{"id":"W8071368","doi":"10.3233/978-1-58603-979-0-496","title":"Use of Case Mix Tools for Utilization Management and Planning","year":2009,"lang":"en","type":"article","venue":"Studies in health technology and informatics","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":11,"is_retracted":false,"has_abstract":true,"ca_institutions":"Canadian Institute for Health Information","funders":"","keywords":"Benchmark (surveying); Utilization management; Health care; Case mix index; Business; Population; Operations management; Health information; Medical emergency; Medicine; Actuarial science; Computer science; Environmental health; Nursing; Engineering; Economics; Geography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.0150372,0.002599961,0.001617174,0.01619776,0.001340693,0.005098266,0.002222763,0.001161048,0.01614897],"category_scores_gemma":[0.06205773,0.00194029,0.001892099,0.01060836,0.0007534254,0.003974251,0.002724792,0.002068515,0.001733495],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003407575,"about_ca_system_score_gemma":0.003947418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0108684,"about_ca_topic_score_gemma":0.009589477,"domain_scores_codex":[0.9906772,0.00495503,0.001301764,0.0007576811,0.002019454,0.0002888298],"domain_scores_gemma":[0.9360777,0.05151487,0.004570983,0.002776525,0.004196667,0.0008632569],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.0003571407,0.0006098049,0.01796132,0.0008247627,0.0005392134,0.0006211755,0.001406546,0.3766976,0.0011195,0.06451987,0.02189825,0.5134449],"study_design_scores_gemma":[0.0001539755,0.0000950395,0.002248844,0.0001777406,0.00008015265,0.0001891238,0.0003681439,0.929289,0.001015551,0.05536704,0.01093955,0.00007597593],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01874862,0.0003337864,0.9555649,0.0008675407,0.0001076411,0.002199785,0.004453449,0.009131878,0.008592442],"genre_scores_gemma":[0.1063774,0.0002239983,0.8873211,0.00008140541,0.00006394643,0.001853702,0.002932024,0.0003610463,0.0007854194],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01619776,"threshold_uncertainty_score":0.07952529,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3992567339388898,"score_gpt":0.5265137599644172,"score_spread":0.1272570260255274,"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."}}