X, Acha V, et al. Real-world evidence to support regulatory submissions: A landscape review and assessment of use cases. Clin Transl Sci. 2024; 17(8):e13903.
    2. Gettman D. Using Real-World Evidence Platforms to Guide Formulary Decisions in Health Systems. Conference: Pharmacy Management (PMD714)At: Buffalo, New YorkAffiliation: D’Youville College. Accessed online on 3rd December 2025 at: https://www.researchgate.net/publication/393377304_Using_Real-World_Evidence_Platforms_to_Guide_Formulary_Decisions_in_Health_Systems
    3. Jansen MS, Dekkers OM, le Cessie S, et al. Multiple Perspectives on the Need for Real-World Evidence to Inform Regulatory and Health Technology Assessment Decision-Making: Scoping Review and Stakeholder Interviews. Pharmacoepidemiol Drug Saf. 2025 Jan;34(1):e70074.
    4. Format for formulary submissions. Accessed online on 3rd December 2025 at: https://www.amcp.org/sites/default/files/2019-03/Format%20Version%201%200%20Final%2010.2000.pdf
    5. CIOMS Working Group report. Real-world data and real-world evidence in regulatory decision making. 2020. Accessed online on 3rd December 2025 at: https://cioms.ch/wp-content/uploads/2020/03/CIOMS-WG-XIII_6June2023_Draft-report-for-comment.pdf

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  • Usefulness of Subject Headings for Literature Search in Databases

    Usefulness of Subject Headings for Literature Search in Databases

    Subject headings are essential aspects while developing literature search strategies for databases as they use a controlled vocabulary to describe each item, rather than depending solely on the exact words appearing in titles or abstracts. This means a single subject heading can yield articles with different terms, synonyms, or varied spellings for the same concept, enabling researchers to avoid missed studies that a simple keyword search would skip. By emphasizing conceptual “aboutness” instead of separate word matches, subject headings typically provide more concentrated and relevant results than just the keywords.​(1, 2)

    Subject headings can also process synonyms and ambiguity systematically, which promptly improves both recall and precision in a search. When an indexer assigns an authorized heading from a thesaurus – such as Medical Subject Headings (MeSH) used in Pubmed and other National Library of Medicine (NLM) databases, Emtree used in Embase with its thorough coverage of drugs and biotechnology terminologies, or the more general Library of Congress Subject Headings (LCSH) used across multidisciplinary library catalogues – that single standardized term can substitute for many possible variants, so the searcher does not need to manually list every possible synonym or related phrase.(1) Simultaneously, controlled vocabularies differentiate between various meanings of identical words, reducing irrelevant hits that may appear in a simple, text-word search with an unclear context.​(2, 3)

    Subject heading systems are usually organized hierarchically, facilitating searchers to move efficiently between broader and narrower concepts as per the level of desired exhaustiveness of the search. Features like “exploding” a heading(4) enable users to include specific terms under a broader topic. Similarly, keyword-based tools like ‘wildcards’ (e.g., truncation symbols used in PubMed/Medline and other databases) help detect variations in word endings when searching with text-words, enabling inclusion of all the relevant records despite differences in terminology.(5) Highlighting a major heading or adding qualifiers can narrow the search to documents where the concept is crucial or framed in a specific way. This structural flexibility is particularly beneficial for complex topics, where a researcher may start with a wide net and then iteratively filter the search as understanding evolves.​(3, 4)

    Subject headings are especially important during systematic and other in-depth reviews as they facilitate transparent, reproducible search strategies across huge databases. Guidance for systematic searching typically recommends mixing subject headings with keywords to capture indexed literature through the controlled terms, while very recent or not-yet-indexed records are obtained via text-words. This combined approach maximizes coverage without overwhelming searchers with noise, enabling the inclusion of important studies, while strengthening the methodology for final review.​(6, 7)

    Despite these strengths, subject headings cannot completely replace keyword searches and must be used with the knowledge of database-specific vocabularies and indexing delays. Several databases maintain their own controlled lists; hence, an appropriate term in one resource may not exist or may appear under a different label in another, necessitating the searchers to examine each thesaurus independently. Also, there can be a time lag before new records are allotted subject headings, so counting only on them risks ignoring cutting-edge work, highlighting the need to treat headings and keywords as complementary tools instead of substitutes.​(8, 9)

    Over time, exploring and using subject headings also enables researchers understand a particular topic, improving their searching and thinking abilities. Exploring a thesaurus or the headings assigned to a specifically relevant article can yield narrower, broader, or relevant concepts that were not part of the initial question but turn out to be critical. This way, subject headings not only enhance retrieval but also act as a conceptual map, guiding researchers as they place their own work within existing literature and allowing for their searches to be both strategic and intellectually informed.

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    References

    1. Australian National University. Systematic Reviews in The Sciences. Accessed online on 25th November 2025. Available at: https://libguides.anu.edu.au/c.php?g=916656&p=6713218
    2. Grewal A, Kataria H, Dhawan I. Literature search for research planning and identification of research problem. Indian J Anaesth. 2016 Sep;60(9):635-639.
    3. Lazarinis F. 10 – Subject access: LCSH, Children’s Subject Headings and Sears List of Subject Headings. In: Cataloguing and Classification – An Introduction to AACR2, RDA, DDC, LCC, LCSH and MARC 21 Standards. 2015; Pages 193-209.
    4. Ecker ED, Skelly AC. Conducting a winning literature search. Evid Based Spine Care J. 2010 May;1(1):9-14.
    5. University of Illinois, Chicago. Advanced Search Strategies. Accessed online on 2nd December 2025. Available at: https://researchguides.uic.edu/searchstrategies/truncation
    6. Gusenbauer M, Haddaway NR. Which academic search systems are suitable for systematic reviews or meta-analyses? Evaluating retrieval qualities of Google Scholar, PubMed, and 26 other resources. Res Synth Methods. 2020 Mar;11(2):181-217.
    7. Hausner E, Guddat C, Hermanns T, et al. Prospective comparison of search strategies for systematic reviews: an objective approach yielded higher sensitivity than a conceptual one. Journal of Clinical Epidemiology 2016; 77:118-124.
    8. Oregon State University. IACUC: The 3Rs and the Literature Search. 2025. Accessed online on 25th November 2025. Available at: https://guides.library.oregonstate.edu/c.php?g=286267&p=1906510
    9. University of Toronto. Searching the Literature: A Guide to Comprehensive Searching in the Health Sciences. 2025. Accessed online on 25th November 2025. Available at: https://guides.library.utoronto.ca/c.php?g=577919&p=4305874

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  • Quantifying the Value of Hope in Healthcare Decision-Making

    Quantifying the Value of Hope in Healthcare Decision-Making

    Although considered an intangible emotion, hope has a considerable influence in healthcare outcomes. It impacts treatment choices, guides behaviour, and influences people’s experience of an illness. For someone with a serious health condition, treatment decisions are rarely driven by data alone. Even a small chance of improvement or extra time can provide significant reassurance to patients.(1,2) Quantifying the “value of hope” is an effort to acknowledge this powerful human response in systems that typically emphasize only the numbers and averages.(1-3) Conventional evaluations of a treatment’s value revolve around quantifiable outcomes, such as survival, symptom improvement, quality of life scores, and cost-effectiveness. These metrics are crucial; however, they do not fully depict patients’ perspectives about facing uncertain futures. People often weigh the opportunity of a transformative benefit more seriously than a surefire yet modest improvement. This behaviour is particularly prevalent in conditions with limited treatment choices, where just the hope can impact decision-making as effectively as clinical evidence.(2, 3) Identifying this, health economists and researchers have started exploring ways to integrate hope into value assessments.(3) Rather than looking just at the average expected outcome of a treatment, some methodologies consider the entire scale of possible benefits. Others directly obtain patient preferences through surveys or choice experiments. These approaches do not claim that hope replaces evidence. But they acknowledge the meaningful contribution of hope to people’s assessments of their options. When described responsibly, it provides a more comprehensive picture of what “value” truly means to those living with illnesses.(2-4) In many therapeutic areas, including cancer, rare diseases, and progressive chronic conditions, hope can become a lifeline, motivating patients to stay involved in their care, follow treatments, or choose new options. Even the knowledge that a treatment can possibly bring about a significant change can enhance emotional resilience and perceived well-being. If this dimension is ignored, there is a risk of devaluing treatments that are significant to patients and families.(1-3) Measuring hope itself is difficult, but the importance of quantifying hope was realised long back. Standardized scales have been designed to facilitate this quantification: for instance, the Herth Hope Index (HHI) was developed and validated in the early 90s.(5) Nevertheless, empirical evidence on the correlation between hope, healthcare utilization, and outcomes is nuanced, and not all studies establish a direct link between higher hope and enhanced health expenditure or survival. This highlights the need for careful consideration in understanding and applying hope-related data in economic and overall health-related decision-making.​(6, 7) Quantifying the value of hope during conventional decision-making requires robust research, ethical considerations, and vigorous methods that balance patient perspectives with evidence-based assessments. Yet the growing interest indicates a change toward more human-centred health policy. With the ever evolving healthcare landscape, quantifying hope is not just an academic exercise, but a step toward recognizing the full impact of medical innovation. By considering hope as part of the value equation, the landscape can move towards healthcare that respects what patients truly pursue – the possibility of a better future. Become A Certified HEOR Professional – Enrol yourself here! References
    1. Clarke S, Oakley J. Where There’s Hope, There’s Life 1 : On the Importance of Hope in Health Care. J Med Philos. 2025; 50(1):13-24.
    2. Berntzen H, Rustoen T, Kyno NM. “Hope at a crossroads” – Experiences of hope in intensive care patients: A qualitative study. Australian Critical Care. 2024; 37(1):120-126.
    3. Reed SD, Yang JC, Gonzalez JM, Johnson FR. Quantifying Value of Hope. Value Health. 2021; 24(10):1511-1519.
    4. Hong J, Bae EY, Yu S. The Value of Hope in Cancer Care: Risk Preference and Heterogeneity in Cancer Patients and the General Public. Value Health. 2025; 28(8):1259-1267.
    5. Herth K. Abbreviated instrument to measure hope: development and psychometric evaluation. J Adv Nurs. 1992; 17(10):1251-9.
    6. Chay J, Huynh VA, Cheung YB, et al. The relationship between hope, medical expenditure and survival among advanced cancer patients. Front Psychol. 2023; 14.
    7. Fukuhara S, Kurita N, Wakita T, et al. A scale for measuring health-related hope: its development and psychometric testing. Annals of Clinical Epidemiology. 2019; 3.

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  • Evidence Syntheses and Value Assessment of Digital Health Interventions

    Evidence Syntheses and Value Assessment of Digital Health Interventions

    Digital health interventions (DHIs) are changing the care delivery and experience. However, the speed of innovation often overtakes the ability to assess what truly works. Therefore, evidence syntheses and value assessments are crucial in helping researchers, clinicians, and policymakers identify solutions that deliver meaningful impact.(1, 2)

    Evidence from systematic reviews has frequently shown DHIs to exhibit strong usability, safety, acceptability, and favourable clinical advantages, especially when rooted in user-centred design and supplemented by human interaction. However, evidence for long-term efficacy, cost-effectiveness, and scalable real-world application continues to be varied. This inconsistency highlights the need for context-aware assessments, which also focus on user experience, equity, and workflow integration as much as measured outcomes.(1, 3, 4)

    For clarity, researchers are increasingly turning to umbrella reviews, rapid assessments, and scoping reviews, and evidence gap maps (EGMs) to synthesize what is known and identify where major gaps persist.(5) These syntheses also underscore persisting challenges, including heterogeneous study designs, variable outcome measures, and diverse intervention components, which restrict comparability across studies. Consequently, modern assessments emphasize systematized reporting, transparent risk-of-bias evaluations, and alignment with the complete patient journey.(4, 6)

    Value assessment is central to digital health decision-making. Beyond clinical outcomes, value includes user-friendliness, cost-effectiveness, equity, and system-level advantages. Although frameworks categorize these components, they are not uniformly applied in empirical research, which warrants practical measures and economic models that depict real-world conditions.(4, 6)

    The success of DHIs also depends on engagement and adherence; however, engagement doesn’t always guarantee better outcomes.(6, 7) Effective solutions must alleviate burden, fit seamlessly into daily routines, and sustain user motivation, elements that are increasingly being captured in contemporary evidence syntheses.(4-6)

    Furthermore, economic evaluation of DHIs adds another layer of complexity. Digital tools are associated with unique costs, including development, maintenance, interoperability, cybersecurity; while also including benefits that grow over time. Standardized methods, scenario analyses, and sensitivity testing are crucial for generating credible, policy-relevant insights.(8, 9)

    Implementing DHIs is a crucial bridge between evidence and practice. EGMs and implementation frameworks help pinpoint where robust research exists and where more work is needed to strengthen adoption, reliability, and sustainability.(4, 5) Quality, safety, and equity are equally non-negotiable components. Systematic reviews increasingly highlight privacy and security risks and explore differential effects across sociodemographic groups to ensure digital tools do not increase disparities. Transparent reporting and robust appraisal improve trust that guide responsible decision-making.(1, 2)

    Finally, consistent, meaningful assessments will help stakeholders recognize high-value digital health solutions while facilitating safe, equitable, and impactful innovation. With evolving methods, evidence will continue to shape, not just validate, the future of digital health.

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    References

    1. Merino M, Del Barrio J, Nuño R, Errea M. Value-based digital health: A systematic literature review of the value elements of digital health care. Digit Health. 2024; 10:20552076241277438.
    2. Liu S, Ma J, Sun M, et al. Mapping the Landscape of Digital Health Intervention Strategies: 25-Year Synthesis. J Med Internet Res. 2025; 27:e59027.
    3. Rauschenberg C, Schick A, Hirjak D, et al. Evidence Synthesis of Digital Interventions to Mitigate the Negative Impact of the COVID-19 Pandemic on Public Mental Health: Rapid Meta-review. J Med Internet Res. 2021; 23(3):e23365.
    4. Milne-Ives M, Homer SR, Andrade J, Meinert E. Mapping the Process of Engagement With Digital Health Interventions: A Cross-Case Synthesis. Mayo Clin Proc Innov Qual Outcomes. 2025; 9(3):100625.
    5. Campbell, F., Tricco, A.C., Munn, Z. et al. Mapping reviews, scoping reviews, and evidence and gap maps (EGMs): the same but different – the “Big Picture” review family. Syst Rev. 2023; 12:45.
    6. Iwakura M, Ozeki C, Jung S, et al. An umbrella review of efficacy of digital health interventions for workers. npj Digit. Med. 2025; 8:207.
    7. Sun S, Simonsson O, McGarvey S, et al. Mobile phone interventions to improve health outcomes among patients with chronic diseases: an umbrella review and evidence synthesis from 34 meta-analyses. The Lancet Digital Health. 2024; 6(11):e857-e870.
    8. Gomes M, Murray E, Raftery J. Economic Evaluation of Digital Health Interventions: Methodological Issues and Recommendations for Practice. Pharmacoeconomics. 2022; 40(4):367-378.
    9. Wilkinson T, Wang M, Friedman J, Görgens M. A Framework for the Economic Evaluation. Policy Research Working Paper; World Bank Group. 2023. Accessed online at: https://documents1.worldbank.org/curated/en/099446504122313917/pdf/IDU0f639726d0f11404a3509af8054677649dcd6.pdf

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  • Advancing Health Technology Assessment Through R Adoption and Standardization

    Advancing Health Technology Assessment Through R Adoption and Standardization

    The increasing implementation of the R programming language in health technology assessments (HTAs) is the result of the need for transparency, reproducibility, and efficiency in health economic modelling. Unlike conventional excel-based tools, R provides a completely script-based setting that records every step of the modelling process, from data import to simulation and reporting. This improves auditability and error reduction while also enabling smooth automation, making R perfectly suitable to cater to the rising demand for “living HTAs” that evolve with new evidence.(1, 2)

    HTA bodies, including NICE (the UK) and ZIN (the Netherlands) are adopting R-based submissions, indicating rising institutional confidence in open-source, code-driven methodologies.(3) The ability of R programming manage multifaceted systems, incorporate version control, and automate analyses is changing how HTAs are performed. Academic and industry partnerships are creating shared frameworks and toolkits to further simplify these processes, facilitating consistent, transparent, and faster decision-making.(1, 3)

    Standardisation is the most crucial factor of R adoption. For this, validated and reusable modelling frameworks are being developed to help regulators.(4) Initiatives like the open-source assertHE package integrate validation and quality checks right into modelling workflows, supporting built-in verification rather than retrospective review.(5) These frameworks reduce review time, enhance reproducibility, and facilitate efficient model adaptation across markets, thus striking a balance between innovation and rigour. The growing number of health economists equipped with R expertise further reinforces this ecosystem, shifting toward code-based submissions that are easier to review, update, and share.(1, 4, 5)

    The move toward standardisation also facilitates scalability in global HTAs. R also facilitates country-specific modifications through modular inputs rather than structural model changes, maintaining consistency across jurisdictions. Shared code sources, scenario templates, and uniform data structures are helpful in cross-country comparisons, making them more reliable and less resource-intensive.(1, 3, 4)

    R’s versatility goes beyond modelling efficiency to support real-world data (RWD) and artificial intelligence (AI) integration, which are the crucial pillars of HTA evidence bases. R’s capacity for secure data handling, API-based automation, and remote computation enables models to advance dynamically while maintaining data privacy. However, challenges, especially about data quality, interoperability, and achieving methodological agreement across agencies, persist. Overcoming these warrants collective participation from academia, regulators, and industry to establish shared standards and training guidance.(2, 3, 6)

    Finally, the implementation and standardisation of R signify a critical step towards making HTAs more transparent, reproducible, and globally aligned. By adopting open-source technology and collaborative validation, R is transforming the assessment of health technologies, taking HTA from static assessments into dynamic, data-driven systems that adapt to evidence and policy needs of the real-world.

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    References

    1. Smith RA, Schneider PP, Mohammed W. Living HTA: Automating Health Economic Evaluation with R. Wellcome Open Res. 2022; 11(7):194.
    2. R Consortium. R for Health Technology Assessment (HTA): Identifying Needs, Streamlining Processes, Building Bridges. Accessed online on 10th November 2025. Available at: https://r-consortium.org/posts/r-for-health-technology-assessment-hta-identifying-needs-streamlining-processes-building-bridges/
    3. Poerrier JE, Ettinger J, Bergemann R. R in HEOR modelling for HTA submissions: An assessment. Accessed online on 10th November 2025. Available at: https://www.parexel.com/application/files/2917/2729/8142/FY24_R_in_HEOR_Modelling_White_Paper_09-2024_v3.pdf
    4. Thokala, P., Srivastava, T., Smith, R. et al. Living Health Technology Assessment: Issues, Challenges and Opportunities. PharmacoEconomics. 2023; 41:227–237.
    5. Smith RA, Samyshkin Y, Mohammed W, et al. assertHE: an R package to improve quality assurance of HTA models. [version 1; peer review: 1 approved, 1 approved with reservations]. Wellcome Open Res. 2024; 9:701.
    6. Zisis K, Pavi E, Geitona M, Athanasakis K. Real-world data: a comprehensive literature review on the barriers, challenges, and opportunities associated with their inclusion in the health technology assessment process. J. Pharm. Pharm. Sci. 2024; 27:12302.

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