{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.005408369,0.0004271063,0.0009551279,0.0009860717,0.001453279,0.001116727,0.0005103902,0.0009017285,0.002027863],"category_scores_gemma":[0.01653554,0.0005574471,0.0009939437,0.0006396875,0.0009011728,0.001206344,0.0009885465,0.001552075,0.0005218216],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001087532,"about_ca_system_score_gemma":0.001681817,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002730806,"about_ca_topic_score_gemma":0.003651817,"domain_scores_codex":[0.9963505,0.001877241,0.000517935,0.0003631418,0.0005220771,0.0003689615],"domain_scores_gemma":[0.9857783,0.00548572,0.003596924,0.0007360082,0.002500135,0.001902951],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.001739718,0.0135722,0.9321083,0.0004891056,0.0002212654,0.0009196832,0.03314868,0.0001436396,0.001256988,0.0001480523,0.001101805,0.01515062],"study_design_scores_gemma":[0.0007229641,0.04329443,0.9038195,0.0002003542,0.0002112758,0.001265336,0.04284875,0.001344901,0.0008001308,0.0001746198,0.005194347,0.0001234376],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9983474,0.00007791332,0.0002574507,0.00006281395,0.00001068965,0.000636715,0.0001542977,0.000004972867,0.0004476736],"genre_scores_gemma":[0.996557,0.0001256523,0.001283354,0.0002380165,0.00002083398,0.001148489,0.000260932,0.000006568675,0.0003592704],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005408369,"threshold_uncertainty_score":0.0286026,"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."}}