{"id":"W2346734629","doi":"10.1080/10686967.2016.11918468","title":"Using Six Sigma, Lean, and Simulation to Improve the Phlebotomy Process","year":2016,"lang":"en","type":"article","venue":"Quality Management Journal","topic":"Healthcare Operations and Scheduling Optimization","field":"Health Professions","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Brock University","funders":"","keywords":"Phlebotomy; Six Sigma; Process (computing); Quality management; Lean Six Sigma; Operations management; Lean manufacturing; Process management; Medicine; Computer science; Engineering; Management system; Surgery","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":[0.01018855,0.001112352,0.0005381592,0.002402957,0.001546126,0.004239284,0.001162568,0.0009698207,0.00131258],"category_scores_gemma":[0.013668,0.0005270188,0.0008884264,0.002180517,0.001114485,0.003314711,0.002632895,0.001440123,0.0002666901],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003493908,"about_ca_system_score_gemma":0.008020074,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00791033,"about_ca_topic_score_gemma":0.01211306,"domain_scores_codex":[0.9897034,0.007387918,0.0004371225,0.000374721,0.001694798,0.0004020329],"domain_scores_gemma":[0.9889582,0.007041547,0.001166012,0.0006776146,0.001648216,0.0005083939],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0008872033,0.002273333,0.03835036,0.001482145,0.0003998824,0.0004359741,0.009228578,0.4031425,0.006857236,0.09099146,0.003945512,0.4420058],"study_design_scores_gemma":[0.0004213906,0.0023435,0.006496966,0.000925754,0.0002498173,0.000150257,0.006593695,0.876698,0.01091903,0.06496903,0.03004877,0.0001838858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2326189,0.0009891591,0.7295328,0.00523522,0.0001778962,0.001276955,0.0001122108,0.001209467,0.0288473],"genre_scores_gemma":[0.5624979,0.0007848229,0.4343921,0.000298516,0.00002752043,0.0003680066,0.0001406103,0.0000520287,0.001438455],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01018855,"threshold_uncertainty_score":0.05388284,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1983623097224087,"score_gpt":0.5376506704015672,"score_spread":0.3392883606791585,"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."}}