{"id":"W2519691106","doi":"10.1016/j.ejor.2018.05.017","title":"Data-driven analytics to support scheduling of multi-priority multi-class patients with wait time targets","year":2018,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":27,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Waterloo","funders":"","keywords":"Computer science; Scheduling (production processes); Analytics; Schedule; Deadline-monotonic scheduling; Operations research; Operations management; Dynamic priority scheduling; Data mining; Rate-monotonic scheduling","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":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.009070285,0.0001527328,0.0003349835,0.0004679836,0.001180212,0.00006752129,0.0007813232,0.00006919069,0.0009605078],"category_scores_gemma":[0.003571055,0.0001157091,0.00004136179,0.0006415888,0.0001784378,0.0005472351,0.0004047987,0.001070957,0.0007745526],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002019532,"about_ca_system_score_gemma":0.002339871,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002718173,"about_ca_topic_score_gemma":0.000100791,"domain_scores_codex":[0.9937847,0.002777632,0.00127524,0.0003292642,0.001333575,0.0004996056],"domain_scores_gemma":[0.9876622,0.0003787044,0.0003496727,0.0005433138,0.01061095,0.0004551391],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.005139958,0.007095963,0.5492439,0.0008404967,0.001191525,0.0003881655,0.03777798,0.2756612,0.01397286,0.002325509,0.08089848,0.02546394],"study_design_scores_gemma":[0.00854403,0.006073952,0.2286569,0.0009907477,0.00006123499,0.0000120723,0.00144339,0.7188244,0.0002142874,0.000008477323,0.03463577,0.0005347242],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8886527,0.00003683435,0.1034991,0.003611265,0.0004473446,0.001318521,0.0004906782,0.00002046348,0.001923052],"genre_scores_gemma":[0.7571704,0.00001870984,0.2403954,0.0003421789,0.0006233435,0.000003601509,0.0002651003,0.00004623819,0.001135049],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4431632,"threshold_uncertainty_score":0.9999527,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2803828324942689,"score_gpt":0.4989741436652641,"score_spread":0.2185913111709952,"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."}}