Journal of managed care & specialty pharmacy最新文献

{"title":"Key considerations when assessing clinical utility for biomarker testing: A checklist.","authors":"Tianyi Wang, Kimberly Tsai, Steven S Kheloussi, Dana McCormick, Pamala A Pawloski","doi":"10.18553/jmcp.2026.32.9-b.s4","DOIUrl":"10.18553/jmcp.2026.32.9-b.s4","url":null,"abstract":"<p><p>Establishing the clinical utility of biomarker tests is a critical component of benefit design. However, payers and laboratory benefit manager organizations may differ in how they evaluate clinical utility for a given test, including how they determine whether evaluated outcomes are clinically meaningful. These differences can create challenges for providers and manufacturers when navigating coverage policies across organizations. Based on expert insights from a partnership forum hosted by AMCP on June 24-25, 2025, in Alexandria, VA, on the topic of precision medicine in oncology, we developed a checklist of considerations to align payers and laboratory benefit managers around a shared, actionable approach to evaluating the clinical utility of biomarker tests and the types of evidence used to demonstrate it. The checklist begins with confirmation that analytical and clinical validity have been established before assessing clinical utility. Tests should then be categorized based on their intended use and whether they provide actionable information that informs clinical decision-making and improves patient health outcomes. Assessment of clinical utility includes consideration of the relevance, strength, and consistency of the supporting evidence base. After clinical utility is established, economic and operational considerations may also be evaluated. This checklist is intended to support a more transparent and structured approach to evaluating clinical utility in coverage decision-making.</p>","PeriodicalId":16170,"journal":{"name":"Journal of managed care & specialty pharmacy","volume":"32 9-b Suppl","pages":"S25-S33"},"PeriodicalIF":3.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13452049/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148697494","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"AMCP Market Insights: Optimizing managed care approaches to treatment-resistant depression.","authors":"Bridget Flavin, Gary Small, Charles Dragovich, Charles Dragovich, Ryan Haumschild, Terry Richardson, Michael H Shannon","doi":"10.18553/jmcp.2026.32.9-a.s1","DOIUrl":"10.18553/jmcp.2026.32.9-a.s1","url":null,"abstract":"<p><p>Treatment-resistant depression (TRD) is a subset of major depressive disorder (MDD) most commonly defined as failure to respond to 2 or more antidepressant regimens despite adequate dose, duration, and adherence to treatment. The burden of TRD impacts individuals, families, and society, with outcomes such as decreased health-related quality of life and increased health care utilization and costs. To address the unmet need in many mental health conditions, psychedelic-assisted treatment such as psilocybin for TRD is emerging as a potential option. To discuss optimizing managed care approaches to TRD including the use of psilocybin, AMCP Market Insights virtually convened an expert panel of managed care stakeholders in March 2026. This article provides a qualitative summary of the panel discussion along with key insights and suggested payer practices meant to support informed coverage decisions and guide future work such as collaboration, research, and advocacy. Key insights underscore that there is a high unmet need in TRD and that mitigating barriers to care is a priority for payers. Perspectives on the current evidence for psilocybin in TRD are mixed; payers want additional data to compare treatments, elucidate the appropriate treatment population, and assess cost-effectiveness. Additionally, a primary challenge with psilocybin treatment will be an adequate network of mental health professionals to oversee administration and observation and to provide post-treatment care. Examples of suggested payer practices include education, collaboration, prioritizing behavioral health network readiness and quality, and mitigating barriers to care.</p>","PeriodicalId":16170,"journal":{"name":"Journal of managed care & specialty pharmacy","volume":"32 9-a Suppl","pages":"S1-S9"},"PeriodicalIF":3.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13520776/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148829374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Artificial intelligence-enabled causal estimate of Medicare drug plan integration in cancer care: A doubly robust machine learning instrumental variable analysis.","authors":"Xiangxiang Jiang, Jayda Snipes, Jun Wu, Minghui Li, Jing Yuan, Z Kevin Lu","doi":"10.18553/jmcp.2026.32.9.1101","DOIUrl":"10.18553/jmcp.2026.32.9.1101","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Artificial intelligence (AI) methods are increasingly used to strengthen policy evaluation in managed care pharmacy. Among Medicare beneficiaries with cancer, which is one of the most clinically complex and costly populations, prescription drug coverage is obtained through either integrated Medicare Advantage Prescription Drug plans (MA-PDs) or stand-alone Prescription Drug Plans (PDPs). However, causal evidence of plans' impact remains limited because of nonrandom enrollment.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To apply an AI-enabled causal inference framework to estimate the causal effect of PDP vs MA-PD enrollment on health care utilization and spending among Medicare beneficiaries with cancer.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a nationally representative analysis among patients with cancer aged 65 years or older using Medicare Current Beneficiary Survey data linked to Medicare claims, supplemented with data from the Area Health Resources Files from 2019 to 2022. Outcomes included annual inpatient, outpatient, and prescription drug events, as well as total, Medicare, and out-of-pocket (OOP) expenditures (inflation-adjusted to 2025 USD). Guided by the National Institute on Aging Health Disparities Framework, 63 multidimensional covariates were incorporated. To address nonrandom plan selection, we implemented conventional regression, two-stage residual inclusion (2SRI) instrumental variables (IVs), and an AI-enabled Doubly Robust Machine Learning IV (DML-IV) approach. The IVs in this study included the county-level PDP penetration rate and the percentage of white-collar workers.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;A total of 3,140 unweighted patients with cancer, corresponding to 22,207,248 weighted patients, were included, with 51.20% enrolled in PDP. For health care use, the naive model showed higher inpatient (incident rate ratio [IRR] = 1.30) and outpatient events (IRR = 1.86) among PDP enrollees; after 2SRI adjustment, only outpatient events remained significant (IRR = 1.56), and no utilization differences were significant in the DML-IV model. For health care costs, the naive model indicated higher total (cost ratio = 1.69), Medicare (cost ratio = 9.94), and OOP spending (cost ratio = 1.63). In the 2SRI model, total (cost ratio = 1.35), Medicare (cost ratio = 5.81), and OOP costs (cost ratio = 1.86) remained elevated. In the DML-IV model, total costs were no longer significant, whereas Medicare (cost ratio = 4.14) and OOP costs (cost ratio = 2.18) remained significantly higher.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;After rigorous AI-enabled causal adjustment, differences in health care utilization and costs between PDP and MA-PD plans largely reflect enrollment selection, whereas financial exposure, particularly beneficiary OOP spending, remains higher under stand-alone PDP coverage. These findings highlight how AI-based causal methods can support managed care and managed care pharmacy leaders in evaluatin","PeriodicalId":16170,"journal":{"name":"Journal of managed care & specialty pharmacy","volume":"32 9","pages":"1101-1113"},"PeriodicalIF":3.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13505361/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812244","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Who is responsible? A risk-stratified framework for pharmacist accountability in artificial intelligence-assisted managed care.","authors":"Emine Gülce Byers, Chalissa Dibya Iranisha","doi":"10.18553/jmcp.2026.32.9.1130","DOIUrl":"10.18553/jmcp.2026.32.9.1130","url":null,"abstract":"<p><p>Artificial intelligence (AI)-enabled tools are increasingly integrated into managed care pharmacy workflows, particularly in utilization management and prior authorization processes. Although these systems aim to improve operational consistency and efficiency, federal regulatory guidance and professional standards make clear that coverage and medical necessity determinations must remain grounded in human clinical judgment. As AI tools become part of routine managed care, responsibility for final decisions may become unclear, creating an accountability gap. This article synthesizes federal regulatory guidance and professional pharmacy standards relevant to AI use in health care. We review Centers for Medicare & Medicaid Services requirements, oversight findings from the US Department of Health and Human Services Office of Inspector General, and established pharmacy ethics frameworks. Based on this review, we propose a pharmacist-centered accountability framework for AI-assisted managed care decisions. The framework emphasizes explicit professional responsibility, structured human-in-the-loop review, and documentation practices that support transparency and auditability. Clarifying pharmacist accountability in AI-assisted utilization management can strengthen professional stewardship, enhance workforce readiness, and support responsible innovation in managed care pharmacy.</p>","PeriodicalId":16170,"journal":{"name":"Journal of managed care & specialty pharmacy","volume":"32 9","pages":"1130-1134"},"PeriodicalIF":3.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812843","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Natural language processing to develop a standardized lexicon for precision medicine in oncology.","authors":"Tianyi Wang, Kimberly Tsai, Steven S Kheloussi, Dana McCormick, Pamala A Pawloski","doi":"10.18553/jmcp.2026.32.9-b.s2","DOIUrl":"10.18553/jmcp.2026.32.9-b.s2","url":null,"abstract":"<p><p>Precision medicine terminology is increasingly used across clinical, laboratory, payer, regulatory, and policy settings; however, distinct terminology may be applied interchangeably or inconsistently across stakeholders, and foundational concepts may not be uniformly understood across multidisciplinary audiences with varying levels of expertise. This misalignment may create confusion for stakeholders as they attempt to navigate guideline recommendations and payer coverage policies, potentially impacting patient access to testing and treatment. In 2025, AMCP held a 2-day partnership forum on the topic of advancing precision medicine in oncology. Forum participants recommended development of a consolidated lexicon of commonly used precision medicine terms to support clearer communication and foundational understanding across stakeholders and to clarify distinctions between related concepts. Nineteen definitions were ultimately generated using a natural language processing process, an artificial intelligence approach used to extract information and derive meaning from large text samples. A comprehensive literature review was first conducted to develop an index of publicly available precision medicine terminology sources. Natural language processing-based textual analysis was then used to systematically evaluate terminology usage patterns across the index, including both consistent and inconsistent usage of terms across sources. Draft definitions generated through this process were subsequently reviewed and refined by forum participants. The resulting lexicon included relevant terms related to (1) foundational concepts in precision medicine; (2) biological foundations and molecular variation; (3) biomarker and molecular testing; (4) testing technologies, specimen collection, and regulatory classification; and (5) testing approach. The objective of this work was to organize commonly used precision medicine terms into a single reference framework, a foundational precision medicine reference for managed care stakeholders, and to clarify distinctions between related concepts where appropriate.</p>","PeriodicalId":16170,"journal":{"name":"Journal of managed care & specialty pharmacy","volume":"32 9-b Suppl","pages":"S3-S16"},"PeriodicalIF":3.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13452051/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148697607","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An informatics framework to harmonize electronic health record medication data for managed care analytics and artificial intelligence applications.","authors":"Haowen Hsu, Gabriel Gazetta, Chi-Hua Lu, David M Jacobs","doi":"10.18553/jmcp.2026.32.9.1052","DOIUrl":"10.18553/jmcp.2026.32.9.1052","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Artificial intelligence (AI) applications in managed care pharmacy depend on semantically consistent medication data, yet heterogeneous medication identifiers across real-world electronic health records (EHRs) could undermine analytic fidelity and risk propagating classification errors. To enable transportable, reproducible AI tools, methods for harmonizing disparate medication identifiers (eg, National Drug Code [NDC] and Multum drug synonym ID) to standardized vocabularies are required.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To develop and evaluate an informatics framework that harmonizes heterogeneous medication identifiers into standardized ingredient- and pharmacologic-class representations, establishing the semantic integrity required to support transparent, reliable downstream managed care analytics and AI developments.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We conducted a retrospective analysis of discharge medication records, excluding health supplements and nonmedicated agents, from EHRs of adults aged 65 years or older who had at least 1 hospitalization between 2020 and 2024 at a tertiary medical center (Buffalo General Medical Center). Heterogeneous identifiers (Multum, NDC, RxCUI) were harmonized using a 2-layered architecture anchored to RxCUI ingredient [IN] concepts and abstracted to Anatomical Therapeutic Chemical (ATC) classification. Deterministic crosswalks were derived from the RxNorm Full Monthly Release and complemented with a validated TriNetX reference file. Unmapped records underwent structured string-based reconciliation through alignment of medication name strings between raw records and reference terminology sources. A pharmacy expert manually validated the string-based reconciliation process and the RxCUI [IN]-to-ATC mapping. Framework feasibility was assessed by calculating mapping success rates and evaluating reconciliation and correction metrics.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Analysis of 214,080 records revealed significant terminology heterogeneity, with 53.0% Multum Drug Synonym IDs as nonstandardized identifiers requiring string-based reconciliation. Mapping using deterministic crosswalks achieved a 100% initial mapping rate, with 30% to 35% of unique records requiring ATC assignment corrections. While string-based alignment achieved initial match rates of 78.5% and 75.5% for RxCUI [IN] and ATC codes, respectively, reconciliation required corrections for 57.4% of assignments. Primary drivers for corrections included crosswalk omissions for branded formulations, lexical misclassifications, and taxonomic ambiguities that required clinical context for accurate ATC assignment. Examples of taxonomic ambiguities include variations in ATC classification based on indication or route of administration (eg, moxifloxacin may be mapped to J01MA14 or S01AE07).&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;This informatics framework establishes the architectural foundation necessary to transform raw EHR medicatio","PeriodicalId":16170,"journal":{"name":"Journal of managed care & specialty pharmacy","volume":"32 9","pages":"1052-1061"},"PeriodicalIF":3.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13505364/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148813081","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Navigating uncertainty matters: Evaluating large language models for drug-drug interaction identification.","authors":"Adeleine Tilley, Brian Murray, Kelli Henry, Xingmeng Zhao, Yanjun Gao, Kaitlin Blotske, Andrea Sikora","doi":"10.18553/jmcp.2026.32.9.1076","DOIUrl":"10.18553/jmcp.2026.32.9.1076","url":null,"abstract":"&lt;p&gt;&lt;strong&gt;Background: &lt;/strong&gt;Accurate detection of drug-drug interactions (DDIs) is a fundamental component of safe medication management. Traditional rule-based clinical decision support systems for DDI identification lack higher-order reasoning and contribute to alert fatigue. Large language models (LLMs) have potential for DDI identification but may hallucinate, inconsistently identify interactions, and provide overly confident responses despite uncertainty. Prior studies have emphasized accuracy, but few have examined whether LLM uncertainty expression aligns with error risk.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Objective: &lt;/strong&gt;To evaluate LLM performance in DDI identification using a clinician-validated dataset and to assess whether prompt-based mitigation strategies improve knowledge-aware uncertainty expression, defined as alignment between refusal behavior and likelihood of error.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Methods: &lt;/strong&gt;We developed a clinician-curated DDI identification task consisting of 250 medication lists, each containing 1 clinically relevant interacting drug pair, to evaluate 5 LLMs: GPT-5-Chat, GPT-4o-mini, Gemma-27B, LLaMA3-70B, and Qwen3-32B. Models were evaluated using 3 prompt formats and a zero-shot approach, with no task-specific training or examples provided. Prompts were designed to encourage uncertainty acknowledgment, including a patient safety-focused mitigation prompt to support clinically appropriate and cautious responses. Each case was run 9 times per prompt condition. The primary outcome was the Refusal Index (RI), which quantifies alignment between model refusal behavior and likelihood of error. Secondary outcomes included overall accuracy, accuracy given attempted, refusal rate, self-consistency, F score, weighted score, and entropy.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Results: &lt;/strong&gt;Across models, overall DDI identification accuracy ranged from 54.1% to 83.7%. GPT-5-Chat demonstrated the highest overall accuracy and self-consistency, whereas Quen3-32B demonstrated the lowest accuracy but the highest refusal rates. Alignment between refusal behavior and likelihood of error was weak to moderate (RI range 0.104-0.574) and varied by model. Prompt-based mitigation strategies produced inconsistent effects on RI and did not reliably recalibrate uncertainty behavior. Notably, higher overall accuracy and response stability did not consistently correspond to stronger knowledge-aware uncertainty expression. Qwen3-32B and GPT-4o-mini increased refusal rates under mitigation prompting but not in situations where responses were more likely to be incorrect.&lt;/p&gt;&lt;p&gt;&lt;strong&gt;Conclusions: &lt;/strong&gt;Substantial variability exists in both DDI identification performance and uncertainty calibration across LLMs. Prompt design alone was insufficient to consistently improve knowledge-aware uncertainty expression. Because safe deployment of LLMs in medication management depends not only on accuracy but also on appropriate deferral when error risk is elevated, multidimensional evalua","PeriodicalId":16170,"journal":{"name":"Journal of managed care & specialty pharmacy","volume":"32 9","pages":"1076-1089"},"PeriodicalIF":3.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13505366/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812825","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Comparative performance of large language models for appraising bias in real-world evidence studies.","authors":"Chijioke M Okeke, Regina Nechi, Javeria Khalid, Ibraheem M Karaye, J Douglas Thornton, Ismaeel Yunusa","doi":"10.18553/jmcp.2026.32.9.1062","DOIUrl":"10.18553/jmcp.2026.32.9.1062","url":null,"abstract":"<p><strong>Background: </strong>Real-world evidence (RWE) is increasingly used to inform regulatory and payer policy decisions and health technology assessment, yet appraising the methodological credibility of RWE studies remains time-intensive and requires specialized expertise. The appraisal task could involve using an appraisal tool that provides a structured approach for evaluating bias in observational studies of comparative effectiveness and safety. Large language models (LLMs) may offer a scalable means to support this appraisal process, but their performance on structured bias assessment tasks has not been fully characterized.</p><p><strong>Objective: </strong>To compare the performance of LLMs from 6 major artificial intelligence (AI) technology providers against human expert assessments in appraising bias in published RWE studies using the Appraisal of Potential Bias in Real-World Evidence Studies framework.</p><p><strong>Methods: </strong>We conducted a comparative diagnostic accuracy study evaluating 40 LLMs from OpenAI, Anthropic, Google, xAI, Meta, and DeepSeek. Ten published RWE studies representing diverse pharmacoepidemiological designs and data sources were appraised by each LLM using a structured chain-of-thought prompt with conditional rubric injection based on the Appraisal of Potential Bias in Real-World Evidence Studies framework. Two independent human reviewers with pharmacoepidemiology training evaluated each study, with a third adjudicator resolving disagreements to establish the reference standard. LLM performance was assessed using overall accuracy and macro-averaged precision, recall, and F1 scores. Assessment time was compared between models and benchmarked against human reviewers. Bootstrap method was used to construct 95% CI for performance measures.</p><p><strong>Results: </strong>Across 280 item-level assessments per model (10 studies × 28 items), overall accuracy ranged from 12.9% to 66.1%. The highest-performing model was Claude-Sonnet-4.6 (66.1%), followed by o3 (65.4%) and Gemini-3.1-pro-preview (65.0%). Macro-averaged F1 scores ranged from 30.9% to 66.9%; o3 achieved the highest F1 score (66.9%), followed by GROK-4 (65.5%) and Gemini-3.1-pro-preview (65.4%). Human reviewers required an average of 61.05 minutes per study; all LLMs completed assessments substantially faster, with average time per study ranging from 0.80 to 17.22 minutes relative to humans.</p><p><strong>Conclusions: </strong>LLMs hold considerable promise for automating methodological appraisal of RWE studies; however, their performance is variable and model dependent. Their greatest value may lie in enhancing efficiency and supporting human-led appraisal as decision-support tools rather than replacing expert review. Future research should assess performance across larger, more diverse RWE study collections and evaluate output reproducibility across repeated runs.</p>","PeriodicalId":16170,"journal":{"name":"Journal of managed care & specialty pharmacy","volume":"32 9","pages":"1062-1075"},"PeriodicalIF":3.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13505367/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Advancing precision medicine access in oncology: Foundational considerations for managed care stakeholders.","authors":"Steven S Kheloussi","doi":"10.18553/jmcp.2026.32.9-b.s1","DOIUrl":"10.18553/jmcp.2026.32.9-b.s1","url":null,"abstract":"","PeriodicalId":16170,"journal":{"name":"Journal of managed care & specialty pharmacy","volume":"32 9-b Suppl","pages":"S1-S2"},"PeriodicalIF":3.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13452048/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148697544","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automating cost-effectiveness models with agentic artificial intelligence: Case study and implications for value assessment.","authors":"Rahul Mudumba, A B Modi, Kevin Mayo","doi":"10.18553/jmcp.2026.32.9.1090","DOIUrl":"10.18553/jmcp.2026.32.9.1090","url":null,"abstract":"<p><strong>Background: </strong>Health economic modeling is conceptually sophisticated but operationally repetitive and resource intensive. Recent advances in large language models suggest potential for automating components of cost-effectiveness model development.</p><p><strong>Objective: </strong>To evaluate whether an agentic artificial intelligence (AI) system can reliably automate cost-effectiveness model development in the context of targeted therapies for anaplastic lymphoma kinase-positive (ALK+) non-small cell lung cancer (NSCLC).</p><p><strong>Methods: </strong>We developed the Agentic Health Economic Modeling Platform (A-HEMP) to construct a cost-effectiveness model for ALK+ NSCLC therapies without access to existing models in that clinical context. Modeling decisions and extracted parameters were compared with a previously published manual cost-effectiveness analysis. A-HEMP automated PICO-based scoping, modeling approach recommendation, systematic literature review, and structured parameter extraction. Performance was evaluated across 3 domains: model structure concordance, evidence identification concordance, and parameter value alignment. Deterministic cost-effectiveness outputs were calculated externally for benchmarking.</p><p><strong>Results: </strong>A-HEMP identified a modeling framework aligned with the published cost-effective analysis and retrieved all primary clinical trials used for clinical efficacy inputs. Concordance in model structure and assumptions was observed in 27% of modeling dimensions, with 36% partially concordant and 36% divergent. Divergences were most prominent in survival extrapolation and intracranial progression handling. Evidence identification concordance was high for primary clinical trials (100%) but moderate for cost inputs, with 25% of evidence domains fully concordant and 38% partially concordant. Parameter value alignment was high for clinical efficacy inputs and progression-free health state utilities (<2% deviation), whereas greater variability was observed for sicker health states (12% deviation) and downstream disease management costs (25%-55% deviation).</p><p><strong>Conclusions: </strong>Agentic AI can reliably automate upstream components of cost-effectiveness model development. However, nuanced modeling decisions with less standardized methodological guidance remain areas requiring expert oversight. These findings support a hybrid paradigm in which AI augments, but does not replace, health economists in value assessment and formulary decision support within managed care settings.</p>","PeriodicalId":16170,"journal":{"name":"Journal of managed care & specialty pharmacy","volume":"32 9","pages":"1090-1100"},"PeriodicalIF":3.3,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC13505362/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148812806","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}