Orchestrating AI in clinical settings with jBPM

Originally published at: Orchestrating AI in clinical settings with jBPM - OpenSource.net

jBPM orchestrates external AI, specifically for integrating AI-powered features into clinical settings like stroke prediction with OpenEMR.

In part one and part two of this series, we’ve seen how jBPM can be used as a platform for orchestrating external AI-centric environments, such as Python, used for designing and running AI solutions. This article brings it all together to show how effective jBPM can be in integrating AI-powered features into a clinical setting.

jBPM, OpenEMR and Python for stroke prediction

A comprehensive illustrative example of jBPM orchestrating Python is our solution for training AI models and estimating stroke risk in a set of patients. In a hospital or clinical setting, physicians use an EMR (Electronic Medical Record) system like open-source OpenEMR (or any other software allowing customization of user screens). Stroke prediction requests may originate from various sections of the EMR, typically within a patient’s file:

The physician selects the stroke prediction AI solution and submits a prediction request to jBPM, where the AI logic is deployed:

jBPM processes the request, orchestrating the AI logic and Python computations, and returns the estimated stroke risk to OpenEMR:

The stroke risk is initially calculated in jBPM before being returned to OpenEMR for the physician:

This prediction workflow, when visualized end-to-end, appears as follows:

Meanwhile, an analyst monitoring the AI models’ performance can retrain and publish updated models:

Similar to the physician’s workflow, the analyst selects the AI solution and submits a train/validate/ test request to jBPM:

jBPM logs and executes the training process:

Results are computed and returned to OpenEMR:

Load testing confirms that jBPM efficiently handles simultaneous user requests for both prediction and model training without bottlenecks or data leaks:

The following video shows how all the above works live. We would be happy to support the interested reader with getting it up and running in their IT landscape: reach out to us for guidance and advice.

For more: C-NLTX/Open-Source

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