{"id":"W4386957605","doi":"10.3389/fdgth.2023.1242214","title":"Leveraging machine learning and prescriptive analytics to improve operating room throughput","year":2023,"lang":"en","type":"article","venue":"Frontiers in Digital Health","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Ottawa Hospital; Carleton University; University of Ottawa","funders":"","keywords":"Overtime; Staffing; Computer science; Schedule; Throughput; Operations management; Medicine; Engineering","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002920038,0.001890028,0.001156061,0.001897191,0.0003946614,0.00197008,0.00135637,0.0006938349,0.002140088],"category_scores_gemma":[0.01807556,0.0004987039,0.0008519941,0.002272164,0.0004083792,0.00234466,0.000973202,0.001730655,0.001074368],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001138547,"about_ca_system_score_gemma":0.00294667,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01046146,"about_ca_topic_score_gemma":0.01229136,"domain_scores_codex":[0.9981706,0.0005594833,0.000172024,0.0004095401,0.000457889,0.0002303682],"domain_scores_gemma":[0.9906054,0.005762941,0.001058924,0.0008702918,0.001205419,0.0004970031],"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":[0.0006972263,0.00104008,0.04986628,0.0003125099,0.0001534333,0.0001193465,0.0001674399,0.6900477,0.002905559,0.00217252,0.006300674,0.2462173],"study_design_scores_gemma":[0.00001550915,0.0001074112,0.002929098,0.00002455903,0.00001627466,0.00001882827,0.00004574355,0.9918042,0.001240772,0.002913772,0.0008658105,0.0000180294],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3938983,0.002871184,0.5731135,0.0042009,0.0006313408,0.0003955063,0.005508906,0.01017406,0.009206257],"genre_scores_gemma":[0.873876,0.0005093626,0.1200026,0.0002488111,0.000219053,0.0001222365,0.003394556,0.0002251529,0.001402031],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01046146,"threshold_uncertainty_score":0.02080113,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04776962174622436,"score_gpt":0.3844907099652903,"score_spread":0.3367210882190659,"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."}}