[Vision Paper] PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI (2411.02973v1)

Abstract: We present an outline of the first LLM based chatbot application in the context of patient-reported outcome measures (PROMs) for diabetic retinopathy. By utilizing the capabilities of current LLMs, we enable patients to provide feedback about their quality of life and treatment progress via an interactive application. The proposed framework offers significant advantages over the current approach, which encompasses only qualitative collection of survey data or a static survey with limited answer options. Using the PROBot LLM-PROM application, patients will be asked tailored questions about their individual challenges, and can give more detailed feedback on the progress of their treatment. Based on this input, we will use machine learning to infer conventional PROM scores, which can be used by clinicians to evaluate the treatment status. The goal of the application is to improve adherence to the healthcare system and treatments, and thus ultimately reduce cases of subsequent vision impairment. The approach needs to be further validated using a survey and a clinical study.

• The paper presents a novel chatbot framework that uses large language models to personalize patient-reported outcomes for diabetic retinopathy.
• It details a three-component system combining interpreters, conversational AI, and patient data storage to improve engagement and adherence.
• Preliminary tests suggest that AI-driven simulation can generate empathetic, context-aware interactions, offering potential for broader healthcare applications.

Enhancing Patient-Reported Outcome Measures Using Generative AI: An Overview of the PROBot Vision Paper

The paper "PRObot: Enhancing Patient-Reported Outcome Measures for Diabetic Retinopathy using Chatbots and Generative AI" explores the integration of LLMs into healthcare, specifically focusing on diabetic retinopathy (DR) management. The authors propose a novel framework leveraging LLMs to enhance Patient-Reported Outcome Measures (PROMs), addressing inherent issues in traditional PROMs' static and standardized format.

Motivations and Current Challenges

Patient-reported outcome measures, such as the NEI-VFQ-25, have been instrumental in capturing patient perspectives on their health and treatment outcomes. However, these PROMs are characteristically rigid and not individually tailored, resulting in low adherence and engagement levels among patients. This is particularly problematic in chronic conditions management, like diabetic retinopathy, where regular monitoring and patient engagement are critical.

The authors advocate for the replacement of static and disconnected interactions with a dynamic, interactive chatbot system, "PROBot," to better tailor the communication and reporting process to individual patients. This aims to improve engagement and treatment adherence by adapting conversations to the patient's context, thus providing a more patient-centric model of care.

Methodological Foundation and Implementation

The framework is built upon three primary components: the Interpreter, Chatbot, and Storage. The Interpreter utilizes encoder LLMs to analyze and understand patient responses, extracting meaningful embeddings from free-text inputs. It operates alongside a machine learning-based Rater designed to predict standardized PROM scores from conversational data. The Chatbot component leverages pre-trained LLMs, such as ChatGPT, using prompt engineering to generate contextual, empathetic dialogue. This setup is complemented by a Storage system housing historical patient data, which provides context to current interactions.

Methodologies include fine-tuning LLMs for generating dynamic and personalized survey questions, predicting outcomes, and validating these through simulation and real-world interactions. The paper outlines plans to perform initial qualitative testing using LLMs as patient simulators, which will later inform experimental designs for deploying PROBot in a clinical setting.

Promising Outcomes and Prospective Developments

The preliminary tests demonstrate the efficacy of using LLMs to simulate patient interactions, showcasing their ability to produce personalized responses and empathetic communication. The use of APIs, coupled with open-source models, supports developing a customizable AI-driven tool that seeks to improve the diagnostic and therapeutic processes of chronic disease management.

In terms of implications, should PROBot prove successful, this framework can be extended beyond diabetic retinopathy to other chronic conditions where patient engagement is similarly critical. Moreover, the potential for this model to generate a vast corpus of patient interaction data could significantly enhance the machine learning models' predictive accuracy over time.

Conclusion

The integration of LLMs into the PROBot framework represents a substantial shift towards interactive, patient-centered healthcare. This vision paper lays the groundwork for transforming PROMs from static assessments to dynamic, personalized, and real-time interactions, potentially revolutionizing patient engagement and adherence.

Future work includes comprehensive clinical validation and continued collaboration with healthcare providers to refine and implement the approach in practical, real-world settings. These steps will ascertain the framework's viability and effectiveness, contributing to more personalized and efficient healthcare delivery. The PROBot framework offers a promising avenue for leveraging AI in personalized medicine, emphasizing patient empowerment and engagement within chronic disease management contexts.