{"id":"W2164674019","doi":"10.2196/mhealth.3216","title":"Mobile Technologies and Geographic Information Systems to Improve Health Care Systems: A Literature Review","year":2014,"lang":"en","type":"review","venue":"JMIR mhealth and uhealth","topic":"Data-Driven Disease Surveillance","field":"Medicine","cited_by":76,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Geographic information system; Health care; Scopus; Information system; mHealth; Mobile technology; Mobile phone; Clinical decision support system; Computer science; Medicine; Knowledge management; Decision support system; Mobile device; MEDLINE; World Wide Web; Nursing; Geography; Psychological intervention; Data mining; Telecommunications; Engineering; Political science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.004333882,0.001040674,0.002219149,0.01728624,0.0007649897,0.003359093,0.00150984,0.002557517,0.004895444],"category_scores_gemma":[0.01518759,0.0006439961,0.001796133,0.02734361,0.001024487,0.004425854,0.001218613,0.001155518,0.0006787944],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.003413595,"about_ca_system_score_gemma":0.008736745,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00678873,"about_ca_topic_score_gemma":0.01072932,"domain_scores_codex":[0.9968902,0.001088007,0.0007697322,0.0002264342,0.0008631977,0.0001623909],"domain_scores_gemma":[0.9802368,0.01594706,0.001472629,0.000136524,0.002009426,0.0001976009],"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":[0.00006985294,0.00008264146,0.001488485,0.3897993,0.0008203664,0.0004307549,0.001148598,0.0006385505,0.0002880444,0.005726166,0.01671382,0.5827934],"study_design_scores_gemma":[0.0000667693,0.000193283,0.008390676,0.6701185,0.003506038,0.001761965,0.002653415,0.0006109495,0.0003812843,0.003567546,0.3086755,0.00007397366],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.000238183,0.9982685,0.00009890894,0.0005869714,0.0001102857,0.00002815044,0.00002468895,0.000003064172,0.0006411693],"genre_scores_gemma":[0.002148594,0.9970815,0.0003609172,0.0002127693,0.00008471946,0.00002945307,0.00001978553,0.0000010727,0.000061177],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.01728624,"threshold_uncertainty_score":0.02476746,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0201536974396192,"score_gpt":0.3756825502228389,"score_spread":0.3555288527832197,"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."}}