{"id":"W3153246754","doi":"10.2196/26402","title":"Optimization of Patient Flow in Urgent Care Centers Using a Digital Tool for Recording Patient Symptoms and History: Simulation Study","year":2021,"lang":"en","type":"article","venue":"JMIR Formative Research","topic":"Emergency and Acute Care Studies","field":"Medicine","cited_by":19,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Wellcome Trust","keywords":"Crowding; Triage; Discrete event simulation; Medical emergency; Medicine; Patient satisfaction; Emergency department; Patient experience; Reduction (mathematics); Computer science; Health care; Emergency medicine; Simulation; Nursing; Psychology","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":[0.001254774,0.001099096,0.0008732953,0.0007537844,0.0006464799,0.0009305656,0.001393182,0.002025713,0.003475838],"category_scores_gemma":[0.004351153,0.0004932549,0.001303458,0.0004880662,0.0007172395,0.0006285179,0.00100089,0.001227638,0.0001635434],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001863483,"about_ca_system_score_gemma":0.001954279,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02713104,"about_ca_topic_score_gemma":0.01501439,"domain_scores_codex":[0.999231,0.0003012677,0.00003144543,0.0001166999,0.00006661036,0.0002530759],"domain_scores_gemma":[0.9912986,0.006451142,0.0005137168,0.0002214377,0.0006056707,0.0009094728],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004848832,0.0009070503,0.01050722,0.00008349468,0.00007483529,0.0001960719,0.0001084298,0.9831476,0.0006279976,0.0007296094,0.000525314,0.002607423],"study_design_scores_gemma":[0.000174818,0.0004715112,0.00268247,0.00001520599,0.00003940913,0.00003290925,0.0001778249,0.9954687,0.0003276787,0.0003365327,0.0002566386,0.00001625902],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9885138,0.0001527497,0.007846425,0.0002618627,0.00004950575,0.000211133,0.0004440659,0.00007505577,0.002445362],"genre_scores_gemma":[0.9945297,0.00008754664,0.004248993,0.00006378807,0.00001061062,0.0001119407,0.0002861759,0.00000752743,0.0006538769],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.02713104,"threshold_uncertainty_score":0.05394626,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05375662418065647,"score_gpt":0.379427350890608,"score_spread":0.3256707267099516,"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."}}