Problem Background
Effectively triaging patients is a key factor in improving access to world-class cancer care and reducing the administrative burden for staff and clinicians. Patient service representatives (PSR) staff, must review detailed, complex, and lengthy triage instructions. Reconciling these important, but often times difficult to understand triage instructions, can result in mismatched routings of patients, leaving patients and their families frustrated with delays and physicians questioning why patients were routed to them. When PSR staff have questions on a triage decision, this is often escalated to a nurse navigator and/or a clinician, leading to additional delays.
Proposed Solution
GuideMyTriage (GMT) addresses this issue by providing PSR staff with a tool that they are able to communicate with directly allowing them to ask questions about the proper way to triage a patient. Interactions are performed in a human centric approach and allows quick guidance from carefully and continuously curated decision trees for triaging. The results – patients are quickly scheduled for the right clinic for the right time without the need to escalate to a nurse manager or physician.
Healthcare Innovation CIC Takes on Initial Prototyping
The Healthcare Innovation CIC worked closely with a leading medical center in the US to develop an impactful prototype highlighting how this triage process could be reimagined.
We worked closely to understand the goals and requirements of the proposed effort and framed three potential use cases to tackle:
- Intake & Triage – When a patient is referred, a Patient Service Representative (PSR) is responsible for triaging the patient to the appropriate clinical department to schedule an appointment. They rely on complex guidelines that can be confusing for the PSR to follow. The PSR might schedule the patient with the incorrect clinical department, taking up a precious appointment slots and creating delays for the patient and provider. The use case is for the PSR to chat with a chatbot after receiving and reviewing the referral, to ensure the patient is triaged to the correct clinical department to schedule an appointment within 24 hours, and for the patient to be seen within 7 business days.
- Ordering Correct Studies & Tests – A chatbot that can provide guidance on the right set of studies/tests (labs, imaging, etc.) for Advanced Practice Providers (APPs) to order. This chatbot would also help APP schedule the correct follow-up appointments and request appropriate referrals.
- After-Hours Operator – Outside of regular business hours (M-F 9AM-5PM), a chatbot could provide the operator with correct information to relay to the patient. The current problem is that operators might connect patients with clinical staff who are already over-burdened by administrative work. These calls could have been completed by administrative staff instead of a clinician (e.g., notifying patients about upcoming appointment details, lab results, etc.) to optimize resources to maximize health outcomes.
Our innovation center decided to take on prototyping for use case #1 and utilized generative AI technologies in correspondence to a knowledge domain of procedure documents to prove out the feasibility of the concept.
We evaluated several Large Language Models (LLM’s) with various prompts including prompts that displayed reasoning of choices. The following demonstrates a correct routing choice the bot was able to achieve based on procedure documentation within the bot’s knowledge base.
Additionally, we validated that the bot would prompt the user for more information if it was unclear of where to route the patient.
Call to Action
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Artifacts
| Deliverable | Description | Link |
|---|---|---|
| PRFAQ | Fictional press release for the GuideMyTriage | https://github.com/UC-CIC/ccc-docs/blob/main/ccc.prfaq.pdf |
| Use case summary & architecture | Architecture diagram & use case | https://github.com/UC-CIC/ccc-docs/blob/main/ccc.usecase.pdf |
| Prototype (Technical) | Technical prototype leveraging AWS Gen AI LLM Chatbot Accelerator | https://github.com/UC-CIC/aws-genai-llm-chatbot-ccc/tree/ccc-mod |