{"id":"W1515835622","doi":"","title":"EHR scorecard: usage up, but hard core is small","year":2006,"lang":"en","type":"article","venue":"InfoTech Bulletin","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Medicine; Electronic health record; Balanced scorecard; Health records; Quarter (Canadian coin); Family medicine; George (robot); Health information technology; Health care; Management","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":[0.01429923,0.0004485336,0.0006302515,0.003630549,0.0006605812,0.005230866,0.0009428958,0.000949892,0.009526588],"category_scores_gemma":[0.07955532,0.0003380832,0.0004696348,0.004968283,0.002731579,0.006647093,0.002663931,0.002542159,0.004781429],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001682892,"about_ca_system_score_gemma":0.001543598,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001388288,"about_ca_topic_score_gemma":0.002082129,"domain_scores_codex":[0.9791771,0.006026602,0.00125058,0.001352786,0.0113636,0.0008293038],"domain_scores_gemma":[0.8933129,0.05730437,0.009213497,0.008544877,0.02428585,0.007338572],"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":[0.0002671808,0.0001279144,0.06086813,0.0003124181,0.00006812521,0.0001140898,0.0004915021,0.00009991806,0.0006477954,0.006880235,0.07250439,0.8576183],"study_design_scores_gemma":[0.0001480614,0.001358546,0.5358753,0.002582661,0.0002053528,0.002346383,0.003132632,0.00168875,0.002791182,0.03065557,0.4190664,0.000149082],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.3875021,0.05030812,0.01962568,0.2438575,0.005020681,0.0005728643,0.003441598,0.001845128,0.2878263],"genre_scores_gemma":[0.9355548,0.01282485,0.008793477,0.01935022,0.005957322,0.0001688002,0.001394103,0.0005905476,0.01536592],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01429923,"threshold_uncertainty_score":0.07562244,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09101517972541348,"score_gpt":0.3765324873357327,"score_spread":0.2855173076103192,"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."}}