{"id":"W2543360016","doi":"10.2196/medinform.5544","title":"Natural Language Processing–Enabled and Conventional Data Capture Methods for Input to Electronic Health Records: A Comparative Usability Study","year":2016,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":58,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"U.S. National Library of Medicine","keywords":"Usability; Documentation; Dictation; Computer science; Workflow; Natural language processing; Protocol (science); Artificial intelligence; Information retrieval; Medicine; Database; Human–computer interaction; Speech recognition; Programming language","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01130314,0.0002888468,0.000905813,0.0001432191,0.0006622827,0.00002412469,0.0007300357,0.0002879343,0.0002126543],"category_scores_gemma":[0.002106162,0.0001783005,0.00004635718,0.0003646212,0.0001305803,0.0005094018,0.0004777871,0.001235671,0.00006258058],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009965379,"about_ca_system_score_gemma":0.006525025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004290538,"about_ca_topic_score_gemma":0.003290434,"domain_scores_codex":[0.9936465,0.001719659,0.00207605,0.0004019054,0.0007035321,0.001452351],"domain_scores_gemma":[0.9950833,0.002195224,0.0007499521,0.0007842401,0.0003369523,0.0008503276],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0008551038,0.0008470446,0.01073644,0.008403201,0.0002311529,0.000002038615,0.2095821,2.644754e-7,0.00003133375,0.001201542,0.09042324,0.6776865],"study_design_scores_gemma":[0.02085325,0.005647102,0.01571939,0.005109811,0.0001000992,0.00006843362,0.2333632,0.06550983,0.00001471701,0.001077483,0.6511274,0.001409263],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7017179,0.004232956,0.2309327,0.02822135,0.00201294,0.03082486,0.0004542938,0.0005541902,0.001048847],"genre_scores_gemma":[0.9552528,0.0000648991,0.0283959,0.0101645,0.0006400839,0.003167201,0.0002430418,0.00004596911,0.002025573],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6762772,"threshold_uncertainty_score":0.9991071,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1075956413765061,"score_gpt":0.5541402620888575,"score_spread":0.4465446207123513,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}