Our Mar-2019 Challenge “Organ Transplants” continues to genera an interest among DM practitioners. Jack Jansonius just submitted a new solution based on the integrated use of decision tables and SQL. We wonder if somebody tries to produce a working solution for this challenge using a Generative AI tool (see below what ChatGPT has offered). As always, our challenges do not have expiration dates, and more solutions to old challenges are always welcome.

Here is what ChatGPT recommends based on the problem description:

The challenge you’ve described involves designing a decision system for allocating donated organs to recipients based on their responses to offers of specific organs. The decision system needs to take into account various factors, including the responses of candidates (whether they accept or decline the offered organs), the priority lists, and the potential combinations of organ offers. To create a decision model for this challenge, you can follow these steps:

1. Define the State:
   • Identify and define the current state of the system. This includes information about which organs have been offered, accepted, declined, and which candidates are next in line to receive offers.
2. Decision Logic:
   • Develop a decision logic that determines the next steps based on the responses received. This logic should consider whether a candidate accepts, partially accepts, or declines the offered organs.
3. Candidate Lists:
   • Determine the ordering of candidate lists for heart and lung transplants, and create a mechanism for tracking which candidates are next in line to receive offers.
4. Organ Combinations:
   • Implement logic for handling heart-lung blocks and combinations. This involves deciding whether to offer both organs together or separately based on responses and the current state of the system.
5. Time Sensitivity:
   • Consider the time sensitivity of offers, and ensure that the system can manage responses within a specified time frame, especially when dealing with donated organs that have limited viability.
6. Fall-Back Policy:
   • Implement the fall-back policy for organs that have been declined by all candidates. This may involve determining alternative courses of action or prioritizing other candidates or centers.
7. Recurrent Activation:
   • Outline how the system can be activated multiple times before all organs from a single donor are accepted. You may need to create a mechanism for initiating the allocation process and monitoring its progress over time.
8. Handling State:
   • Clearly specify how the current state of the system is held, accessed, and updated. You can use data structures or databases to store this information.
9. Visualization:
   • Consider using visual representations, such as BPMN (Business Process Model and Notation) or CMMN (Case Management Model and Notation) diagrams, to illustrate how the system operates and how decisions are made at each step.
10. Testing and Validation:
    • Test the decision system with different scenarios and data to ensure it behaves as expected. Validate its ability to make appropriate decisions and handle the allocation process effectively.
11. Documentation:
    • Provide comprehensive documentation that explains the decision system’s design, logic, and operation, as well as how it can adapt to changing circumstances.

The challenge involves both stateful and stateless decision-making, so you’ll need to demonstrate how the state is managed and maintained throughout the process. Additionally, the design should be flexible enough to accommodate real-world variations and complexities in the organ allocation process.