{"id":"W2398358912","doi":"10.2196/ijmr.5462","title":"An Observational Study to Evaluate the Usability and Intent to Adopt an Artificial Intelligence–Powered Medication Reconciliation Tool","year":2016,"lang":"en","type":"article","venue":"Interactive Journal of Medical Research","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Office of the Assistant Secretary for Health; United States Drug Enforcement Administration; Health Resources and Services Administration; Office of Disease Prevention and Health Promotion; Division of Civil, Mechanical and Manufacturing Innovation; U.S. Department of Defense; Agency for Healthcare Research and Quality; U.S. Department of Health and Human Services; National Institutes of Health; Centers for Disease Control and Prevention; Administration for Community Living; Substance Abuse and Mental Health Services Administration; U.S. Department of Veterans Affairs","keywords":"Usability; Workflow; Observational study; Computer science; Service (business); Medication Reconciliation; Process (computing); Medicine; Medical education; Medical emergency; Nursing; Pharmacy; Pharmacist; Human–computer interaction; Database","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch","insufficient_payload"],"consensus_categories":["metaresearch"],"category_scores_codex":[0.07690579,0.0001469661,0.0003936366,0.000395181,0.0005843796,0.00004530012,0.0009688159,0.0001782985,0.00222231],"category_scores_gemma":[0.07416263,0.00007601824,0.00004978296,0.0005422555,0.0001756256,0.0006091265,0.0002371087,0.002268561,0.000204991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00182786,"about_ca_system_score_gemma":0.004764684,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001000435,"about_ca_topic_score_gemma":0.004321337,"domain_scores_codex":[0.9795769,0.01239389,0.00204084,0.0004208168,0.00483153,0.0007359602],"domain_scores_gemma":[0.9799764,0.01269383,0.0004019142,0.0005268722,0.005065012,0.001335995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.003862411,0.001556603,0.04680083,0.00006281309,0.0001100139,0.00001736573,0.0475375,0.000004937592,0.004308688,0.001733665,0.004180998,0.8898242],"study_design_scores_gemma":[0.002376967,0.03189712,0.6631461,0.004208962,0.00006031624,0.00007351805,0.2447009,0.006749139,0.001096142,0.02244734,0.02274609,0.0004973135],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8845275,0.00002900266,0.01542982,0.0964978,0.001224909,0.002202888,0.000008449236,0.00001077867,0.00006883477],"genre_scores_gemma":[0.9965491,0.00006312343,0.0002093663,0.001383796,0.001451708,0.0002487084,0.000002039772,0.00001798133,0.00007413809],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8893269,"threshold_uncertainty_score":0.9986898,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6262853964431453,"score_gpt":0.6681146901595232,"score_spread":0.04182929371637789,"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."}}