{"id":"W4417274060","doi":"10.1109/rbme.2025.3632213","title":"FHIR in Focus: Enabling Biomedical Data Harmonization for Intelligent Healthcare Systems","year":2025,"lang":"en","type":"article","venue":"IEEE Reviews in Biomedical Engineering","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Shriners Hospitals for Children","keywords":"Interoperability; Health care; Health informatics; Identification (biology); Digital health; Big data; Scalability; Data integration; Analytics","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.009243161,0.0003320051,0.00124197,0.001067814,0.0001595861,0.00001689148,0.0008642353,0.0006511656,0.00002339209],"category_scores_gemma":[0.003867434,0.0002905644,0.00007627212,0.00236627,0.00005014145,0.0001592446,0.0002074371,0.00144705,0.00007461485],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001829918,"about_ca_system_score_gemma":0.001354827,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001311413,"about_ca_topic_score_gemma":0.0004323272,"domain_scores_codex":[0.9933548,0.0007851499,0.003234814,0.0007942038,0.000407552,0.001423478],"domain_scores_gemma":[0.9965581,0.001649203,0.0003296054,0.0009714772,0.0001031657,0.0003883964],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001684071,0.0008992712,0.009203963,0.1950834,0.0001651536,0.00007626943,0.003414917,0.0008816058,0.002488445,0.01626671,0.07478958,0.6965622],"study_design_scores_gemma":[0.0008839887,0.00006696241,0.00008207675,0.01860317,0.00001244884,0.000003014011,0.00022578,0.1345652,0.00001083042,0.00005266956,0.8452756,0.000218208],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.001921806,0.1925335,0.7698236,0.008472864,0.01503975,0.01165993,0.0001459956,0.0002689134,0.0001336359],"genre_scores_gemma":[0.6199914,0.2991071,0.02791401,0.00598715,0.01248368,0.02740913,0.003196542,0.0006883785,0.003222628],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.7704861,"threshold_uncertainty_score":0.9999546,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1483401452004359,"score_gpt":0.4702770842050913,"score_spread":0.3219369390046554,"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."}}