{"id":"W2765591468","doi":"10.1177/0840470417716470","title":"How health leaders can benefit from predictive analytics","year":2017,"lang":"en","type":"article","venue":"Healthcare Management Forum","topic":"Healthcare cost, quality, practices","field":"Health Professions","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Predictive analytics; Analytics; Psychological intervention; Productivity; Health care; Risk analysis (engineering); Computer science; Data science; Business; Nursing; Medicine; Economics","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":[0.02173935,0.001188498,0.0006491211,0.002278602,0.004109084,0.01809965,0.002068652,0.006545367,0.02039704],"category_scores_gemma":[0.1042025,0.0006070609,0.001200306,0.001973337,0.005869744,0.02033865,0.008973314,0.007693128,0.009266792],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002579088,"about_ca_system_score_gemma":0.009949679,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003616163,"about_ca_topic_score_gemma":0.00500241,"domain_scores_codex":[0.9832225,0.009937115,0.0003855232,0.0009310636,0.003584216,0.001939525],"domain_scores_gemma":[0.9341131,0.04201651,0.002437463,0.005217768,0.00885045,0.007364583],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001296621,0.0003599871,0.01097258,0.0005870424,0.0001456243,0.0004828431,0.007961307,0.004525815,0.0003831582,0.1691406,0.5523242,0.2529873],"study_design_scores_gemma":[0.0001168802,0.0001520424,0.002320694,0.001916713,0.00008126577,0.00020358,0.01446143,0.008351619,0.0005344313,0.5471287,0.4245948,0.0001379854],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"commentary","genre_gemma":"commentary","genre_scores_codex":[0.007651974,0.003416826,0.03468839,0.8661741,0.003882313,0.0001559327,0.0003718806,0.0009595859,0.082699],"genre_scores_gemma":[0.6777087,0.01870276,0.08360849,0.1581326,0.01109271,0.0006444036,0.001593975,0.001245042,0.04727142],"genre_candidate":"commentary","genre_consensus":"commentary","teacher_disagreement_score":0.02173935,"threshold_uncertainty_score":0.11497,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5693339401366341,"score_gpt":0.5271846237332601,"score_spread":0.04214931640337394,"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."}}