{"id":"W3163905180","doi":"10.1145/3411764.3445172","title":"Automating Clinical Documentation with Digital Scribes: Understanding the Impact on Physicians","year":2021,"lang":"en","type":"article","venue":"","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Workflow; Documentation; Computer science; Implementation; Popularity; Exploratory research; Conversation; Multimedia; World Wide Web; Data science; Software engineering; Psychology; Database","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.00115174,0.0001558734,0.0002886897,0.00003444535,0.0009595393,0.00007736671,0.0001148885,0.0001104746,0.0004101176],"category_scores_gemma":[0.0002364721,0.00008112822,0.00009694114,0.0003298913,0.00005068424,0.0003024,0.00003981524,0.000825006,0.0004087476],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001169765,"about_ca_system_score_gemma":0.001363395,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002534168,"about_ca_topic_score_gemma":0.0004444523,"domain_scores_codex":[0.9970672,0.0009292709,0.0007136556,0.0002923255,0.0003250586,0.000672513],"domain_scores_gemma":[0.9963992,0.002704702,0.0003177544,0.0003563959,0.0001078605,0.0001140614],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"qualitative","study_design_scores_codex":[0.0004786519,0.0006817709,0.7497089,0.0007337286,0.0006649382,0.00006703087,0.01913784,0.0004405339,0.000155626,0.08937015,0.08074486,0.05781597],"study_design_scores_gemma":[0.01432566,0.007139599,0.3135638,0.0100869,0.0002052901,0.000108735,0.5870882,0.0337636,0.0002167774,0.0202553,0.01123773,0.002008463],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7939169,0.00006580202,0.03121186,0.007505522,0.000891403,0.001422672,0.00001783885,0.0003677676,0.1646002],"genre_scores_gemma":[0.9932075,0.00001540449,0.0001530416,0.002479502,0.000528311,0.00005999644,0.00002822398,0.00003300893,0.003494978],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.5679503,"threshold_uncertainty_score":0.7380098,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1651219891252978,"score_gpt":0.5321502281447215,"score_spread":0.3670282390194237,"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."}}