{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,22]],"date-time":"2026-08-22T14:24:10Z","timestamp":1787408650455,"version":"build-2736575974"},"reference-count":20,"publisher":"Wiley","issue":"8","license":[{"start":{"date-parts":[[2026,8,6]],"date-time":"2026-08-06T00:00:00Z","timestamp":1785974400000},"content-version":"vor","delay-in-days":5,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"},{"start":{"date-parts":[[2026,8,1]],"date-time":"2026-08-01T00:00:00Z","timestamp":1785542400000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"content-domain":{"domain":["ascpt.onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Clinical Translational Sci"],"published-print":{"date-parts":[[2026,8]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:p>\n                    The \u201clast mile\u201d problem in healthcare AI\u2014translating high\u2010performance models into accessible, privacy\u2010preserving point\u2010of\u2010care tools\u2014remains unsolved for pharmacogenomic (PGx) risk assessment. No existing platform integrates opioid and polypharmacy risk scoring, model\u2010based scenario analysis, and CPIC\u2010based PGx patient cards within a single privacy\u2010first serverless architecture. We designed, developed, and implemented the PGx Risk Dashboard: a serverless system combining an S3\u2010hosted static frontend with AWS Lambda containerized backends. Fifty\u2010six bin\u2010level ensembles (2 cohorts \u00d7\u20097 age bands \u00d7\u20094 density bins), each comprising CatBoost\/XGBoost\/XGBoost\u2010RF base learners, are packaged within an AWS Lambda container, with aggregate fallback artifacts used for sparse cells. Partial\u2010input imputation using training\u2010set medians handles real\u2010world data sparsity; the PGx Patient Card executes stateless CPIC lookups for 573 deterministic gene\u2010drug logic\u2010verification cases without storing any PII. Lambda cold\u2010start latency was mean 2100\u2009ms (SD 250\u2009ms); warm inference latency was mean 6\u2009ms (SD 1\u2009ms), meeting the sub\u2010100\u2009ms target. Prediction stability was maintained under sparse inputs (\u2264\u20095 features provided): mean |\u0394\n                    <jats:italic>p\u0302<\/jats:italic>\n                    |\u2009=\u20090.10 vs. full\u2010feature baseline. The PGx Risk Dashboard demonstrates technical feasibility for low\u2010latency, privacy\u2010first PGx risk decision support without EHR integration. We maintain the association\u2010versus\u2010causation distinction to support safer clinical use, avoid overstating treatment\u2010effect evidence from observational outputs, and preserve a clear path to prospective causal\u2010effect validation as next steps.\n                  <\/jats:p>","DOI":"10.1111\/cts.70697","type":"journal-article","created":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T03:46:46Z","timestamp":1786074406000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["A Serverless Pharmacogenomic Risk Dashboard: Translating Ensemble Models and Model\u2010Based Scenario Rules to Clinical Decision Support"],"prefix":"10.1111","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8622-0597","authenticated-orcid":false,"given":"R. Jerome","family":"Dixon","sequence":"first","affiliation":[{"name":"Department of Pharmacotherapy and Outcomes Science, School of Pharmacy Virginia Commonwealth University  Richmond Virginia USA"},{"name":"Ph.D. Program in Integrative Life Sciences, School of Life Sciences and Sustainability Virginia Commonwealth University  Richmond Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6046-7540","authenticated-orcid":false,"given":"Elvin T.","family":"Price","sequence":"additional","affiliation":[{"name":"Department of Pharmacotherapy and Outcomes Science, School of Pharmacy Virginia Commonwealth University  Richmond Virginia USA"},{"name":"ConvergenceLabs@VCU\u2014Health Outcomes Virginia Commonwealth University  Richmond Virginia USA"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2026,8,6]]},"reference":[{"key":"e_1_2_13_2_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010018\u20100300\u20107"},{"key":"e_1_2_13_3_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41591\u2010021\u201001614\u20100"},{"key":"e_1_2_13_4_1","unstructured":"U.S. Food and Drug Administration \u201cArtificial Intelligence\/Machine Learning (AI\/ML)\u2010Based Software as a Medical Device (SaMD) Action Plan \u201daccessed June 2 2026 https:\/\/www.fda.gov\/media\/145022\/download."},{"key":"e_1_2_13_5_1","doi-asserted-by":"publisher","DOI":"10.1056\/NEJMp1714229"},{"key":"e_1_2_13_6_1","unstructured":"U.S. Department of Health and Human Services \u201cHIPAA Omnibus Rule: Modifications to the HIPAA Privacy Security Enforcement and Breach Notification Rules \u201daccessed June 2 2026 https:\/\/www.hhs.gov\/hipaa\/for\u2010professionals\/privacy\/laws\u2010regulations\/index.html."},{"key":"e_1_2_13_7_1","doi-asserted-by":"publisher","DOI":"10.1002\/cpt.2079"},{"key":"e_1_2_13_8_1","doi-asserted-by":"publisher","DOI":"10.7326\/M14\u20100697"},{"key":"e_1_2_13_9_1","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1001381"},{"key":"e_1_2_13_10_1","volume-title":"Flexible Imputation of Missing Data","author":"Buuren S.","year":"2011"},{"key":"e_1_2_13_11_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0169\u20107439(00)00122\u20102"},{"key":"e_1_2_13_12_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.patter.2023.100804"},{"key":"e_1_2_13_13_1","first-page":"4765","article-title":"A Unified Approach to Interpreting Model Predictions","volume":"30","author":"Lundberg S. M.","year":"2017","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_2_13_14_1","unstructured":"A.IgnatievandJ.Marques\u2010Silva \u201cFFA: Formal Feature Attribution\u2014Reference Implementation \u201daccessed March 1 2025 https:\/\/github.com\/ffattr\/ffa."},{"key":"e_1_2_13_15_1","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9780511803161"},{"key":"e_1_2_13_16_1","volume-title":"Causal Inference: What if","author":"Hern\u00e1n M. 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