{"id":"W2889335051","doi":"10.2196/humanfactors.9889","title":"An Optimization Program to Help Practices Assess Data Quality and Workflow With Their Electronic Medical Records: Observational Study","year":2018,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Workflow; Electronic medical record; Observational study; Work (physics); Meaningful use; Medical record; Primary care; Quality (philosophy); Quality management; Patient care; Best practice; Medicine; Medical education; Nursing; Medical emergency; Family medicine; Health care; Computer science; Operations management; Management system; Database; Engineering","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.01340863,0.0005812831,0.0006133453,0.001950518,0.001994377,0.001078719,0.001015563,0.0007317457,0.002032558],"category_scores_gemma":[0.03365174,0.0006383969,0.001089966,0.003037735,0.000821838,0.001552052,0.001452341,0.001241279,0.0005603739],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.008397509,"about_ca_system_score_gemma":0.01522713,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.07821906,"about_ca_topic_score_gemma":0.07695157,"domain_scores_codex":[0.9898039,0.004554936,0.001266705,0.001194878,0.002048781,0.001130907],"domain_scores_gemma":[0.961587,0.008694607,0.01367509,0.003633574,0.008377341,0.004032246],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00102345,0.007606898,0.9442341,0.0001939476,0.0001196399,0.00009654401,0.004508926,0.0002614015,0.0002106637,0.0001075617,0.001743787,0.03989304],"study_design_scores_gemma":[0.0003599191,0.00575453,0.9855297,0.00009604402,0.0001146469,0.00009323094,0.003221605,0.001232242,0.0003686513,0.00007299581,0.003106819,0.00004949442],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9937131,0.0001298127,0.001419989,0.0001654222,0.000006214379,0.002099102,0.001314311,0.00006044736,0.00109163],"genre_scores_gemma":[0.986882,0.0001893786,0.007111803,0.0001887985,0.00001958101,0.003287383,0.001654479,0.00002051517,0.0006461383],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.07821906,"threshold_uncertainty_score":0.1555275,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5442576520684974,"score_gpt":0.6104045951976282,"score_spread":0.06614694312913083,"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."}}