{"id":"W3185660615","doi":"10.1080/20476965.2021.1952113","title":"Patterns of health information exchange strategies underlying health information technologies capabilities building","year":2021,"lang":"en","type":"article","venue":"Health Systems","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Université du Québec à Trois-Rivières; National Bank of Canada; Université du Québec à Montréal","funders":"","keywords":"Health information exchange; Health informatics; Information exchange; Health care; Cluster analysis; Set (abstract data type); Health records; European union; Data set; Health information technology; Computer science; Business; Medicine; Data science; Medical emergency; Nursing; Public health; Health information; Artificial intelligence","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":[0.00297489,0.0001898707,0.0002671935,0.003466386,0.0005480732,0.002279544,0.0005154292,0.0005578934,0.001772452],"category_scores_gemma":[0.02395549,0.0003207291,0.0003464743,0.004222377,0.001103789,0.002087416,0.001887266,0.0005728225,0.0003484102],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001236618,"about_ca_system_score_gemma":0.001084747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004868637,"about_ca_topic_score_gemma":0.005245199,"domain_scores_codex":[0.995995,0.001620868,0.0005805059,0.0006125462,0.0006119683,0.0005791624],"domain_scores_gemma":[0.9670175,0.01860102,0.00780585,0.002722301,0.002880244,0.0009731135],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001195683,0.0001096269,0.9694155,0.00006413252,0.00007652254,0.0001203721,0.005048153,0.001267558,0.001971013,0.004138632,0.0002131308,0.01745582],"study_design_scores_gemma":[0.00000990446,0.00005379575,0.9824672,0.00003256883,0.00002044594,0.0001782846,0.005979107,0.00684388,0.0007547595,0.003049245,0.0005902436,0.00002048396],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9967277,0.00007213033,0.001194641,0.0001524592,7.737163e-7,0.00003322331,0.0002442703,0.00001233301,0.001562311],"genre_scores_gemma":[0.99886,0.00002031386,0.0007994308,0.000008150912,7.731489e-7,0.000009917479,0.00019844,0.000002331662,0.0001007622],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.004868637,"threshold_uncertainty_score":0.01573288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.107687756460784,"score_gpt":0.4345416159166085,"score_spread":0.3268538594558246,"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."}}