{"id":"W4399463296","doi":"10.12927/hcq.2024.27325","title":"A Primer on Artificial Intelligence for Healthcare Administrators","year":2024,"lang":"es","type":"article","venue":"Healthcare Quarterly","topic":"Healthcare Systems and Public Health","field":"Medicine","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"Artificial Intelligence in Medicine (Canada); University Health Network","funders":"","keywords":"Best practice; Health care; Health administration; Primer (cosmetics); Nursing; Business; Medical education; Medicine; Management; Political science; Public health","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":true,"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":[0.006363435,0.001077212,0.0007412795,0.002264115,0.001960216,0.008612702,0.001955648,0.007038669,0.01849495],"category_scores_gemma":[0.01269133,0.0008077098,0.0006979519,0.002270132,0.004727247,0.01236066,0.003944952,0.01659819,0.01221807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001899431,"about_ca_system_score_gemma":0.004430136,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001040567,"about_ca_topic_score_gemma":0.001661705,"domain_scores_codex":[0.9957829,0.002234382,0.00037034,0.0002597249,0.001152383,0.0002002788],"domain_scores_gemma":[0.9869961,0.01014856,0.0003051827,0.0004864951,0.001503191,0.0005604426],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00001942656,0.00008372349,0.0001694037,0.0005178285,0.00001090221,0.0001340829,0.001379288,0.0004514852,0.0003897726,0.2582273,0.5862808,0.1523359],"study_design_scores_gemma":[0.000003743583,0.00001439109,0.00007780892,0.0007019121,0.000002013056,0.0001444545,0.0002761409,0.0002711189,0.00006293922,0.06877172,0.9296637,0.00001009547],"study_design_candidate":"not_applicable","study_design_consensus":"not_applicable","genre_codex":"commentary","genre_gemma":"other","genre_scores_codex":[0.0004932625,0.1837326,0.08311225,0.5471202,0.02084414,0.0002526022,0.0003641423,0.001024695,0.1630562],"genre_scores_gemma":[0.02194074,0.2968126,0.1779154,0.3027893,0.033686,0.00158592,0.000816241,0.001024069,0.1634297],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.01849495,"threshold_uncertainty_score":0.06187171,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08335440607717955,"score_gpt":0.4169112908902508,"score_spread":0.3335568848130713,"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."}}