An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study
<h3>Background</h3><p dir="ltr">A coordinated care system helps provide timely access to treatment for suspected acute stroke. In Northwestern Ontario (NWO), Canada, communities are widespread with several hospitals offering various diagnostic equipment and services. Thus...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| مؤلفون آخرون: | , , , , , , , , , |
| منشور في: |
2024
|
| الموضوعات: | |
| الوسوم: |
إضافة وسم
لا توجد وسوم, كن أول من يضع وسما على هذه التسجيلة!
|
| _version_ | 1864513543235174400 |
|---|---|
| author | Ayman Hassan (14426412) |
| author2 | Rachid Benlamri (17541624) Trina Diner (21400745) Keli Cristofaro (21400748) Lucas Dillistone (21400751) Hajar Khallouki (21400754) Mahvareh Ahghari (21400757) Shalyn Littlefield (21400760) Rabail Siddiqui (21400763) Russell MacDonald (20377037) David W Savage (21400766) |
| author2_role | author author author author author author author author author author |
| author_facet | Ayman Hassan (14426412) Rachid Benlamri (17541624) Trina Diner (21400745) Keli Cristofaro (21400748) Lucas Dillistone (21400751) Hajar Khallouki (21400754) Mahvareh Ahghari (21400757) Shalyn Littlefield (21400760) Rabail Siddiqui (21400763) Russell MacDonald (20377037) David W Savage (21400766) |
| author_role | author |
| dc.creator.none.fl_str_mv | Ayman Hassan (14426412) Rachid Benlamri (17541624) Trina Diner (21400745) Keli Cristofaro (21400748) Lucas Dillistone (21400751) Hajar Khallouki (21400754) Mahvareh Ahghari (21400757) Shalyn Littlefield (21400760) Rabail Siddiqui (21400763) Russell MacDonald (20377037) David W Savage (21400766) |
| dc.date.none.fl_str_mv | 2024-08-01T00:00:00Z |
| dc.identifier.none.fl_str_mv | 10.2196/54009 |
| dc.relation.none.fl_str_mv | https://figshare.com/articles/journal_contribution/An_App_for_Navigating_Patient_Transportation_and_Acute_Stroke_Care_in_Northwestern_Ontario_Using_Machine_Learning_Retrospective_Study/29605166 |
| dc.rights.none.fl_str_mv | CC BY 4.0 info:eu-repo/semantics/openAccess |
| dc.subject.none.fl_str_mv | Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Neurosciences Health sciences Health services and systems Public health Information and computing sciences Artificial intelligence Data management and data science Distributed computing and systems software Human-centred computing Information systems Machine learning Stroke care Acute stroke Northwestern Ontario Predictions Models Geography Machine learning Stroke Cardiovascular Brain Neuroscience TIA Transient ischemic attack Coordinated care Navigation Navigating mHealth Mobile health App Apps Applications Geomapping Geographical Location Spatial Predict Predictive |
| dc.title.none.fl_str_mv | An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study |
| dc.type.none.fl_str_mv | Text Journal contribution info:eu-repo/semantics/publishedVersion text contribution to journal |
| description | <h3>Background</h3><p dir="ltr">A coordinated care system helps provide timely access to treatment for suspected acute stroke. In Northwestern Ontario (NWO), Canada, communities are widespread with several hospitals offering various diagnostic equipment and services. Thus, resources are limited, and health care providers must often transfer patients with stroke to different hospital locations to ensure the most appropriate care access within recommended time frames. However, health care providers frequently situated temporarily (locum) in NWO or providing care remotely from other areas of Ontario may lack sufficient information and experience in the region to access care for a patient with a time-sensitive condition. Suboptimal decision-making may lead to multiple transfers before definitive stroke care is obtained, resulting in poor outcomes and additional health care system costs.</p><h3>Objectives</h3><p dir="ltr">We aimed to develop a tool to inform and assist NWO health care providers in determining the best transfer options for patients with stroke to provide the most efficient care access. We aimed to develop an app using a comprehensive geomapping navigation and estimation system based on machine learning algorithms. This app uses key stroke-related timelines including the last time the patient was known to be well, patient location, treatment options, and imaging availability at different health care facilities.</p><h3>Methods</h3><p dir="ltr">Using historical data (2008-2020), an accurate prediction model using machine learning methods was developed and incorporated into a mobile app. These data contained parameters regarding air (Ornge) and land medical transport (3 services), which were preprocessed and cleaned. For cases in which Ornge air services and land ambulance medical transport were both involved in a patient transport process, data were merged and time intervals of the transport journey were determined. The data were distributed for training (35%), testing (35%), and validation (30%) of the prediction model.</p><h3>Results</h3><p dir="ltr">In total, 70,623 records were collected in the data set from Ornge and land medical transport services to develop a prediction model. Various learning models were analyzed; all learning models perform better than the simple average of all points in predicting output variables. The decision tree model provided more accurate results than the other models. The decision tree model performed remarkably well, with the values from testing, validation, and the model within a close range. This model was used to develop the “NWO Navigate Stroke” system. The system provides accurate results and demonstrates that a mobile app can be a significant tool for health care providers navigating stroke care in NWO, potentially impacting patient care and outcomes.</p><h3>Conclusions</h3><p dir="ltr">The NWO Navigate Stroke system uses a data-driven, reliable, accurate prediction model while considering all variations and is simultaneously linked to all required acute stroke management pathways and tools. It was tested using historical data, and the next step will to involve usability testing with end users.</p><h2>Other Information</h2><p dir="ltr">Published in: JMIR Formative Research<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/54009" target="_blank">https://dx.doi.org/10.2196/54009</a></p> |
| eu_rights_str_mv | openAccess |
| id | Manara2_c96fe923e4ee378a620e873d97ed675e |
| identifier_str_mv | 10.2196/54009 |
| network_acronym_str | Manara2 |
| network_name_str | Manara2 |
| oai_identifier_str | oai:figshare.com:article/29605166 |
| publishDate | 2024 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| rights_invalid_str_mv | CC BY 4.0 |
| spelling | An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective StudyAyman Hassan (14426412)Rachid Benlamri (17541624)Trina Diner (21400745)Keli Cristofaro (21400748)Lucas Dillistone (21400751)Hajar Khallouki (21400754)Mahvareh Ahghari (21400757)Shalyn Littlefield (21400760)Rabail Siddiqui (21400763)Russell MacDonald (20377037)David W Savage (21400766)Biomedical and clinical sciencesCardiovascular medicine and haematologyClinical sciencesNeurosciencesHealth sciencesHealth services and systemsPublic healthInformation and computing sciencesArtificial intelligenceData management and data scienceDistributed computing and systems softwareHuman-centred computingInformation systemsMachine learningStroke careAcute strokeNorthwesternOntarioPredictionsModelsGeographyMachine learningStrokeCardiovascularBrainNeuroscienceTIATransient ischemic attackCoordinated careNavigationNavigatingmHealthMobile healthAppAppsApplicationsGeomappingGeographicalLocationSpatialPredictPredictive<h3>Background</h3><p dir="ltr">A coordinated care system helps provide timely access to treatment for suspected acute stroke. In Northwestern Ontario (NWO), Canada, communities are widespread with several hospitals offering various diagnostic equipment and services. Thus, resources are limited, and health care providers must often transfer patients with stroke to different hospital locations to ensure the most appropriate care access within recommended time frames. However, health care providers frequently situated temporarily (locum) in NWO or providing care remotely from other areas of Ontario may lack sufficient information and experience in the region to access care for a patient with a time-sensitive condition. Suboptimal decision-making may lead to multiple transfers before definitive stroke care is obtained, resulting in poor outcomes and additional health care system costs.</p><h3>Objectives</h3><p dir="ltr">We aimed to develop a tool to inform and assist NWO health care providers in determining the best transfer options for patients with stroke to provide the most efficient care access. We aimed to develop an app using a comprehensive geomapping navigation and estimation system based on machine learning algorithms. This app uses key stroke-related timelines including the last time the patient was known to be well, patient location, treatment options, and imaging availability at different health care facilities.</p><h3>Methods</h3><p dir="ltr">Using historical data (2008-2020), an accurate prediction model using machine learning methods was developed and incorporated into a mobile app. These data contained parameters regarding air (Ornge) and land medical transport (3 services), which were preprocessed and cleaned. For cases in which Ornge air services and land ambulance medical transport were both involved in a patient transport process, data were merged and time intervals of the transport journey were determined. The data were distributed for training (35%), testing (35%), and validation (30%) of the prediction model.</p><h3>Results</h3><p dir="ltr">In total, 70,623 records were collected in the data set from Ornge and land medical transport services to develop a prediction model. Various learning models were analyzed; all learning models perform better than the simple average of all points in predicting output variables. The decision tree model provided more accurate results than the other models. The decision tree model performed remarkably well, with the values from testing, validation, and the model within a close range. This model was used to develop the “NWO Navigate Stroke” system. The system provides accurate results and demonstrates that a mobile app can be a significant tool for health care providers navigating stroke care in NWO, potentially impacting patient care and outcomes.</p><h3>Conclusions</h3><p dir="ltr">The NWO Navigate Stroke system uses a data-driven, reliable, accurate prediction model while considering all variations and is simultaneously linked to all required acute stroke management pathways and tools. It was tested using historical data, and the next step will to involve usability testing with end users.</p><h2>Other Information</h2><p dir="ltr">Published in: JMIR Formative Research<br>License: <a href="https://creativecommons.org/licenses/by/4.0/deed.en" rel="noreferrer noopener" target="_blank">https://creativecommons.org/licenses/by/4.0/</a> <br>See article on publisher's website: <a href="https://dx.doi.org/10.2196/54009" target="_blank">https://dx.doi.org/10.2196/54009</a></p>2024-08-01T00:00:00ZTextJournal contributioninfo:eu-repo/semantics/publishedVersiontextcontribution to journal10.2196/54009https://figshare.com/articles/journal_contribution/An_App_for_Navigating_Patient_Transportation_and_Acute_Stroke_Care_in_Northwestern_Ontario_Using_Machine_Learning_Retrospective_Study/29605166CC BY 4.0info:eu-repo/semantics/openAccessoai:figshare.com:article/296051662024-08-01T00:00:00Z |
| spellingShingle | An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study Ayman Hassan (14426412) Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Neurosciences Health sciences Health services and systems Public health Information and computing sciences Artificial intelligence Data management and data science Distributed computing and systems software Human-centred computing Information systems Machine learning Stroke care Acute stroke Northwestern Ontario Predictions Models Geography Machine learning Stroke Cardiovascular Brain Neuroscience TIA Transient ischemic attack Coordinated care Navigation Navigating mHealth Mobile health App Apps Applications Geomapping Geographical Location Spatial Predict Predictive |
| status_str | publishedVersion |
| title | An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study |
| title_full | An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study |
| title_fullStr | An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study |
| title_full_unstemmed | An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study |
| title_short | An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study |
| title_sort | An App for Navigating Patient Transportation and Acute Stroke Care in Northwestern Ontario Using Machine Learning: Retrospective Study |
| topic | Biomedical and clinical sciences Cardiovascular medicine and haematology Clinical sciences Neurosciences Health sciences Health services and systems Public health Information and computing sciences Artificial intelligence Data management and data science Distributed computing and systems software Human-centred computing Information systems Machine learning Stroke care Acute stroke Northwestern Ontario Predictions Models Geography Machine learning Stroke Cardiovascular Brain Neuroscience TIA Transient ischemic attack Coordinated care Navigation Navigating mHealth Mobile health App Apps Applications Geomapping Geographical Location Spatial Predict Predictive |