{"id":"W4376873823","doi":"10.1287/msom.2023.1225","title":"Geographic Virtual Pooling of Hospital Resources: Data-Driven Trade-off Between Waiting and Traveling","year":2023,"lang":"en","type":"article","venue":"Manufacturing & Service Operations Management","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":8,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"","keywords":"Pooling; Computer science; Geospatial analysis; Virtual patient; Scheduling (production processes); Limiting; Shared resource; Operations research; Data mining; Operations management; Medicine; Artificial intelligence; Computer network; Geography; Cartography; Nursing; Mathematics","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":[0.002993575,0.001169497,0.00120133,0.0007964176,0.0006675184,0.001334855,0.002259411,0.001264,0.001923402],"category_scores_gemma":[0.007670222,0.0009091006,0.0009207547,0.0008991778,0.001254662,0.001487748,0.001201139,0.00131748,0.00008769119],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004793372,"about_ca_system_score_gemma":0.003945525,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.1017328,"about_ca_topic_score_gemma":0.05726276,"domain_scores_codex":[0.9985429,0.0005567374,0.00006210519,0.0003765237,0.0001338837,0.0003278207],"domain_scores_gemma":[0.9949126,0.003302214,0.00068712,0.0001986493,0.0004475183,0.0004519165],"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":[0.00005937116,0.00004477221,0.003690935,0.00002691709,0.00002980158,0.00004645098,0.00002975055,0.9921015,0.0001864141,0.0009758715,0.0003740604,0.002434138],"study_design_scores_gemma":[0.00001768145,0.00003011672,0.001303964,0.00000425298,0.00001113754,0.00001254989,0.00005052102,0.9969518,0.0001865416,0.001296071,0.0001286133,0.000006857181],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8458508,0.0004141314,0.1463786,0.00222025,0.00007216861,0.0003063843,0.00169878,0.000318761,0.002740099],"genre_scores_gemma":[0.978312,0.00005860494,0.02019442,0.0001175372,0.00001578542,0.0001028672,0.0005507717,0.00002747649,0.0006205415],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1017328,"threshold_uncertainty_score":0.2022813,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05953192574289054,"score_gpt":0.3511285295411782,"score_spread":0.2915966037982877,"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."}}