Abstract
Question/Introduction: e-ASPECTS detects areas of acute ischemic stroke on CT, using the validated ASPECTS score. As immediate access to the calculated e-ASPECTS score is crucial in acute strokes optimized software integration into the clinical pathway is vital, we evaluated different integration strategies. The aim was to identify the optimal integration of the e-ASPECTS software into our stroke pathway. Methods: A user survey was conducted amongst key healthcare professionals to determine preferred integration strategies. Several interegation options were identified, differing according to user needs. Whilst members of the research team favoured images to be sent from PACS to a local sever with processed images being returned to PACS, the acute stroke team preferred images to be automatically sent from CT to a hospital-based server. The results would be accessible via an ipad user interface and results also returned to PACS. The referring hospitals preferred a cloud-based server that would allow them to use the software without server installation in their respective hospitals. We evaluated all the above options. Results: All 3 integration solutions were feasible.
We found that a local server decreased time when compared to the cloud-based option. The research team found a time lag of up to 6 minutes when retrospectively transferring images from PACS. However, they found the option best suited their needs particularly as it allows the simultaneous upload of several hundred images in one go with the possibility of data being returned in an excel spreadsheet. The clinical team remained with their original preferred option, finding it provided the fastest solution with near instant access to post-processed images (<1min). The third option was feasible but not practical in our setting. Conclusion: The value of computer assisted diagnostics can be increased by optimized integration into the clinical pathway, thus increasing user friendliness, speed and potentially outcome.
We found that a local server decreased time when compared to the cloud-based option. The research team found a time lag of up to 6 minutes when retrospectively transferring images from PACS. However, they found the option best suited their needs particularly as it allows the simultaneous upload of several hundred images in one go with the possibility of data being returned in an excel spreadsheet. The clinical team remained with their original preferred option, finding it provided the fastest solution with near instant access to post-processed images (<1min). The third option was feasible but not practical in our setting. Conclusion: The value of computer assisted diagnostics can be increased by optimized integration into the clinical pathway, thus increasing user friendliness, speed and potentially outcome.
Original language | English |
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Article number | P148 |
Pages (from-to) | 82 |
Number of pages | 1 |
Journal | Cerebrovascular Diseases |
Volume | 43 |
Issue number | suppl 1 |
DOIs | |
Publication status | Published - Jul 2017 |
Event | 26th European Stroke Conference - Berlin, Germany Duration: 24 May 2017 → 26 May 2017 |