{"id":"W4415785920","doi":"10.1051/bioconf/202519300028","title":"Unlocking Electronic Medical Record Success: Readiness Assessment with the DOQ-IT Method","year":2025,"lang":"fr","type":"article","venue":"BIO Web of Conferences","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Vendor; Medical record; Electronic medical record; Work (physics); Quality (philosophy); Information technology; Quarter (Canadian coin); Medical information","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01043137,0.0004698517,0.0005740495,0.004022769,0.0009758966,0.002000734,0.000728142,0.0004688607,0.002785064],"category_scores_gemma":[0.02218165,0.0003753899,0.001387559,0.002550622,0.0006511587,0.001771158,0.0022141,0.001095406,0.0004591069],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001114772,"about_ca_system_score_gemma":0.002752459,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002244898,"about_ca_topic_score_gemma":0.004323259,"domain_scores_codex":[0.9909724,0.004439413,0.001423058,0.0004195708,0.002095109,0.0006504551],"domain_scores_gemma":[0.9852837,0.005500819,0.003355313,0.0008049426,0.004097703,0.0009574906],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003023132,0.001612453,0.8598371,0.000369292,0.0001573155,0.0001192935,0.005967387,0.0004517694,0.001100695,0.001146999,0.001435784,0.1274995],"study_design_scores_gemma":[0.0001624718,0.002574587,0.950776,0.0003243905,0.0001646225,0.0002429355,0.02748505,0.005710855,0.003346685,0.001239463,0.00784282,0.0001302189],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9798076,0.0001366389,0.007047647,0.0008031398,0.00005287711,0.003014007,0.0004626237,0.00007031886,0.008605207],"genre_scores_gemma":[0.9775887,0.0001920366,0.01623331,0.0001739167,0.00001965677,0.003533302,0.0005427723,0.00001921631,0.001697133],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01043137,"threshold_uncertainty_score":0.05516696,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04690402444216106,"score_gpt":0.4637762927106875,"score_spread":0.4168722682685265,"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."}}