{"id":"W4280526860","doi":"10.2196/31758","title":"Assessing the Usability of a Clinical Decision Support System: Heuristic Evaluation","year":2022,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":49,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institute of Nursing Research; National Institutes of Health","keywords":"Usability; Heuristic evaluation; Computer science; Checklist; Clinical decision support system; Cognitive walkthrough; Heuristics; System usability scale; Usability engineering; Heuristic; Usability goals; Decision support system; Software deployment; Human–computer interaction; Psychology; Software engineering; Artificial intelligence","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":[0.09181553,0.001108619,0.001197955,0.003764187,0.001100707,0.002197558,0.001166994,0.001115175,0.00136746],"category_scores_gemma":[0.1966026,0.000596646,0.001575562,0.002426045,0.001791489,0.001298597,0.001695248,0.0006945163,0.0001423504],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003750413,"about_ca_system_score_gemma":0.005654128,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001420937,"about_ca_topic_score_gemma":0.002293734,"domain_scores_codex":[0.8724077,0.104997,0.0113451,0.00184176,0.00836145,0.001047054],"domain_scores_gemma":[0.649738,0.3126511,0.01179325,0.004502659,0.01979266,0.001522358],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"qualitative","study_design_scores_codex":[0.01868657,0.01263641,0.09565713,0.0372738,0.00300271,0.0006185039,0.03624756,0.02862545,0.009903875,0.006317093,0.003144059,0.7478869],"study_design_scores_gemma":[0.03395832,0.2132287,0.2855042,0.02887975,0.01443092,0.001973217,0.06377672,0.2214437,0.07028446,0.03101161,0.03340689,0.002101492],"study_design_candidate":"qualitative","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8809404,0.003014068,0.07513941,0.0005212983,0.0001165918,0.03306948,0.0003046573,0.0001280952,0.006766005],"genre_scores_gemma":[0.764716,0.001153074,0.2152027,0.0002717319,0.00003805167,0.01810077,0.0001970203,0.00001914959,0.0003015882],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.09181553,"threshold_uncertainty_score":0.4855728,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3244422152217981,"score_gpt":0.6158100358963237,"score_spread":0.2913678206745256,"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."}}