arXiv subjects

Jagpreet Chhatwal

Publications and source records attributed to Jagpreet Chhatwal.

5 recordsLinked to original sources

arXiv2025

ELEVATE-GenAI: Reporting Guidelines for the Use of Large Language Models in Health Economics and Outcomes Research: an ISPOR Working Group on Generative AI Report

Introduction: Generative artificial intelligence (AI), particularly large language models (LLMs), holds significant promise for Health Economics and Outcomes Research (HEOR). However, standardized reporting guidance for LLM-assisted research is lacking. This article introduces the ELEVATE GenAI framework and checklist - reporting guidelines specifically designed for HEOR studies involving LLMs. Methods: The framework was developed through a targeted literature review of existing reporting guidelines, AI evaluation frameworks, and expert input from the ISPOR Working Group on Generative AI. It comprises ten domains, including model characteristics, accuracy, reproducibility, and fairness and bias. The accompanying checklist translates the framework into actionable reporting items. To illustrate its use, the framework was applied to two published HEOR studies: one focused on systematic literature review tasks and the other on economic modeling. Results: The ELEVATE GenAI framework offers a comprehensive structure for reporting LLM-assisted HEOR research, while the checklist facilitates practical implementation. Its application to the two case studies demonstrates its relevance and usability across different HEOR contexts. Limitations: Although the framework provides robust reporting guidance, further empirical testing is needed to assess its validity, completeness, usability, as well as its generalizability across diverse HEOR use cases. Conclusion: The ELEVATE GenAI framework and checklist address a critical gap by offering structured guidance for transparent, accurate, and reproducible reporting of LLM-assisted HEOR research. Future work will focus on extensive testing and validation to support broader adoption and refinement.

cs.CY↗

arXiv2025

Generative AI in Health Economics and Outcomes Research: A Taxonomy of Key Definitions and Emerging Applications, an ISPOR Working Group Report

Objective: This article offers a taxonomy of generative artificial intelligence (AI) for health economics and outcomes research (HEOR), explores its emerging applications, and outlines methods to enhance the accuracy and reliability of AI-generated outputs. Methods: The review defines foundational generative AI concepts and highlights current HEOR applications, including systematic literature reviews, health economic modeling, real-world evidence generation, and dossier development. Approaches such as prompt engineering (zero-shot, few-shot, chain-of-thought, persona pattern prompting), retrieval-augmented generation, model fine-tuning, and the use of domain-specific models are introduced to improve AI accuracy and reliability. Results: Generative AI shows significant potential in HEOR, enhancing efficiency, productivity, and offering novel solutions to complex challenges. Foundation models are promising in automating complex tasks, though challenges remain in scientific reliability, bias, interpretability, and workflow integration. The article discusses strategies to improve the accuracy of these AI tools. Conclusion: Generative AI could transform HEOR by increasing efficiency and accuracy across various applications. However, its full potential can only be realized by building HEOR expertise and addressing the limitations of current AI technologies. As AI evolves, ongoing research and innovation will shape its future role in the field.

cs.LG↗

arXiv2024

Generative AI for Health Technology Assessment: Opportunities, Challenges, and Policy Considerations

This review introduces the transformative potential of generative Artificial Intelligence (AI) and foundation models, including large language models (LLMs), for health technology assessment (HTA). We explore their applications in four critical areas, evidence synthesis, evidence generation, clinical trials and economic modeling: (1) Evidence synthesis: Generative AI has the potential to assist in automating literature reviews and meta-analyses by proposing search terms, screening abstracts, and extracting data with notable accuracy; (2) Evidence generation: These models can potentially facilitate automating the process and analyze the increasingly available large collections of real-world data (RWD), including unstructured clinical notes and imaging, enhancing the speed and quality of real-world evidence (RWE) generation; (3) Clinical trials: Generative AI can be used to optimize trial design, improve patient matching, and manage trial data more efficiently; and (4) Economic modeling: Generative AI can also aid in the development of health economic models, from conceptualization to validation, thus streamlining the overall HTA process. Despite their promise, these technologies, while rapidly improving, are still nascent and continued careful evaluation in their applications to HTA is required. To ensure their responsible use and implementation, both developers and users of research incorporating these tools, should familiarize themselves with their current limitations, including the issues related to scientific validity, risk of bias, and consider equity and ethical implications. We also surveyed the current policy landscape and provide suggestions for HTA agencies on responsibly integrating generative AI into their workflows, emphasizing the importance of human oversight and the fast-evolving nature of these tools.

cs.LG↗

arXiv2023

Small Area Estimation of Case Growths for Timely COVID-19 Outbreak Detection

The COVID-19 pandemic has exerted a profound impact on the global economy and continues to exact a significant toll on human lives. The COVID-19 case growth rate stands as a key epidemiological parameter to estimate and monitor for effective detection and containment of the resurgence of outbreaks. A fundamental challenge in growth rate estimation and hence outbreak detection is balancing the accuracy-speed tradeoff, where accuracy typically degrades with shorter fitting windows. In this paper, we provide a transfer learning framework, which we call Transfer Learning Random Forest (TLRF), for an effective implementation of the random forests algorithm that balances this accuracy-speed tradeoff. Specifically, we develop an identification strategy that converts the growth rate estimation problem into a regression task, which enables effective transfer learning across space and time through random forests' adaptive weighting mechanism. As such, through adaptively choosing fitting window sizes based on relevant day-level and county-level features affecting the disease spread, TLRF can accurately estimate case growth rates for counties with small sample sizes. Out-of-sample prediction analysis shows that TLRF outperforms established growth rate estimation methods. Furthermore, we conducted a case study based on outbreak case data from the state of Colorado and showed that TLRF could improve timely detections of outbreaks up to 224% when compared to the decisions made by Colorado's Department of Health and Environment (CDPHE). To demonstrate practical implementation, we developed a publicly available outbreak detection tool that operated from September 2020 through March 2023, receiving substantial attention from policymakers across all 50 states.

stat.ML↗

arXiv2020

Estimating County-Level COVID-19 Exponential Growth Rates Using Generalized Random Forests

Rapid and accurate detection of community outbreaks is critical to address the threat of resurgent waves of COVID-19. A practical challenge in outbreak detection is balancing accuracy vs. speed. In particular, while estimation accuracy improves with longer fitting windows, speed degrades. This paper presents a machine learning framework to balance this tradeoff using generalized random forests (GRF), and applies it to detect county level COVID-19 outbreaks. This algorithm chooses an adaptive fitting window size for each county based on relevant features affecting the disease spread, such as changes in social distancing policies. Experiment results show that our method outperforms any non-adaptive window size choices in 7-day ahead COVID-19 outbreak case number predictions.

cs.LG↗