Integration of artificial intelligence (AI) applications within clinical workflows is an important step for leveraging developed AI algorithms. In this report, generalizable components for deploying AI systems into clinical practice are described that were implemented in a clinical pilot study using lymphoscintigraphy examinations as a prospective use case (July 1, 2019-October 31, 2020). Deployment of the AI algorithm consisted of seven software components, as follows: (a) image delivery, (b) quality control, (c) a results database, (d) results processing, (e) results presentation and delivery, (f) error correction, and (g) a dashboard for performance monitoring. A total of 14 users used the system (faculty radiologists and trainees) to assess the degree of satisfaction with the components and overall workflow. Analyses included the assessment of the number of examinations processed, error rates, and corrections. The AI system processed 1748 lymphoscintigraphy examinations. The system enabled radiologists to correct 146 AI results, generating real-time corrections to the radiology report. All AI results and corrections were successfully stored in a database for downstream use by the various integration components. A dashboard allowed monitoring of the AI system performance in real time. All 14 survey respondents "somewhat agreed" or "strongly agreed" that the AI system was well integrated into the clinical workflow. In all, a framework of processes and components for integrating AI algorithms into clinical workflows was developed. The implementation described could be helpful for assessing and monitoring AI performance in clinical practice.
OBJECTIVE.
Long indwelling times for inferior vena cava (IVC) filters that are used to prevent venous thromboembolism can result in complications. To improve care for patients receiving retrievable IVC filters, we developed and evaluated an informatics-based initiative to facilitate patient tracking, clinical decision-making, and care coordination.
MATERIALS AND METHODS.
A semiautomated filter-tracking application was custom-built to query our radiology information system to extract and transfer key data elements related to IVC filter insertion procedures into a database. A web-based interface displayed key information and facilitated communication between the interventional radiology clinical team and referring physicians. A set of filter management options was provided depending on each patient’s clinical condition. The system was launched in April 2016. Using retrospective observational cohort methods, we compared filter retrieval rates during a test period from July through December 2016 with a control period of the same 6 months in 2015.
RESULTS.
System development required approximately 100 hours of development time. Two hundred ninety-three IVC filter placements and 83 filter retrievals were tracked during the study periods. The overall filter retrieval rate was 23% in the control period and 34% in the test period. Mean times from filter placement to retrieval in the control and test periods were not significantly different (88.9 and 102.7 days, respectively; p = 0.32).
CONCLUSION.
A semiautomated approach to tracking patients with IVC filters can facilitate care coordination and clinical decision-making for a device with known potential complications. Similar applications designed to improve provider communication and documentation of filter management plans, including appropriateness for retrieval, can be replicated.
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