{"id":"W2566921621","doi":"","title":"Role of Social Knowledge Networks in facilitating meaningful use of Electronic Health Record medication reconciliation.","year":2016,"lang":"en","type":"article","venue":"Journal of Hospital Administration","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"","keywords":"Electronic health record; Meaningful use; Medication Reconciliation; Health records; Electronic medical record; Knowledge management; Medicine; Business; Psychology; Medical emergency; Family medicine; Computer science; Health care; Political science","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":[0.004099611,0.0001496832,0.0006209363,0.0002399834,0.0001631296,0.000005051659,0.0001699496,0.0002431228,0.00006780355],"category_scores_gemma":[0.001506661,0.0001167551,0.0001194478,0.000328868,0.00005734704,0.0005277708,0.00002148424,0.0006478695,0.000007349745],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001463208,"about_ca_system_score_gemma":0.005349018,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001938264,"about_ca_topic_score_gemma":0.002538434,"domain_scores_codex":[0.9946055,0.001234446,0.003009967,0.0001808751,0.0003966202,0.0005725994],"domain_scores_gemma":[0.9931408,0.001361225,0.0043741,0.0001518285,0.0008312413,0.0001408593],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001655798,0.002020333,0.5432328,0.002020604,0.0002571509,0.00000389853,0.06523808,0.00005139061,0.005615735,0.02064144,0.007565363,0.3516974],"study_design_scores_gemma":[0.01597692,0.08720327,0.7635238,0.0127305,0.0001707123,0.00004935863,0.04500928,0.008306053,0.002152418,0.01629347,0.04708257,0.00150164],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9900141,0.0008120459,0.004395111,0.003056596,0.0007554179,0.0006771231,0.00001512785,0.00001319179,0.0002613336],"genre_scores_gemma":[0.998354,0.0003232234,0.0005416911,0.00004804837,0.0004812243,0.00002739212,0.00000900045,0.00001949363,0.0001959766],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3501958,"threshold_uncertainty_score":0.9488928,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0385524758221101,"score_gpt":0.3892677953577808,"score_spread":0.3507153195356707,"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."}}