Dr. John Svirbely's blog post - Orchestrating Generative AI in Business Process Models
Dr. John Svirbely, MD

Orchestrating Generative AI in Business Process Models

By Dr. John Svirbely, MD

Read Time: 2 Minutes

Generative AI is spreading fast and constantly becoming more powerful. Its uses and roles in healthcare are still uncertain. Although it will be disruptive, it is unclear what it will change or what will be replaced as the technology evolves.

The use of Generative AI poses several challenges, at least for now. In some respects, it behaves like a black box. It may be unable to give the sources for what it produces, so it is hard to judge the reliability of its sources. It can be hard to validate depending on how it is used. These factors may make doctors, patients, and regulators nervous about its use in a sensitive area like healthcare. If a claim of malpractice is made involving it, then it may be hard to defend its mysterious behavior.

Generative AI and Business Process Models

A business process model can access Generative AI simply by adding a connector to a task, which is done by a simple drag and drop. Because it is now part of a process, you can control when and how it is called.

Since there may be several possible paths through the model, you can have different calls that are appropriate for each path. Orchestrating the output provides an opportunity to give an individualized solution for a specific situation. Orchestration of Generative AI can make it less of a black box.

Since the calls to Generative AI can be tightly constrained and since you know exactly where it is being used and what the inputs are, the appropriateness of its explanation can be judged in context. This can make validation a bit less daunting.

Illustrative Example

A common problem in healthcare is the need to communicate health information to patients. Not only may the patient and family not understand what the provider is saying, but also the provider may misunderstand the patient. The need to communicate better has created a need for access to human translators around the clock. This raises other problems, as the translator may not understand the nuances of medical terms. It can also be quite expensive since you need to have multiple translators on call.

In Figure 1 there is a portion of a BPMN model for the diagnosis of anemia. A DMN decision model first determines whether a patient has anemia, and, if so, its severity. It may be desirable to inform the patient quickly and easily about these findings. The problem of translation can be approached by taking the outputs of the decision and sending them as inputs to Generative AI (in this case OpenAI, indicated by the icon in the top left corner), along with the patient’s preferred language and education level. The Generative AI then takes these inputs and instructions and generates a letter tailored to the patient.

Figure 1

Generating narrative text is a strength for Generative AI. If known inputs and appropriate constraints are placed on it, then it can reproducibly generate a letter to inform a patient of the diagnosis in language that the patient can understand. Performance can be validated by periodic review of various outputs by a suitably qualified person. This can simply but elegantly solve problems in a cost-effective manner.

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