{"id":"W4294786237","doi":"10.2196/38385","title":"Evaluating the Impact of a Point-of-Care Cardiometabolic Clinical Decision Support Tool on Clinical Efficiency Using Electronic Health Record Audit Log Data: Algorithm Development and Validation","year":2022,"lang":"en","type":"article","venue":"JMIR Medical Informatics","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Sutter Health","keywords":"Electronic health record; Audit; Clinical decision support system; Computer science; Decision support system; Data mining; Algorithm; Point of care; Health records; Health care; Data science; Medicine; Nursing; Accounting; Business","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.03316632,0.0007779107,0.0008876057,0.002161108,0.00035854,0.001709073,0.001015758,0.0008072139,0.0008505657],"category_scores_gemma":[0.09164277,0.0004397534,0.0009754843,0.001218876,0.0005629784,0.001340446,0.001279847,0.0006596043,0.0001681498],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00109043,"about_ca_system_score_gemma":0.001820285,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001769749,"about_ca_topic_score_gemma":0.001246852,"domain_scores_codex":[0.9852511,0.009443582,0.001491061,0.001562841,0.001998355,0.0002531769],"domain_scores_gemma":[0.8945494,0.08802956,0.007401512,0.003337009,0.005923788,0.0007588043],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.003931595,0.001538334,0.8368292,0.0002094395,0.0007205739,0.00004623318,0.0002231012,0.03445555,0.002099582,0.0003140595,0.0003878445,0.1192445],"study_design_scores_gemma":[0.0007791099,0.006222399,0.2881501,0.00008303074,0.0004839225,0.0002104621,0.0002243856,0.6948212,0.007888096,0.0006206525,0.0004688725,0.00004776907],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9439361,0.0002089139,0.05390924,0.0001282132,0.00001755001,0.000807494,0.0002690516,0.0002925174,0.0004309001],"genre_scores_gemma":[0.9360931,0.0000656156,0.0630291,0.00003668863,0.00001147826,0.0003994092,0.0002794417,0.00001778754,0.00006741126],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03316632,"threshold_uncertainty_score":0.1754023,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.232881350551755,"score_gpt":0.5892627383802983,"score_spread":0.3563813878285433,"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."}}