{"id":"W4283793419","doi":"10.1111/poms.13801","title":"Dynamic scheduling of home care patients to medical providers","year":2022,"lang":"en","type":"article","venue":"Production and Operations Management","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Markov decision process; Leverage (statistics); Scheduling (production processes); Dynamic programming; Home health; Operations research; Curse of dimensionality; Heuristic; Mathematical optimization; Service provider; Health care; Markov process; Operations management; Service (business); Business; Economics; Machine learning; Artificial intelligence; Mathematics; Marketing","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["sts"],"consensus_categories":[],"category_scores_codex":[0.0005889164,0.00009028176,0.0001310297,0.0002507855,0.001978603,0.0000158599,0.0001014431,0.00003976978,0.0006604969],"category_scores_gemma":[0.0002904185,0.00009025478,0.00002016522,0.0004378591,0.00002282526,0.0001341059,0.0002338268,0.0002593557,0.00001668049],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000217549,"about_ca_system_score_gemma":0.0002079471,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000099547,"about_ca_topic_score_gemma":0.0003264856,"domain_scores_codex":[0.9983099,0.0002917663,0.0004567464,0.0003231195,0.0004269585,0.0001915468],"domain_scores_gemma":[0.999213,0.00001651205,0.00005669596,0.0002291829,0.0003361929,0.0001483877],"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":[0.0001419919,0.0006528231,0.04751097,0.001543001,0.0001100951,0.000003307716,0.04398871,0.8099684,0.00008428861,0.02320567,0.003522579,0.06926823],"study_design_scores_gemma":[0.006980243,0.002199573,0.1365904,0.001158729,0.000307331,0.00000951966,0.3464177,0.3490847,0.00009021159,0.0003329989,0.1548359,0.001992758],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.957095,0.000191918,0.01255777,0.02304648,0.001931414,0.003532378,0.00003338272,0.00009805967,0.001513559],"genre_scores_gemma":[0.9765498,0.0001153298,0.01906979,0.001276469,0.00004980199,0.0009325823,0.0001729342,0.0000164645,0.00181681],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4608836,"threshold_uncertainty_score":0.9993207,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02030300568105367,"score_gpt":0.361420034223787,"score_spread":0.3411170285427333,"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."}}