{"doi":"10.1002/cpt.1787","title":"Development of a System for Postmarketing Population Pharmacokinetic and Pharmacodynamic Studies Using Real‐World Data From Electronic Health Records","abstract":"Postmarketing population pharmacokinetic (PK) and pharmacodynamic (PD) studies can be useful to capture patient characteristics affecting PK or PD in real-world settings. These studies require longitudinally measured dose, outcomes, and covariates in large numbers of patients; however, prospective data collection is cost-prohibitive. Electronic health records (EHRs) can be an excellent source for such data, but there are challenges, including accurate ascertainment of drug dose. We developed a standardized system to prepare datasets from EHRs for population PK/PD studies. Our system handles a variety of tasks involving data extraction from clinical text using a natural language processing algorithm, data processing, and data building. Applying this system, we performed a fentanyl population PK analysis, resulting in comparable parameter estimates to a prior study. This new system makes the EHR data extraction and preparation process more efficient and accurate and provides a powerful tool to facilitate postmarketing population PK/PD studies using information available in EHRs.","journal":"Clinical Pharmacology & Therapeutics","year":2020,"id":97557,"datarank":0.0,"base_score":0.0,"endowment":0.0,"self_citation_contribution":0.0,"citation_network_contribution":0.0,"self_endowment_contribution":0.0,"citer_contribution":0.0,"corpus_percentile":null,"corpus_rank":null,"citation_count":32,"citer_count":0,"citers_with_citation_signal":0,"citers_with_endowment":0,"datacite_reuse_total":0,"is_dataset":false,"is_dataset_confidence":0.9049,"is_data_producer":false,"deposit_databanks":null,"is_oa":false,"file_count":0,"downloads":0,"has_version_chain":false,"published_date":"2020-01-01","fair_score":null,"fair_percentile":null,"algorithm_id":"datarank_citation_only_1hop_v6","ranking_scope":"data_only","authors":[{"id":482638,"name":"Cole Beck","orcid":"0000-0002-6849-6255","position":1,"is_corresponding":false},{"id":483785,"name":"Elizabeth McNeer","orcid":null,"position":2,"is_corresponding":false},{"id":482639,"name":"Hannah L. Weeks","orcid":"0000-0002-0262-6790","position":3,"is_corresponding":false},{"id":482640,"name":"Michael L. Williams","orcid":"0000-0003-4991-2329","position":4,"is_corresponding":false},{"id":482641,"name":"Nathan T. James","orcid":"0000-0001-7079-9151","position":5,"is_corresponding":false},{"id":405726,"name":"Xinnan Niu","orcid":null,"position":6,"is_corresponding":false},{"id":482642,"name":"Bassel Abou‐Khalil","orcid":"0000-0002-6559-1329","position":7,"is_corresponding":false},{"id":384869,"name":"Kelly A. Birdwell","orcid":"0000-0002-7996-3947","position":8,"is_corresponding":false},{"id":1110,"name":"Dan M. Roden","orcid":"0000-0002-6302-0389","position":9,"is_corresponding":false},{"id":367910,"name":"C. Michael Stein","orcid":null,"position":10,"is_corresponding":false},{"id":373246,"name":"Cosmin A. Bejan","orcid":"0000-0001-5107-0584","position":11,"is_corresponding":false},{"id":22022,"name":"Joshua C. Denny","orcid":"0000-0002-3049-7332","position":12,"is_corresponding":false},{"id":235375,"name":"Sara L. Van Driest","orcid":"0000-0003-2580-1405","position":13,"is_corresponding":false},{"id":459735,"name":"Leena Choi","orcid":"0000-0002-2544-7090","position":0,"is_corresponding":true}],"reference_count":23,"raw_metadata":null,"created_at":"2026-07-18T22:36:02.818637Z","pmid":"31957870","pmcid":null,"fwci":null,"citation_percentile":null,"influential_citations":0,"oa_status":null,"license":null,"views":0,"total_file_size_bytes":0,"version_count":0,"fair_f":null,"fair_a":null,"fair_i":null,"fair_r":null,"fair_zscore":null,"fair_rationale":null,"fair_model":null,"fair_agent_version":null,"fair_fulltext_source":null,"fair_has_llm":null,"fair_computed_at":null,"clinical_trials":[],"software_tools":[],"db_accessions":[],"linked_datasets":[],"topics":[]}