@article{GreenbaumEtAl17,
	author = {Greenbaum, Nathaniel R and Jernite, Yacine and Halpern, Yoni and Calder, Shelley and Nathanson, Larry A. and Sontag, David and Horng, Steven},
	title = {Contextual Autocomplete: A Novel User Interface Using Machine Learning to Improve Ontology Usage and Structured Data Capture for Presenting Problems in the Emergency Department},
	year = {2017},
	journal = {bioRxiv:10.1101/127092},
        keywords = {Health care},
        url_Paper = {https://www.biorxiv.org/content/early/2017/04/12/127092.full.pdf},
abstract = {Our objective is to determine the effect of contextual autocomplete, a user interface that uses machine learning, on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED). We used contextual autocomplete, a user interface that ranks concepts by their predicted probability, to help nurses enter data about a patient’s reason for visiting the ED. Predicted probabilities were calculated using a previously derived model based on triage vital signs and a brief free text note. We evaluated the percentage and quality of structured data captured using a prospective before-and-after study design. A total of 279,231 patient encounters were analyzed. Structured data capture improved from 26.2\% to 97.2\% (p<0.0001). During the post-implementation period, presenting problems
were more complete (3.35 vs 3.66; p=0.0004), as precise (3.59 vs. 3.74; p=0.1), and higher in overall quality (3.38 vs. 3.72; p=0.0002). Our system reduced the mean number of keystrokes required to document a presenting problem from 11.6 to 0.6 (p<0.0001), a 95\% improvement. We have thus demonstrated a technique that captures structured data on nearly all patients. We estimate that our system reduces the number of man-hours required annually to type presenting problems at our institution from 92.5 hours to 4.8 hours. In conclusion, implementation of a contextual autocomplete system resulted in improved structured data capture, ontology usage compliance, and data quality.}
}

{"_id":"HDSYnpcKgGYebbhMy","bibbaseid":"greenbaum-jernite-halpern-calder-nathanson-sontag-horng-contextualautocompleteanoveluserinterfaceusingmachinelearningtoimproveontologyusageandstructureddatacaptureforpresentingproblemsintheemergencydepartment-2017","author_short":["Greenbaum, N. R","Jernite, Y.","Halpern, Y.","Calder, S.","Nathanson, L. A.","Sontag, D.","Horng, S."],"bibdata":{"bibtype":"article","type":"article","author":[{"propositions":[],"lastnames":["Greenbaum"],"firstnames":["Nathaniel","R"],"suffixes":[]},{"propositions":[],"lastnames":["Jernite"],"firstnames":["Yacine"],"suffixes":[]},{"propositions":[],"lastnames":["Halpern"],"firstnames":["Yoni"],"suffixes":[]},{"propositions":[],"lastnames":["Calder"],"firstnames":["Shelley"],"suffixes":[]},{"propositions":[],"lastnames":["Nathanson"],"firstnames":["Larry","A."],"suffixes":[]},{"propositions":[],"lastnames":["Sontag"],"firstnames":["David"],"suffixes":[]},{"propositions":[],"lastnames":["Horng"],"firstnames":["Steven"],"suffixes":[]}],"title":"Contextual Autocomplete: A Novel User Interface Using Machine Learning to Improve Ontology Usage and Structured Data Capture for Presenting Problems in the Emergency Department","year":"2017","journal":"bioRxiv:10.1101/127092","keywords":"Health care","url_paper":"https://www.biorxiv.org/content/early/2017/04/12/127092.full.pdf","abstract":"Our objective is to determine the effect of contextual autocomplete, a user interface that uses machine learning, on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED). We used contextual autocomplete, a user interface that ranks concepts by their predicted probability, to help nurses enter data about a patient’s reason for visiting the ED. Predicted probabilities were calculated using a previously derived model based on triage vital signs and a brief free text note. We evaluated the percentage and quality of structured data captured using a prospective before-and-after study design. A total of 279,231 patient encounters were analyzed. Structured data capture improved from 26.2% to 97.2% (p<0.0001). During the post-implementation period, presenting problems were more complete (3.35 vs 3.66; p=0.0004), as precise (3.59 vs. 3.74; p=0.1), and higher in overall quality (3.38 vs. 3.72; p=0.0002). Our system reduced the mean number of keystrokes required to document a presenting problem from 11.6 to 0.6 (p<0.0001), a 95% improvement. We have thus demonstrated a technique that captures structured data on nearly all patients. We estimate that our system reduces the number of man-hours required annually to type presenting problems at our institution from 92.5 hours to 4.8 hours. In conclusion, implementation of a contextual autocomplete system resulted in improved structured data capture, ontology usage compliance, and data quality.","bibtex":"@article{GreenbaumEtAl17,\n\tauthor = {Greenbaum, Nathaniel R and Jernite, Yacine and Halpern, Yoni and Calder, Shelley and Nathanson, Larry A. and Sontag, David and Horng, Steven},\n\ttitle = {Contextual Autocomplete: A Novel User Interface Using Machine Learning to Improve Ontology Usage and Structured Data Capture for Presenting Problems in the Emergency Department},\n\tyear = {2017},\n\tjournal = {bioRxiv:10.1101/127092},\n keywords = {Health care},\n url_Paper = {https://www.biorxiv.org/content/early/2017/04/12/127092.full.pdf},\nabstract = {Our objective is to determine the effect of contextual autocomplete, a user interface that uses machine learning, on the efficiency and quality of documentation of presenting problems (chief complaints) in the emergency department (ED). We used contextual autocomplete, a user interface that ranks concepts by their predicted probability, to help nurses enter data about a patient’s reason for visiting the ED. Predicted probabilities were calculated using a previously derived model based on triage vital signs and a brief free text note. We evaluated the percentage and quality of structured data captured using a prospective before-and-after study design. A total of 279,231 patient encounters were analyzed. Structured data capture improved from 26.2\\% to 97.2\\% (p<0.0001). During the post-implementation period, presenting problems\nwere more complete (3.35 vs 3.66; p=0.0004), as precise (3.59 vs. 3.74; p=0.1), and higher in overall quality (3.38 vs. 3.72; p=0.0002). Our system reduced the mean number of keystrokes required to document a presenting problem from 11.6 to 0.6 (p<0.0001), a 95\\% improvement. We have thus demonstrated a technique that captures structured data on nearly all patients. We estimate that our system reduces the number of man-hours required annually to type presenting problems at our institution from 92.5 hours to 4.8 hours. In conclusion, implementation of a contextual autocomplete system resulted in improved structured data capture, ontology usage compliance, and data quality.}\n}\n\n","author_short":["Greenbaum, N. R","Jernite, Y.","Halpern, Y.","Calder, S.","Nathanson, L. A.","Sontag, D.","Horng, S."],"key":"GreenbaumEtAl17","id":"GreenbaumEtAl17","bibbaseid":"greenbaum-jernite-halpern-calder-nathanson-sontag-horng-contextualautocompleteanoveluserinterfaceusingmachinelearningtoimproveontologyusageandstructureddatacaptureforpresentingproblemsintheemergencydepartment-2017","role":"author","urls":{" paper":"https://www.biorxiv.org/content/early/2017/04/12/127092.full.pdf"},"keyword":["Health care"],"metadata":{"authorlinks":{}},"downloads":0,"html":""},"bibtype":"article","biburl":"http://people.csail.mit.edu/dsontag/papers/bibtex/david_sontag_papers_all.bib","dataSources":["g3ofqhxNQWsRWkCrp"],"keywords":["health care"],"search_terms":["contextual","autocomplete","novel","user","interface","using","machine","learning","improve","ontology","usage","structured","data","capture","presenting","problems","emergency","department","greenbaum","jernite","halpern","calder","nathanson","sontag","horng"],"title":"Contextual Autocomplete: A Novel User Interface Using Machine Learning to Improve Ontology Usage and Structured Data Capture for Presenting Problems in the Emergency Department","year":2017}