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To identify the barriers and facilitators in the implementation of fertility preservation (FP) shared decision-making (SDM) in oncology care.
Qualitative descriptive study.
Qualitative interviews with 16 female patients with cancer and seven healthcare providers were conducted between July 2022 and April 2024. Data were analyzed using directed content analysis, guided by the implementation science framework.
We identified 22 categories comprising 38 codes as barriers to SDM implementation and 17 categories comprising 26 codes as facilitators. Findings revealed that, at the innovation level, accessibility, feasibility, interdisciplinary collaboration, and quality improvement efforts were decisive in the implementation of FP SDM. At the individual level, healthcare providers' awareness and attitudes towards FP and SDM, as well as patients' knowledge, attitudes, and capabilities in FP SDM, were crucial factors in the implementation of FP SDM. In social, economic, and organizational contexts, support from significant others, social awareness about FP, multidisciplinary care, financial assistance, and educational resources were determinants in implementing FP SDM.
Implementing FP SDM among female patients with cancer necessitates a strategic approach that considers barriers and facilitators. Educating and promoting FP SDM among the public and healthcare providers, combined with incentivizing policies, can enhance individual knowledge and awareness while achieving systemic improvements, facilitating its successful implementation.
This study provides insights into barriers and facilitators and proposes strategic approaches to enhancing FP SDM implementation, contributing to improved quality of life for cancer survivors and advancements in clinical practice.
With ambient listening systems increasingly adopted in healthcare, analyzing clinician-patient conversations has become essential. The Omaha System is a standardized terminology for documenting patient care, classifying health problems into four domains across 42 problems and 377 signs/symptoms. Manually identifying and mapping these problems is time-consuming and labor-intensive. This study aims to automate health problem identification from clinician-patient conversations using large language models (LLMs) with retrieval-augmented generation (RAG).
Using the Omaha System framework, we analyzed 5118 utterances from 22 clinician-patient encounters in home healthcare. RAG-enhanced LLMs detected health problems and mapped them to Omaha System terminology. We evaluated different model configurations, including embedding models, context window sizes, parameter settings (top k, top p), and prompting strategies (zero-shot, few-shot, and chain-of-thought). Three LLMs—Llama 3.1-8B-Instruct, GPT-4o-mini, and GPT-o3-mini—were compared using precision, recall, and F1-score against expert annotations.
The optimal configuration used a 1-utterance context window, top k = 15, top p = 0.6, and few-shot learning with chain-of-thought prompting. GPT-4o-mini achieved the highest F1-score (0.90) for both problem and sign/symptom identification, followed by GPT-o3-mini (0.83/0.82), while Llama 3.1-8B-Instruct performed worst (0.73/0.72).
Using the Omaha System, LLMs with RAG effectively automate health problem identification in clinical conversations. This approach can enhance documentation completeness, reduce documentation burden, and potentially improve patient outcomes through more comprehensive problem identification, translating into tangible improvements in clinical efficiency and care delivery.
Automating health problem identification from clinical conversations can improve documentation accuracy, reduce burden, and ensure alignment with standardized frameworks like the Omaha System, enhancing care quality and continuity in home healthcare.
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