{"id":"W4381550299","doi":"10.2196/41552","title":"Alerts and Collections for Automating Patients’ Sensemaking and Organizing of Their Electronic Health Record Data for Reflection, Planning, and Clinical Visits: Qualitative Research-Through-Design Study","year":2023,"lang":"en","type":"article","venue":"JMIR Human Factors","topic":"Electronic Health Records Systems","field":"Health Professions","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health","keywords":"Sensemaking; Computer science; Data science; Thematic analysis; Context (archaeology); Reflection (computer programming); Identification (biology); Qualitative research; Knowledge management","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":[0.06651645,0.001063067,0.001224224,0.002292701,0.00836671,0.004459779,0.003030127,0.002427131,0.003424484],"category_scores_gemma":[0.06554237,0.001137265,0.0008184637,0.001575958,0.01070442,0.004838069,0.006655552,0.003281716,0.0005820831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006650837,"about_ca_system_score_gemma":0.01075183,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003411739,"about_ca_topic_score_gemma":0.00492916,"domain_scores_codex":[0.9465333,0.04571312,0.001518814,0.00187175,0.001775692,0.002587402],"domain_scores_gemma":[0.9029856,0.08158229,0.003607175,0.002348105,0.005808912,0.003667949],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"qualitative","study_design_scores_codex":[0.0001026769,0.0002975195,0.003065365,0.0005988204,0.00001300654,0.000408448,0.9848689,0.00005212775,0.0009525296,0.001141725,0.0005892001,0.007909689],"study_design_scores_gemma":[0.00006373748,0.0003846232,0.00137052,0.000368653,0.0000163454,0.0001670785,0.9902425,0.0001729108,0.0007270134,0.0005756568,0.005883487,0.00002737373],"study_design_candidate":"qualitative","study_design_consensus":"qualitative","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9814238,0.0006414634,0.008312554,0.002333008,0.00008841295,0.003536142,0.0003222573,0.0000483195,0.003293995],"genre_scores_gemma":[0.9790261,0.000666696,0.01152751,0.001368897,0.00003147489,0.004874304,0.0001165002,0.00005427586,0.002334182],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06651645,"threshold_uncertainty_score":0.3517768,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.6512770456689191,"score_gpt":0.6739336910558511,"score_spread":0.02265664538693202,"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."}}