Value in Health最新文献

{"title":"Face Validation of an Artificial Intelligence Driven Tool for Clinical Triaging in Australian Public Oral Healthcare: A Pilot Study","authors":"Tan Minh Nguyen MPH, MSc ,&nbsp;Vito Carlo Alberto Caponio PhD ,&nbsp;Ravikumar Rajappa DDS ,&nbsp;Daniel Lawrence BAppSc(Phys) ,&nbsp;Youstina Tawadros BOHSc ,&nbsp;Bree Jones MPH ,&nbsp;Keegan Crow MBA ,&nbsp;Nicola Cirillo PhD","doi":"10.1016/j.jval.2025.12.001","DOIUrl":"10.1016/j.jval.2025.12.001","url":null,"abstract":"<div><h3>Objectives</h3><div>The adoption of artificial intelligence (AI) technology holds significant potential to drive innovation for value-based healthcare for Victorian public oral healthcare, Australia. This pilot study compared the clinical triage decision outcome of experienced dentists in prioritizing consumers with urgent dental care at Your Community Health, a community health service in Victoria, Australia, with those generated by the CoTreat AI-driven tool (CoTreat Pty Ltd, Australia).</div></div><div><h3>Methods</h3><div>Consecutive sampling of consumers were recruited for the 6-month pilot. Five clinical criteria were established to determine consumers with urgent care: (1) requires urgent extraction causing loss of upper or lower anterior teeth, (2) multiple teeth (≥15% of remaining dentition) with moderate/extensive dental caries, (3) abscessed tooth/teeth, (4) root caries, and (5) missing upper and lower anterior teeth. Face validation was undertaken to compare the agreement between the triage output returned by CoTreat (index test) and 2 experienced dentists (reference standard) in determining urgent care.</div></div><div><h3>Results</h3><div>A total of 173 consumers were triaged in this pilot study (mean age 61.9 years [SD 18.]). One-third (<em>n</em> = 57) were identified needing urgent care. CoTreat achieved 98.3% agreement with dentist clinical triage outcome (Cohen’s k = 0.96), 100% sensitivity, and 97.5% specificity, with positive predictive value of 94.7% and negative predictive value of 100%. The most common triage criteria were missing anterior teeth (60.3%) and abscesses (20.7%).</div></div><div><h3>Conclusions</h3><div>CoTreat demonstrated excellent agreement with dentists’ clinical triage decisions. AI-driven tools, such as CoTreat, can advance value-based healthcare implementation by identifying consumers with urgent care needs for Victorian public oral healthcare.</div></div>","PeriodicalId":23508,"journal":{"name":"Value in Health","volume":"29 4","pages":"Pages 589-594"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145757452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Heatwaves on Health-Related Quality of Life in Older Chinese Adults: A Longitudinal Cohort Study","authors":"Yilin Zhang MSc ,&nbsp;Yan Lin MSc ,&nbsp;Zhihong Xiao MSc ,&nbsp;Yifeng Chen PhD ,&nbsp;Quan Zhou PhD ,&nbsp;Shuling Kang PhD ,&nbsp;Zhihao Yang PhD ,&nbsp;Fanni Rencz PhD ,&nbsp;Nan Luo PhD ,&nbsp;Jianjun Xiang PhD","doi":"10.1016/j.jval.2025.11.005","DOIUrl":"10.1016/j.jval.2025.11.005","url":null,"abstract":"<div><h3>Objectives</h3><div>Climate change has intensified heatwaves, posing significant health risks to older adults; yet, their impact on health-related quality of life (HRQoL) in China’s aging population remains underexplored. This study investigates the impact of heatwaves on HRQoL among older adults using the EuroQol 5-Dimension 5-Level (EQ-5D-5L).</div></div><div><h3>Methods</h3><div>A cohort of community-dwelling Chinese adults aged ≥60 years were followed up from spring to fall in 2023. Univariate regression and restricted cubic spline models analyzed linear and nonlinear relationships between meteorological/demographic factors and EQ-5D-5L outcomes (utility values and EuroQol Visual Analog Scale [EQ VAS] scores). Machine learning models identified key predictors of heatwave-related HRQoL changes.</div></div><div><h3>Results</h3><div>Among the 627 participants, both EQ-5D-5L utility values and EQ VAS scores followed a rise-and-fall pattern, initially increasing during the early heatwave period (T1) but subsequently declining significantly during the peak heatwave period (T2) (<em>P</em> &lt; .001). Underweight individuals had lower utility values (<em>β</em> = −0.394, <em>P =</em> .006), whereas obese individuals had lower EQ VAS scores (<em>β</em> = −0.385, <em>P =</em> .040). Age was negatively associated with both utility values (<em>β</em> = −0.003, <em>P &lt;</em> .001) and EQ VAS scores (<em>β</em> = −0.228, <em>P &lt;</em> .001). Meteorological factors, such as daily average relative humidity and temperature significantly affected both EQ-5D-5L utility values and EQ VAS scores (<em>P &lt;</em> .05). The Support Vector Machine model outperformed the other machine learning models, identifying body mass index as the most influential variable in predicting the EQ-5D-5L utility values, followed by age, preretirement occupation, education level, and daily average relative humidity.</div></div><div><h3>Conclusions</h3><div>Heatwaves dynamically affect HRQoL in older adults in China, with an initial increase followed by a significant decline. Older, underweight, and less-educated adults are more vulnerable to the adverse impacts of heatwaves on HRQoL.</div></div>","PeriodicalId":23508,"journal":{"name":"Value in Health","volume":"29 4","pages":"Pages 668-677"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145688481","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Forward Agenda for Digital Health Technologies: Evidence That Works for Decisions","authors":"Axel Mühlbacher PhD ,&nbsp;Volker Amelung PhD ,&nbsp;Katarzyna Kolasa PhD","doi":"10.1016/j.jval.2026.02.007","DOIUrl":"10.1016/j.jval.2026.02.007","url":null,"abstract":"","PeriodicalId":23508,"journal":{"name":"Value in Health","volume":"29 4","pages":"Pages 547-548"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147366722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Economic Evaluations of Orphan Drugs for Rare Kidney Diseases in Low- and Middle-Income Countries: A Bibliometric Systematic Review With Policy and Evidence Gaps Analysis","authors":"Mohammed Alfaqeeh MSc ,&nbsp;Auliya A. Suwantika PhD ,&nbsp;Maarten J. Postma PhD ,&nbsp;Rizkia Andicha Putra BPharm ,&nbsp;Jasmine Rani Aisyah MPharm ,&nbsp;Fima Perdani Rahayu BPharm ,&nbsp;Lubna Farhana MPharm ,&nbsp;Muhammad Ilyas BPharm ,&nbsp;Shofuro Sholihah BPharm ,&nbsp;Neily Zakiyah PhD","doi":"10.1016/j.jval.2025.12.008","DOIUrl":"10.1016/j.jval.2025.12.008","url":null,"abstract":"<div><h3>Objectives</h3><div>We conducted a systematic review and bibliometric analysis to explore the economic evaluation methods, outcomes, trends, and geographical distribution of orphan drugs for rare kidney diseases (RKDs) in low- and middle-income countries over the past decade.</div></div><div><h3>Methods</h3><div>A systematic review was conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Studies published from 2014 to 2024 were identified in PubMed and Scopus using keywords related to orphan drugs and RKDs. Eligible studies included full economic evaluations in low- and middle-income countries, reporting outcomes, such as incremental cost-effectiveness ratio per quality-adjusted life-years gained, life-years gained, and other clinical outcomes. Study quality was assessed using the Consolidated Health Economic Evaluation Reporting Standards checklist. Bibliometric analysis was performed with VOSviewer and Tableau.</div></div><div><h3>Results</h3><div>Among 406 identified studies, 16 met inclusion criteria. Cost-utility analysis was most common (88%), followed by cost-effectiveness (6%) and cost-minimization (6%). Most studies adopted a healthcare system (56%) or societal (19%) perspective. The majority used life-years and quality-adjusted life-years as outcome measures and considered direct medical costs, with 63% applying both probabilistic and deterministic sensitivity analyses. Reporting quality was high (87.5% rated as good), although gaps remained in heterogeneity, distributional effects, and funding disclosure. Overall, 63% found orphan drugs for RKDs cost-effective. Bibliometric analysis highlighted themes such as “cost-effectiveness,” “advanced renal cell carcinoma,” and “nivolumab.” Geographically, 67% of studies were conducted in China.</div></div><div><h3>Conclusions</h3><div>This review unexpectedly found that many studies identified orphan drugs for RKDs as cost-effective, despite their traditionally high costs. Future evaluations should refine methods to better capture long-term value and affordability in low- and middle-income countries.</div></div>","PeriodicalId":23508,"journal":{"name":"Value in Health","volume":"29 4","pages":"Pages 678-689"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145844033","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Effect of Perspective, Duration, and Views on Death on the Valuation of Severe Health States","authors":"Zhongyu Lang MSc ,&nbsp;Stefan A. Lipman PhD ,&nbsp;Bram Roudijk PhD ,&nbsp;Peep Stalmeier PhD ,&nbsp;Arthur E. Attema PhD","doi":"10.1016/j.jval.2025.11.004","DOIUrl":"10.1016/j.jval.2025.11.004","url":null,"abstract":"<div><h3>Objectives</h3><div>Composite time trade-off utilities typically vary by perspective: adults tend to assign lower utilities to health states for themselves (self-perspective) than for children (proxy-perspective). Utilities also differ between proxy-perspectives: i.e., deciding for a child (proxy1) vs imagining what a child wants (proxy2). This can cause health states to be evaluated as better than dead (BTD) under one perspective, yet worse than dead (WTD) under another. We investigate how such WTD preferences vary with health states across durations and perspectives, alongside sociocultural factors such as religion and attitudes toward death. As a secondary analysis, we explore the maximal endurable time hypothesis to assess the consistency of WTD preferences.</div></div><div><h3>Methods</h3><div>We surveyed 1000 UK respondents, stratified into 2 groups by religious affiliation (religious vs nonreligious). Each completed BTD tasks from a self-perspective and a randomly assigned proxy perspective (proxy1 or proxy2), involving 5 health states across 4 durations. Respondents also ranked all health states (without duration) and immediate death.</div></div><div><h3>Results</h3><div>In ranking tasks without duration, WTD preferences were unaffected by perspective or religious affiliation, whereas the proportion of states considered WTD increased with longer durations in paired comparisons in the BTD tasks among nonreligious participants. WTD preferences were associated with stronger support for euthanasia and lower religiousness. Maximal endurable time preferences showed minimal differences across perspectives or religious affiliations.</div></div><div><h3>Conclusions</h3><div>Perspectives, duration, religious affiliation, and views on euthanasia shape WTD preferences. Future research is encouraged to consider religion in sampling and how to deal with the potential duration dependency of QALY-anchored utilities.</div></div>","PeriodicalId":23508,"journal":{"name":"Value in Health","volume":"29 4","pages":"Pages 659-667"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145640199","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Qini Curves for Potential Impact Assessment of Risk Predictive Models Informing Intervention Policies","authors":"Pierpaolo Palumbo PhD","doi":"10.1016/j.jval.2025.01.024","DOIUrl":"10.1016/j.jval.2025.01.024","url":null,"abstract":"<div><h3>Objectives</h3><div>Predictive models in medicine help make decisions about which individual to treat with a given therapeutic or preventive intervention. Before being tested in large field studies and recommended for clinical adoption, it is important to evaluate not only their statistical accuracy but also the impact they may have when used to inform health intervention policies. We aim to provide simple methods for the potential impact assessment of health intervention policies based on predictive models.</div></div><div><h3>Methods</h3><div>We propose an analytic framework based on Qini curves wherein prediction-based policies are analyzed on 2 impact endpoints: (1) the fraction of the population that would be selected for the intervention (coverage) and (2) the effect on the clinical outcomes of interest (disutility). The drivers of values are the disease prevalence, the predictive performance of the model, and the effectiveness of the intervention.</div></div><div><h3>Results</h3><div>We present simple formulas for calculating coverage and disutility from either observational or randomized controlled data. We illustrate possible value measures arising from geometrical properties on the Qini plane: delta coverage and disutility, number needed to treat, and integrated difference between Qini curves. We show the applicability of the Qini analysis by providing examples about the prevention of falls in older adults and prevention of secondary cardiovascular events with pioglitazone.</div></div><div><h3>Conclusions</h3><div>Coverage and disutility capture key value components of prediction-based policies. The method can be used for comparing models or tuning risk thresholds for managing trade-offs between conflicting objectives (eg, clinical benefits, side effects, and healthcare resources).</div></div>","PeriodicalId":23508,"journal":{"name":"Value in Health","volume":"29 4","pages":"Pages 567-574"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143426307","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Systematic Review of the Methods and Quality of Economic Evaluations for Artificial-Intelligence-Assisted Cancer Screening or Diagnosis","authors":"Yuyanzi Zhang BS ,&nbsp;Lei Wang MS ,&nbsp;Yifang Liang BS ,&nbsp;Annushiah Vasan Thakumar PhD ,&nbsp;Hongfei Hu MS ,&nbsp;Yan Li MS ,&nbsp;Aixia Ma PhD ,&nbsp;Hongchao Li PhD ,&nbsp;Luying Wang PhD","doi":"10.1016/j.jval.2025.10.014","DOIUrl":"10.1016/j.jval.2025.10.014","url":null,"abstract":"<div><h3>Objectives</h3><div>To address the methodological challenges in health economic evaluations (HEEs) of artificial intelligence (AI), this study systematically reviewed AI-assisted cancer screening or diagnosis HEEs, focusing on their methodologies and reporting quality, and providing advice for future HEEs.</div></div><div><h3>Methods</h3><div>We systematically searched 5 databases from inception to December 2024 following Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines. Two researchers independently screened studies, extracted data, and performed a descriptive analysis. The reporting quality of the selected studies was assessed using the Consolidated Health Economic Evaluation Reporting Standards for Interventions That Use Artificial Intelligence (CHEERS-AI) and the Philips checklist.</div></div><div><h3>Results</h3><div>A total of 17 studies evaluating AI-assisted screening or diagnosis across 8 cancer types were included. Artificial intelligence was primarily applied to enhance the sensitivity or specificity of the cancer screening or diagnosis process. The Markov model was the most frequently used, with cohort simulation remaining the most popular simulation method. Findings from the main analyses suggested cost-effectiveness, although sensitivity analyses indicated inconsistent results. Regarding quality, the Philips checklist indicated omissions in general modeling elements such as half-cycle correction, data quality assessment, and subgroup analysis. Meanwhile, the CHEERS-AI assessment revealed that AI-specific items were not adequately addressed, particularly the measurement and modeling of AI learning over time, population differences, and implementation aspects.</div></div><div><h3>Conclusions</h3><div>A limitation was the considerable heterogeneity in reporting quality among the included HEEs. AI-specific items in HEEs should be more comprehensively addressed in line with the CHEERS-AI checklist.</div></div>","PeriodicalId":23508,"journal":{"name":"Value in Health","volume":"29 4","pages":"Pages 690-700"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145534189","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Implications of Incorporating Environmental Sustainability Into Health Technology Assessment for Digital Health Technologies","authors":"Rossella Di Bidino PhD ,&nbsp;Abhirup Dutta Majumdar MSc ,&nbsp;Melissa Pegg MSc ,&nbsp;Ronan Mahon PhD ,&nbsp;Sara Consilia Papavero MSc ,&nbsp;Debjani Mueller PhD","doi":"10.1016/j.jval.2026.01.019","DOIUrl":"10.1016/j.jval.2026.01.019","url":null,"abstract":"<div><div>Health technology assessment (HTA) must increasingly incorporate environmental sustainability (ES) to ensure digital health technologies (DHTs) deliver true value for both population and planetary health. Existing HTA frameworks inadequately capture the upstream and downstream environmental implications of DHTs, overlooking critical factors such as energy consumption, data storage, water usage, and electronic waste. Using a few examples on telehealth platforms, electronic health records, and artificial intelligence–driven diagnostic tools, we illustrate how these technologies can reduce carbon emissions and other pollutants by limiting patient travel and optimizing resource use. We review current HTA frameworks, identify ongoing initiatives, and highlight gaps and challenges in integrating ES into value assessment. Traditional HTA models provide limited guidance for incorporating broad environmental factors, risking underestimation of DHTs’ environmental impacts and potentially undermining health systems’ net-zero commitments by 2050. To address these issues, in this commentary, we propose targeted investment in frameworks, streamlined environmental data collection, and stronger cross-sector collaboration. Systemic inclusion of ES can reduce inequalities, support ethical supply chains, and incentivize developers to design lower-impact technologies, positioning HTA as a driver of sustainable digital health innovation. By embedding environmental metrics, health systems can better balance clinical benefits, economic efficiency, and ecological responsibility, thereby advancing both human and planetary health.</div></div>","PeriodicalId":23508,"journal":{"name":"Value in Health","volume":"29 4","pages":"Pages 595-599"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146181960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Exploring Methods to Include Carbon Emissions Into a Health Technology Assessment: The Case of Remote Patient Monitoring","authors":"Sophia L. Kingma MD ,&nbsp;Egid M. van Bree MD ,&nbsp;Maureen P.M.H. Rutten-van Mölken ,&nbsp;Maarten J. IJzerman","doi":"10.1016/j.jval.2025.11.007","DOIUrl":"10.1016/j.jval.2025.11.007","url":null,"abstract":"<div><h3>Objectives</h3><div>To examine how 2 methodological approaches can be applied to incorporate environmental outcomes in Health Technology Assessment, using remote patient monitoring (RPM) versus usual care (UC) after cardiac surgery.</div></div><div><h3>Methods</h3><div>We reanalyzed data from an observational cohort (<em>N</em> = 730). Outcomes over 3 months included healthcare utilization, quality of life, patient satisfaction, costs (societal perspective), and carbon emissions, quantified by a life cycle assessment. In extended economic evaluation, emissions were monetized (€0.13/kg CO₂eq; scenario analysis up to €0.21/kg) and added to costs. In multicriteria decision analysis (MCDA), 6 criteria including environmental impact were evaluated with Analytical Hierarchy Process, using stakeholder valuations.</div></div><div><h3>Results</h3><div>RPM generated higher carbon emissions than UC (90.7 vs 55.4 kg CO₂eq per patient), driven by device production, partly offset by reduced unplanned care, outpatient visits, and patient travel. Despite higher emissions, RPM remained cost-saving (€102 per patient) with a positive incremental net monetary benefit (€42); only 1% of cost difference was attributable to emissions. In the MCDA, RPM outperformed UC in 5 of 6 criteria, with UC only favored on environmental impact. Stakeholders assigned moderate weight to the environmental criterion, resulting in a higher overall value score for RPM (0.649 vs 0.351).</div></div><div><h3>Conclusions</h3><div>Both methods were feasible and, despite RPM’s higher carbon emissions, pointed to the same conclusion. Economic evaluation had little impact at current carbon prices, whereas MCDA allowed explicit sustainability considerations but is sensitive to subjective choices. Overall, environmental outcomes may be better addressed through deliberative Health Technology Assessment processes, with broader policy instruments key to reducing healthcare’s carbon emissions.</div></div>","PeriodicalId":23508,"journal":{"name":"Value in Health","volume":"29 4","pages":"Pages 641-649"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145669647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From Consensus to Implementation: Advancing Patient-Centered Health Technology Assessment","authors":"Lharra Mae C. Postrano PhD","doi":"10.1016/j.jval.2025.11.022","DOIUrl":"10.1016/j.jval.2025.11.022","url":null,"abstract":"","PeriodicalId":23508,"journal":{"name":"Value in Health","volume":"29 4","pages":"Pages 706-707"},"PeriodicalIF":6.0,"publicationDate":"2026-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146097375","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}