{"id":"W7099471601","doi":"","title":"RESEARCH ARTICLE Open Access Measuring interoperable EHR adoption and maturity: a Canadian example","year":2016,"lang":"en","type":"article","venue":"","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Interoperability; Health information exchange; Health care; Information system; Electronic health record; Maturity (psychological); Order (exchange); Data access; Information sharing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":["open_science"],"consensus_categories":[],"category_scores_codex":[0.01438186,0.0004376319,0.0005192368,0.007340238,0.007745914,0.006921872,0.00203683,0.001347636,0.005776811],"category_scores_gemma":[0.05296614,0.0002810733,0.0008056401,0.02266395,0.002603215,0.003548012,0.0041543,0.001658292,0.0005706379],"about_ca_system_candidate":true,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.08754533,"about_ca_system_score_gemma":0.1633061,"about_ca_topic_candidate":true,"about_ca_topic_consensus":true,"about_ca_topic_score_codex":0.9892559,"about_ca_topic_score_gemma":0.9905956,"domain_scores_codex":[0.9793814,0.003469639,0.001082865,0.0009765865,0.01252534,0.002564174],"domain_scores_gemma":[0.9188259,0.01105358,0.002704397,0.003249219,0.06005922,0.004107615],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0004260128,0.0006463967,0.4518356,0.0009618932,0.0001450017,0.00114198,0.02931742,0.002062368,0.001040715,0.0723521,0.05460339,0.3854671],"study_design_scores_gemma":[0.0001616337,0.0003120031,0.6701734,0.001518252,0.000261863,0.001042655,0.05188642,0.006469941,0.001689584,0.009430593,0.2567004,0.0003532601],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5208939,0.008953521,0.01361256,0.04165556,0.0004927307,0.001748215,0.01585455,0.0005026028,0.3962864],"genre_scores_gemma":[0.9496621,0.006596265,0.02745003,0.001608133,0.00007542718,0.0002476518,0.003627683,0.0001079362,0.01062473],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.9979632,"threshold_uncertainty_score":0.6351888,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.5315081919711923,"score_gpt":0.5692231138554606,"score_spread":0.03771492188426839,"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."}}