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{"title"=>"Electronic sensors for assessing interactions between healthcare workers and patients under airborne precautions", "type"=>"journal", "authors"=>[{"first_name"=>"Jean Christophe", "last_name"=>"Lucet", "scopus_author_id"=>"7006655229"}, {"first_name"=>"Cédric", "last_name"=>"Laouenan", "scopus_author_id"=>"27967842000"}, {"first_name"=>"Guillaume", "last_name"=>"Chelius", "scopus_author_id"=>"14038649900"}, {"first_name"=>"Nicolas", "last_name"=>"Veziris", "scopus_author_id"=>"6602331312"}, {"first_name"=>"Didier", "last_name"=>"Lepelletier", "scopus_author_id"=>"6602815849"}, {"first_name"=>"Adrien", "last_name"=>"Friggeri", "scopus_author_id"=>"36143857300"}, {"first_name"=>"Dominique", "last_name"=>"Abiteboul", "scopus_author_id"=>"6604034491"}, {"first_name"=>"Elisabeth", "last_name"=>"Bouvet", "scopus_author_id"=>"7006375078"}, {"first_name"=>"France", "last_name"=>"Mentre", "scopus_author_id"=>"7003610807"}, {"first_name"=>"Eric", "last_name"=>"Fleury", "scopus_author_id"=>"8339432200"}], "year"=>2012, "source"=>"PLoS ONE", "identifiers"=>{"issn"=>"19326203", "scopus"=>"2-s2.0-84861475456", "pui"=>"364882935", "doi"=>"10.1371/journal.pone.0037893", "isbn"=>"1932-6203 (Electronic)\\r1932-6203 (Linking)", "sgr"=>"84861475456", "pmid"=>"22662245"}, "id"=>"60dc04c1-2ac9-3391-89df-561037667729", "abstract"=>"BACKGROUND: Direct observation has been widely used to assess interactions between healthcare workers (HCWs) and patients but is time-consuming and feasible only over short periods. We used a Radio Frequency Identification Device (RFID) system to automatically measure HCW-patient interactions. METHODS: We equipped 50 patient rooms with fixed sensors and 111 HCW volunteers with mobile sensors in two clinical wards of two hospitals. For 3 months, we recorded all interactions between HCWs and 54 patients under airborne precautions for suspected (n = 40) or confirmed (n = 14) tuberculosis. Number and duration of HCW entries into patient rooms were collected daily. Concomitantly, we directly observed room entries and interviewed HCWs to evaluate their self-perception of the number and duration of contacts with tuberculosis patients. RESULTS: After signal reconstruction, 5490 interactions were recorded between 82 HCWs and 54 tuberculosis patients during 404 days of airborne isolation. Median (interquartile range) interaction duration was 2.1 (0.8-4.4) min overall, 2.3 (0.8-5.0) in the mornings, 1.8 (0.8-3.7) in the afternoons, and 2.0 (0.7-4.3) at night (P<10(-4)). Number of interactions/day/HCW was 3.0 (1.0-6.0) and total daily duration was 7.6 (2.4-22.5) min. Durations estimated from 28 direct observations and 26 interviews were not significantly different from those recorded by the network. CONCLUSIONS: The RFID was well accepted by HCWs. This original technique holds promise for accurately and continuously measuring interactions between HCWs and patients, as a less resource-consuming substitute for direct observation. The results could be used to model the transmission of significant pathogens. HCW perceptions of interactions with patients accurately reflected reality.", "link"=>"http://www.mendeley.com/research/electronic-sensors-assessing-interactions-between-healthcare-workers-patients-under-airborne-precaut", "reader_count"=>144, "reader_count_by_academic_status"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Researcher"=>22, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>40, "Student > Postgraduate"=>5, "Student > Master"=>34, "Other"=>8, "Student > Bachelor"=>24, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_user_role"=>{"Unspecified"=>2, "Professor > Associate Professor"=>4, "Researcher"=>22, "Student > Doctoral Student"=>1, "Student > Ph. D. Student"=>40, "Student > Postgraduate"=>5, "Student > Master"=>34, "Other"=>8, "Student > Bachelor"=>24, "Lecturer"=>1, "Professor"=>3}, "reader_count_by_subject_area"=>{"Unspecified"=>3, "Agricultural and Biological Sciences"=>22, "Arts and Humanities"=>3, "Business, Management and Accounting"=>2, "Computer Science"=>34, "Earth and Planetary Sciences"=>1, "Economics, Econometrics and Finance"=>1, "Energy"=>1, "Engineering"=>19, "Environmental Science"=>4, "Nursing and Health Professions"=>1, "Materials Science"=>1, "Mathematics"=>4, "Medicine and Dentistry"=>24, "Design"=>2, "Sports and Recreations"=>1, "Physics and Astronomy"=>13, "Psychology"=>2, "Social Sciences"=>6}, "reader_count_by_subdiscipline"=>{"Materials Science"=>{"Materials Science"=>1}, "Medicine and Dentistry"=>{"Medicine and Dentistry"=>24}, "Social Sciences"=>{"Social Sciences"=>6}, "Sports and Recreations"=>{"Sports and Recreations"=>1}, "Physics and Astronomy"=>{"Physics and Astronomy"=>13}, "Psychology"=>{"Psychology"=>2}, "Mathematics"=>{"Mathematics"=>4}, "Unspecified"=>{"Unspecified"=>3}, "Environmental Science"=>{"Environmental Science"=>4}, "Arts and Humanities"=>{"Arts and Humanities"=>3}, "Design"=>{"Design"=>2}, "Engineering"=>{"Engineering"=>19}, "Energy"=>{"Energy"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Economics, Econometrics and Finance"=>{"Economics, Econometrics and Finance"=>1}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>22}, "Computer Science"=>{"Computer Science"=>34}, "Business, Management and Accounting"=>{"Business, Management and Accounting"=>2}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}}, "reader_count_by_country"=>{"Canada"=>1, "Netherlands"=>1, "United States"=>5, "Brazil"=>2, "United Kingdom"=>1, "Italy"=>1, "France"=>5, "Switzerland"=>1, "Portugal"=>1, "Germany"=>3}, "group_count"=>5}

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• http://doi.org/10.1098/rspa.2019.0737
• http://doi.org/10.1007/s13278-018-0535-9
• http://doi.org/10.1371/journal.pone.0095425
• http://doi.org/10.2196/19437
• http://doi.org/10.1371/journal.pcbi.1004170
• http://doi.org/10.1142/S021952591550023X
• http://doi.org/10.1017/ice.2019.35
• http://doi.org/10.3390/a10010001
• http://doi.org/10.1017/S0950268817002035
• http://doi.org/10.1016/j.ajic.2016.01.023
• http://doi.org/10.1016/j.jpsic.2015.11.004
• http://doi.org/10.1016/j.socnet.2016.08.005
• http://doi.org/10.1017/ice.2015.89
• http://doi.org/10.1140/epjds/s13688-018-0140-1
• http://doi.org/10.1371/journal.pone.0070854
• http://doi.org/10.1038/s41598-018-20008-w
• http://doi.org/10.1016/j.rmr.2018.08.025
• http://doi.org/10.1371/journal.pone.0153690
• http://doi.org/10.5897/JVMAH2015.0434
• http://doi.org/10.1186/s12879-016-1676-y

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• {"id"=>"210497626599198720", "text"=>"This is cool: \"Electronic Sensors for Assessing Interactions between Healthcare Workers and Patients\" http://t.co/4zkG4HBf #analytics", "created_at"=>"2012-06-06T22:25:19Z", "user"=>"molecularist", "user_name"=>"Charlie Schick, PhD", "user_profile_image"=>"http://a0.twimg.com/profile_images/1214068794/Charlie_Schick_Thumbnail__normal.jpg"}
• {"id"=>"210497752449298432", "text"=>"This is cool: \"Electronic Sensors for Assessing Interactions between Healthcare Workers and Patients\" http://t.co/Aue0hjpi #analytics", "created_at"=>"2012-06-06T22:25:49Z", "user"=>"IBMBigDataHLS", "user_name"=>"IBM Big Data HLS", "user_profile_image"=>"http://a0.twimg.com/profile_images/1808658589/IBMBigDataHLS_normal.jpg"}
• {"id"=>"212952552139010048", "text"=>"What can we discover when measuring interactions via sensors? #healthcare examples: http://t.co/4zkG4HBf http://t.co/cveHy6fQ #qs #anlaytics", "created_at"=>"2012-06-13T17:00:19Z", "user"=>"molecularist", "user_name"=>"Charlie Schick, PhD", "user_profile_image"=>"http://a0.twimg.com/profile_images/1214068794/Charlie_Schick_Thumbnail__normal.jpg"}
• {"id"=>"212955155467993088", "text"=>"RT @molecularist: What can we discover when measuring interactions via sensors? #healthcare examples: http://t.co/4zkG4HBf http://t.co/cveHy6fQ #qs #anlaytics", "created_at"=>"2012-06-13T17:10:40Z", "user"=>"asaarinen", "user_name"=>"asaarinen", "user_profile_image"=>"http://a0.twimg.com/profile_images/53008837/AM_-_11_normal.jpg"}
• {"id"=>"213277110540836865", "text"=>"Electronic sensors measure heatlhcare staff interactons. http://t.co/Aue0hjpi Imagine adding this data to operational #analytics?", "created_at"=>"2012-06-14T14:30:00Z", "user"=>"IBMBigDataHLS", "user_name"=>"IBM Big Data HLS", "user_profile_image"=>"http://a0.twimg.com/profile_images/1808658589/IBMBigDataHLS_normal.jpg"}
• {"id"=>"711959525700542464", "text"=>"Communication with our product QStamp(R) can be achieve by using RF, i. e., Radio Frequency. Check out Lucet et al. https://t.co/mzOuWjJmpK", "created_at"=>"2016-03-21T16:55:53Z", "user"=>"mater_dynamics", "user_name"=>"Mater Dynamics", "user_profile_image"=>"http://pbs.twimg.com/profile_images/673904810014437376/9I0CFeMd_normal.png"}

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Figshare

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• {"files"=>["https://ndownloader.figshare.com/files/632729"], "description"=>"<p>Footnote: The graphs show cumulative data from 5490 interactions during the 3-month study period in both study wards.</p>", "links"=>[], "tags"=>["duration", "5490", "interactions", "82", "healthcare", "workers", "54", "patients", "placed", "airborne", "precautions"], "article_id"=>303203, "categories"=>["Microbiology", "Medicine", "Infectious Diseases", "Biotechnology"], "users"=>["Jean-Christophe Lucet", "Cédric Laouenan", "Guillaume Chelius", "Nicolas Veziris", "Didier Lepelletier", "Adrien Friggeri", "Dominique Abiteboul", "Elisabeth Bouvet", "France Mentré", "Eric Fleury"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0037893.g002", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Distribution_of_A_number_per_hour_and_B_mean_duration_per_hour_of_5490_interactions_between_82_healthcare_workers_and_54_patients_placed_under_airborne_precautions_over_one_week_during_the_study_period_/303203", "title"=>"Distribution of (A) number per hour and (B) mean duration per hour of 5490 interactions between 82 healthcare workers and 54 patients placed under airborne precautions over one week during the study period.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2013-02-20 04:41:03"}

PMC Usage Stats | Further Information

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Relative Metric

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F1000Prime