{"id":{"repo_id":"ottawa-retro","oai_identifier":"oai:ruor.uottawa.ca:10393/31190"},"canonical_url":"https://search.dev.ndltd.org/etd/ottawa-retro/oai:ruor.uottawa.ca:10393/31190","repository":{"repo_id":"ottawa-retro","name":"University of Ottawa","base_url":"https://ruor.uottawa.ca/server/oai/request"},"display":{"title":"Developing an Electronic Hospital Trigger for Bleeding – The Ottawa Hospital ETriggers Project","abstract":"Background Bleeding can be an adverse side effect from hospital treatment. The aim was to develop an electronic identification method for patients who are bleeding within The Ottawa Hospital. Methods A retrospective exploratory cohort (N=1000) was used to identify potential candidate markers for bleeding. Electronic data were extracted to evaluate candidate identifiers. Data which were associated with bleeding events were assessed in a model derivation cohort (N=700). Multivariate analysis was used to establish the best model for identifying all bleeding events and in-hospital bleeding events. Results Overall 38% of the exploratory cohort had bleeding. In the model derivation set 29% had bleeding. The model predicting all bleeding included number of transfusions, admitting specialty, re-operation and endoscopy (C-statistic 0.82, 95%CI 0.79-0.86). The model predicting in-hospital bleeding included number of transfusions, admitting specialty and re-operation (C-statistic 0.78, 95% CI 0.73-0.84). Conclusion We have developed two models for identifying hospital bleeding events from The Ottawa Hospital electronic medical records. These should be validated prospectively on the hospital-wide population.","abstract_html":"Background Bleeding can be an adverse side effect from hospital treatment. The aim was to develop an electronic identification method for patients who are bleeding within The Ottawa Hospital. Methods A retrospective exploratory cohort (N=1000) was used to identify potential candidate markers for bleeding. Electronic data were extracted to evaluate candidate identifiers. Data which were associated with bleeding events were assessed in a model derivation cohort (N=700). Multivariate analysis was used to establish the best model for identifying all bleeding events and in-hospital bleeding events. Results Overall 38% of the exploratory cohort had bleeding. In the model derivation set 29% had bleeding. The model predicting all bleeding included number of transfusions, admitting specialty, re-operation and endoscopy (C-statistic 0.82, 95%CI 0.79-0.86). The model predicting in-hospital bleeding included number of transfusions, admitting specialty and re-operation (C-statistic 0.78, 95% CI 0.73-0.84). Conclusion We have developed two models for identifying hospital bleeding events from The Ottawa Hospital electronic medical records. These should be validated prospectively on the hospital-wide population.","abstract_has_math":false,"creators":["de Wit, Kerstin"],"institution":"Université d'Ottawa / University of Ottawa","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Forster, Alan","Wells, Philip"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2014,"date_issued":"2014-06-17T20:09:23Z","date_published":"2014-06-17T20:09:23Z","updated_at":"2026-07-24T03:39:20Z","subjects":["bleeding","electronic medical records","electronic identification"],"languages":["en"],"rights":[],"rights_urls":[],"identifier_entries":[{"key":"dc:identifier","label":"Identifier","values":["http://dx.doi.org/10.20381/ruor-3786"],"render_values":[{"text":"http://dx.doi.org/10.20381/ruor-3786","href":"http://dx.doi.org/10.20381/ruor-3786","code":true}]}]},"links":{"outbound_url":"http://hdl.handle.net/10393/31190","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Forster, Alan","Wells, Philip"]},{"key":"dc:creator","label":"Author","values":["de Wit, Kerstin"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2014-06-17T20:09:23Z","2014"]},{"key":"dc:publisher","label":"Institution","values":["Université d'Ottawa / University of Ottawa"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["bleeding","electronic medical records","electronic identification"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["en"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10393/31190","http://dx.doi.org/10.20381/ruor-3786"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Background Bleeding can be an adverse side effect from hospital treatment. The aim was to develop an electronic identification method for patients who are bleeding within The Ottawa Hospital. Methods A retrospective exploratory cohort (N=1000) was used to identify potential candidate markers for bleeding. Electronic data were extracted to evaluate candidate identifiers. Data which were associated with bleeding events were assessed in a model derivation cohort (N=700). Multivariate analysis was used to establish the best model for identifying all bleeding events and in-hospital bleeding events. Results Overall 38% of the exploratory cohort had bleeding. In the model derivation set 29% had bleeding. The model predicting all bleeding included number of transfusions, admitting specialty, re-operation and endoscopy (C-statistic 0.82, 95%CI 0.79-0.86). The model predicting in-hospital bleeding included number of transfusions, admitting specialty and re-operation (C-statistic 0.78, 95% CI 0.73-0.84). Conclusion We have developed two models for identifying hospital bleeding events from The Ottawa Hospital electronic medical records. These should be validated prospectively on the hospital-wide population."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Developing an Electronic Hospital Trigger for Bleeding – The Ottawa Hospital ETriggers Project"]}]}],"canonical_facts":{"dc:contributor":["Forster, Alan","Wells, Philip"],"dc:creator":["de Wit, Kerstin"],"dc:date":["2014-06-17T20:09:23Z","2014"],"dc:description":["Background Bleeding can be an adverse side effect from hospital treatment. The aim was to develop an electronic identification method for patients who are bleeding within The Ottawa Hospital. Methods A retrospective exploratory cohort (N=1000) was used to identify potential candidate markers for bleeding. Electronic data were extracted to evaluate candidate identifiers. Data which were associated with bleeding events were assessed in a model derivation cohort (N=700). Multivariate analysis was used to establish the best model for identifying all bleeding events and in-hospital bleeding events. Results Overall 38% of the exploratory cohort had bleeding. In the model derivation set 29% had bleeding. The model predicting all bleeding included number of transfusions, admitting specialty, re-operation and endoscopy (C-statistic 0.82, 95%CI 0.79-0.86). The model predicting in-hospital bleeding included number of transfusions, admitting specialty and re-operation (C-statistic 0.78, 95% CI 0.73-0.84). Conclusion We have developed two models for identifying hospital bleeding events from The Ottawa Hospital electronic medical records. These should be validated prospectively on the hospital-wide population."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10393/31190","http://dx.doi.org/10.20381/ruor-3786"],"dc:language":["en"],"dc:publisher":["Université d'Ottawa / University of Ottawa"],"dc:subject":["bleeding","electronic medical records","electronic identification"],"dc:title":["Developing an Electronic Hospital Trigger for Bleeding – The Ottawa Hospital ETriggers Project"],"dc:type":["Thesis"]},"updated_at":"2026-07-24T03:39:20Z"}