Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway
Objective To evaluate the application of clinical pathway simulation in machine learning, using clinical audit data, in order to identify key drivers for improving use and speed of thrombolysis at individual hospitals.Design Computer simulation modelling and machine learning.Setting Seven acute stro...
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| Format: | Article |
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BMJ Publishing Group
2019-09-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/9/9/e028296.full |
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| author | Andrew Salmon Thomas Monks Michael Allen Kerry Pearn Benjamin D Bray Richard Everson Martin James Ken Stein |
| author_facet | Andrew Salmon Thomas Monks Michael Allen Kerry Pearn Benjamin D Bray Richard Everson Martin James Ken Stein |
| author_sort | Andrew Salmon |
| collection | DOAJ |
| description | Objective To evaluate the application of clinical pathway simulation in machine learning, using clinical audit data, in order to identify key drivers for improving use and speed of thrombolysis at individual hospitals.Design Computer simulation modelling and machine learning.Setting Seven acute stroke units.Participants Anonymised clinical audit data for 7864 patients.Results Three factors were pivotal in governing thrombolysis use: (1) the proportion of patients with a known stroke onset time (range 44%–73%), (2) pathway speed (for patients arriving within 4 hours of onset: per-hospital median arrival-to-scan ranged from 11 to 56 min; median scan-to-thrombolysis ranged from 21 to 44 min) and (3) predisposition to use thrombolysis (thrombolysis use ranged from 31% to 52% for patients with stroke scanned with 30 min left to administer thrombolysis). A pathway simulation model could predict the potential benefit of improving individual stages of the clinical pathway speed, whereas a machine learning model could predict the benefit of ‘exporting’ clinical decision making from one hospital to another, while allowing for differences in patient population between hospitals. By applying pathway simulation and machine learning together, we found a realistic ceiling of 15%–25% use of thrombolysis across different hospitals and, in the seven hospitals studied, a realistic opportunity to double the number of patients with no significant disability that may be attributed to thrombolysis.Conclusions National clinical audit may be enhanced by a combination of pathway simulation and machine learning, which best allows for an understanding of key levers for improvement in hyperacute stroke pathways, allowing for differences between local patient populations. These models, based on standard clinical audit data, may be applied at scale while providing results at individual hospital level. The models facilitate understanding of variation and levers for improvement in stroke pathways, and help set realistic targets tailored to local populations. |
| format | Article |
| id | doaj-art-1526cc092f3c47a3b341c6cf329cf671 |
| institution | DOAJ |
| issn | 2044-6055 |
| language | English |
| publishDate | 2019-09-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-1526cc092f3c47a3b341c6cf329cf6712025-08-20T02:48:42ZengBMJ Publishing GroupBMJ Open2044-60552019-09-019910.1136/bmjopen-2018-028296Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathwayAndrew Salmon0Thomas Monks1Michael Allen2Kerry Pearn3Benjamin D Bray4Richard Everson5Martin James6Ken Stein71 Medical School, University of Exeter, Exeter, UK2 University of Exeter Medical School, University of Exeter, Exeter, Devon, UK1 Medical School, University of Exeter, Exeter, UK1 Medical School, University of Exeter, Exeter, UKDepartment of Population Health Sciences, King`s College London, London, UK4 Computer Sciences, University of Exeter, Exeter, UK5 Stroke Consultant, Royal Devon & Exeter NHS Trust, Exeter, UKprofessorObjective To evaluate the application of clinical pathway simulation in machine learning, using clinical audit data, in order to identify key drivers for improving use and speed of thrombolysis at individual hospitals.Design Computer simulation modelling and machine learning.Setting Seven acute stroke units.Participants Anonymised clinical audit data for 7864 patients.Results Three factors were pivotal in governing thrombolysis use: (1) the proportion of patients with a known stroke onset time (range 44%–73%), (2) pathway speed (for patients arriving within 4 hours of onset: per-hospital median arrival-to-scan ranged from 11 to 56 min; median scan-to-thrombolysis ranged from 21 to 44 min) and (3) predisposition to use thrombolysis (thrombolysis use ranged from 31% to 52% for patients with stroke scanned with 30 min left to administer thrombolysis). A pathway simulation model could predict the potential benefit of improving individual stages of the clinical pathway speed, whereas a machine learning model could predict the benefit of ‘exporting’ clinical decision making from one hospital to another, while allowing for differences in patient population between hospitals. By applying pathway simulation and machine learning together, we found a realistic ceiling of 15%–25% use of thrombolysis across different hospitals and, in the seven hospitals studied, a realistic opportunity to double the number of patients with no significant disability that may be attributed to thrombolysis.Conclusions National clinical audit may be enhanced by a combination of pathway simulation and machine learning, which best allows for an understanding of key levers for improvement in hyperacute stroke pathways, allowing for differences between local patient populations. These models, based on standard clinical audit data, may be applied at scale while providing results at individual hospital level. The models facilitate understanding of variation and levers for improvement in stroke pathways, and help set realistic targets tailored to local populations.https://bmjopen.bmj.com/content/9/9/e028296.full |
| spellingShingle | Andrew Salmon Thomas Monks Michael Allen Kerry Pearn Benjamin D Bray Richard Everson Martin James Ken Stein Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway BMJ Open |
| title | Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway |
| title_full | Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway |
| title_fullStr | Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway |
| title_full_unstemmed | Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway |
| title_short | Can clinical audits be enhanced by pathway simulation and machine learning? An example from the acute stroke pathway |
| title_sort | can clinical audits be enhanced by pathway simulation and machine learning an example from the acute stroke pathway |
| url | https://bmjopen.bmj.com/content/9/9/e028296.full |
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