{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006934099,0.0002579479,0.0002025545,0.001407627,0.00243803,0.004019623,0.0007774998,0.001105263,0.006249706],"category_scores_gemma":[0.06422977,0.0002618076,0.0003200916,0.0006563838,0.00111166,0.004200363,0.004387828,0.001124391,0.0004646775],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001077738,"about_ca_system_score_gemma":0.004492072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004069167,"about_ca_topic_score_gemma":0.007311321,"domain_scores_codex":[0.990489,0.007155967,0.0003046187,0.0004311322,0.0009390141,0.0006801913],"domain_scores_gemma":[0.9266427,0.05605821,0.008270093,0.001741062,0.002994959,0.004293001],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0009894485,0.002664285,0.2982745,0.0008905142,0.0005061574,0.001617722,0.07027965,0.0009527565,0.003155902,0.02401354,0.01169627,0.5849593],"study_design_scores_gemma":[0.0005576278,0.002175934,0.5962285,0.003416431,0.001235776,0.002728884,0.1677009,0.01676955,0.005243414,0.08472174,0.118913,0.000308172],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8910794,0.001986239,0.005717474,0.01703972,0.0001817408,0.0002303617,0.0002461128,0.0001041146,0.08341482],"genre_scores_gemma":[0.9955656,0.0004571378,0.002407667,0.0003389475,0.00004902398,0.00005210589,0.0000520887,0.00001057582,0.001066767],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006934099,"threshold_uncertainty_score":0.0366714,"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."}}