{"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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.00964513,0.0004601719,0.001810963,0.0007791552,0.002014643,0.0001190709,0.0003955609,0.0004889126,0.00004747288],"category_scores_gemma":[0.0005454895,0.0004494357,0.0001293612,0.0009641671,0.00006648027,0.002709826,0.0002016787,0.001353956,0.0001160856],"about_ca_system_candidate":true,"about_ca_system_consensus":true,"about_ca_system_score_codex":0.004735424,"about_ca_system_score_gemma":0.02086468,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06500421,"about_ca_topic_score_gemma":0.005198088,"domain_scores_codex":[0.9858996,0.004016508,0.006207216,0.0004103492,0.00102769,0.002438581],"domain_scores_gemma":[0.991379,0.000814735,0.005427911,0.001038578,0.0009460033,0.00039382],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"systematic_review","study_design_gemma":"qualitative","study_design_scores_codex":[0.00007893312,0.0001724257,0.03737178,0.3792027,0.0001601696,0.000002686167,0.1739718,0.0007453826,0.00002785243,0.1827357,0.03015628,0.1953743],"study_design_scores_gemma":[0.001704349,0.0009904952,0.007707464,0.01183908,0.000006576657,0.00005635447,0.6838834,0.001451386,0.00001463935,0.0007388322,0.2910936,0.0005138489],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2433302,0.1367198,0.4069514,0.1377128,0.02575007,0.03402502,0.002301943,0.006922474,0.006286332],"genre_scores_gemma":[0.9874319,0.005571931,0.0009804015,0.003898721,0.0002130129,0.001150071,0.0005000224,0.00005245481,0.000201447],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.7441018,"threshold_uncertainty_score":0.9997957,"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."}}