{"id":"W2891164545","doi":"10.1016/j.ijmedinf.2018.09.007","title":"Connecting healthcare and clinical research: Workflow optimizations through seamless integration of EHR, pseudonymization services and EDC systems","year":2018,"lang":"en","type":"article","venue":"International Journal of Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Health, British Columbia","keywords":"Workflow; Documentation; Computer science; Service (business); System integration; Electronic data capture; Health care; Pseudonym; Database; Data collection; Business","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0114142,0.0001283433,0.0004744253,0.0003285371,0.0004042864,0.00006238226,0.0005063018,0.0004606331,0.00007575144],"category_scores_gemma":[0.002867573,0.00009999669,0.00005128181,0.0002956576,0.0002552319,0.0007094527,0.0002251478,0.001583602,0.00001104608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002556537,"about_ca_system_score_gemma":0.001290815,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007862448,"about_ca_topic_score_gemma":0.0006003389,"domain_scores_codex":[0.9928194,0.001289053,0.003337091,0.000111368,0.002072274,0.0003708214],"domain_scores_gemma":[0.9901124,0.002640409,0.001985705,0.0001589201,0.004734439,0.0003681158],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002050314,0.0009466286,0.1664927,0.01296141,0.001900004,0.00006449583,0.235003,0.0008564123,0.0000660983,0.2008129,0.01892657,0.3599195],"study_design_scores_gemma":[0.006757409,0.00259683,0.003299585,0.02581965,0.00009066696,0.0007417897,0.1397714,0.778708,0.00004728786,0.004015728,0.03776227,0.0003892777],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7962083,0.001549727,0.1773591,0.01473148,0.00741688,0.0009685367,0.00003776336,0.00004049985,0.001687706],"genre_scores_gemma":[0.9802902,0.00526046,0.01052077,0.001150741,0.002675595,0.00001101649,0.00002601082,0.00002107182,0.00004406782],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7778516,"threshold_uncertainty_score":0.688005,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2442844514357955,"score_gpt":0.5762459431601915,"score_spread":0.331961491724396,"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."}}