{"id":"W4247896136","doi":"10.7287/peerj.preprints.27881","title":"Lean healthcare integrated with discrete event simulation and design of experiments: an emergency department expansion","year":2019,"lang":"en","type":"preprint","venue":"","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Emergency department; Discrete event simulation; Health care; Confidence interval; Lean manufacturing; Operations management; Event (particle physics); Interval (graph theory); Simulation software; Quality (philosophy); Plan (archaeology); Computer science; Medical emergency; Software; Engineering; Medicine; Simulation; Statistics; Nursing; Mathematics; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0008725584,0.0003678808,0.0005746032,0.0001890264,0.0004899249,0.00001502033,0.0001447567,0.0005512281,0.0004497972],"category_scores_gemma":[0.00007388044,0.0002694245,0.00004595561,0.0001762882,0.00003221552,0.0002045257,0.0001878451,0.0007750547,0.00001249129],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002952832,"about_ca_system_score_gemma":0.001261273,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002380421,"about_ca_topic_score_gemma":0.0006256407,"domain_scores_codex":[0.996072,0.001206265,0.001254588,0.0006871134,0.0003823669,0.0003976509],"domain_scores_gemma":[0.9973214,0.0001288815,0.0006267795,0.0007122645,0.0009541493,0.0002565848],"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":[0.0003334914,0.0001240054,0.01493753,0.0008869203,0.0000470556,9.226159e-7,0.005752057,0.9755799,0.00009774127,0.0005914864,0.0001164057,0.001532492],"study_design_scores_gemma":[0.0008523134,0.001021145,0.003646529,0.001193491,0.00004750855,4.25469e-7,0.006649987,0.985692,0.0001060128,0.0002338424,0.0001450734,0.0004117262],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2953492,0.0003769963,0.698446,0.0006149804,0.0005271176,0.004402311,0.0000817781,0.00009616634,0.0001054963],"genre_scores_gemma":[0.9472036,0.0005630304,0.04913317,0.000176187,0.0000857148,0.0004508954,0.00175181,0.0000640479,0.0005715824],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6518544,"threshold_uncertainty_score":0.9999758,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1383501335460967,"score_gpt":0.4729544347357633,"score_spread":0.3346043011896667,"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."}}