{"id":"W2768917383","doi":"10.1186/s12913-017-2697-y","title":"Barriers to data quality resulting from the process of coding health information to administrative data: a qualitative study","year":2017,"lang":"en","type":"article","venue":"BMC Health Services Research","topic":"Medical Coding and Health Information","field":"Health Professions","cited_by":124,"is_retracted":false,"has_abstract":true,"ca_institutions":"Health Sciences Centre; University of Calgary","funders":"O'Brien Institute for Public Health, University of Calgary; Canadian Institutes of Health Research; Alberta Innovates","keywords":"Data quality; Health informatics; Health administration; Nursing research; Health services research; Data collection; Quality (philosophy); Coding (social sciences); Information quality; Data science; Medicine; Computer science; Knowledge management; Public health; Information system; Business; Nursing; Marketing; Statistics; Engineering","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":["metaresearch","sts","open_science"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.08977824,0.0002263555,0.0007546121,0.0002891465,0.01075595,0.0002038207,0.005386893,0.0001576273,0.0001452828],"category_scores_gemma":[0.01816348,0.0001649346,0.00002327785,0.0007555418,0.0001459208,0.00241267,0.003526596,0.001762091,0.0003050691],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005056095,"about_ca_system_score_gemma":0.01221277,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.103001,"about_ca_topic_score_gemma":0.06834656,"domain_scores_codex":[0.9796536,0.01174231,0.003419671,0.0007642279,0.002836392,0.001583796],"domain_scores_gemma":[0.9798763,0.007947517,0.002563195,0.005548592,0.001801087,0.002263339],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0008699228,0.00006424313,0.03487903,0.005769485,0.00002561683,2.359483e-7,0.9435176,0.000006494143,7.929213e-7,0.0003646656,0.00746972,0.007032245],"study_design_scores_gemma":[0.001068198,0.000647935,0.1621353,0.002070039,0.000003672651,8.576485e-8,0.8200419,0.003895365,0.000001474378,0.0001172546,0.009893475,0.0001253399],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8893311,0.0001425515,0.004371568,0.07588966,0.0008735507,0.01740672,0.009462138,0.0001323935,0.002390324],"genre_scores_gemma":[0.9808752,0.00005131488,0.002194515,0.0141118,0.0004158107,0.0004053577,0.001871564,0.00001862042,0.00005579674],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1272562,"threshold_uncertainty_score":0.9999945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.7821812141022199,"score_gpt":0.7238918634367201,"score_spread":0.05828935066549978,"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."}}