Transforming Discharge Summaries: How a Large Language Model Enhances Cardiovascular Care with Personalized Lifestyle Recommendations

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Transforming Discharge Summaries: How a Large Language Model Enhances Cardiovascular Care with Personalized Lifestyle Recommendations

To explore the effectiveness of large language models (LLMs) in improving hospital discharge summaries, we conducted a study focused on readability. Our results showed that LLMs could lower the reading level of these documents from college-level to around 10th grade. However, this still falls short of the recommended 6th-grade level for optimal patient understanding.

Interestingly, previous studies on radiology reports have achieved reading levels below 8th grade. This efficiency might stem from the more standardized and concise nature of those texts compared to complex discharge summaries. When content is intricate, simplifying it becomes more challenging, often risking the loss of crucial information.

In our study, experts praised the LLM-generated outputs for their clarity and comprehensibility, even if they sometimes included more words. Experts emphasized that simply translating medical jargon isn’t enough; providing clear explanations is key to helping patients grasp their health information better. LLMs excel in this area, as they can adapt to complex contexts, unlike traditional rule-based systems.

However, using LLMs isn’t without dangers. While most generated summaries were deemed clear and accurate, we noticed “hallucinations,” where the model produced incorrect information that appeared plausible. For instance, past diagnoses were sometimes mistakenly cited as current ones due to how the data was anonymized. Such errors could lead to serious consequences in patient care and increase anxiety for patients.

Another concern was instances of insensitive communication, which could lead to patient confusion. Clear, empathetic language is vital in discharge summaries to avoid misunderstanding and distress.

One major flaw we encountered was the omission of medication dosages in the summaries. This is critical information that patients need, and its absence can lead to dangerous situations if patients rely solely on these simplified texts. Interestingly, switching from a full-text prompt to a segmented approach improved the outputs significantly in terms of clarity and safety.

Overall, our findings align with previous research, suggesting that LLMs have promise in simplifying medical texts. The goal should always be to enhance understanding, not just reduce length.

When we also looked at the LLM’s ability to generate personalized lifestyle recommendations from discharge summaries, we discovered it produced numerous relevant and evidence-based suggestions. This could assist in preventative care without adding much burden to healthcare providers. However, these recommendations were often too generic to be truly effective. For instance, advising a patient to do cardiovascular exercise wouldn’t be suitable for someone with a foot injury.

While we observed these results using a small sample size of only 20 discharge summaries, limitations hinder broader application. For instance, the variable responses of LLMs can complicate consistency and reproducibility. Also, we didn’t compare our findings with other specialized models, which could yield different insights.

Future efforts should focus on larger and more diverse samples while directly involving patients to assess whether these changes improve understanding and health outcomes. Addressing biases in LLMs is also crucial to provide equitable care across different patient populations.

Additionally, we should consider the ethical concerns tied to using LLMs in a clinical setting, such as data privacy. To maintain patient confidentiality while harnessing these models’ power, exploring anonymization strategies is vital.

As of now, LLMs, including advanced models like GPT-4o, aren’t FDA-approved as medical devices. However, regulations are evolving, and there’s hope that with more research and technological advancements, LLMs could become a trusted tool in healthcare.

Open-source models are emerging as a promising alternative for regulatory approval. Compared to closed-source options, they provide better control over algorithms and data privacy, making them a strong choice for medical applications. While challenges remain, the refinement of these models could bridge performance gaps and enhance their utility in healthcare.

In summary, our study suggests that LLMs can improve the readability of patient discharge summaries and provide useful lifestyle recommendations. However, there are significant challenges regarding personalization, quality assurance, and regulatory compliance that must be addressed before these models can be widely adopted in clinical practice.

View related clinical research here.



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Cardiology,Health care,Medicine/Public Health,general