The Role Of LLMs For Improving Patient-Centered Care

The Role Of LLMs For Improving Patient-Centered Care

With the emergence of AI and large language models (LLMs), technology can help to truly put patients at the center of Healthcare. Piotr is the CEO of Infermedica, a leading AI Health company dedicated to improving preliminary symptom analysis and digital triage. In the Healthcare industry, we work for patients, so it isn’t just a buzzword when we talk about “patient-centered” care.

Care should always have been centered around the patient, but a lack of resources and workforce has made this ideal a struggle. Now, with the emergence of AI and large language models (LLMs), technology can help to truly put patients at the center of Healthcare. In this article, I’ll outline how LLMs can enhance the patient experience before addressing concerns about limitations and privacy.

LLMs In The Service Of Care

Here are a few ways that LLMs can assist the Healthcare industry in centering care around patients’ needs:

The emergence of LLMs opens up a whole new world of possibilities about what particulars of Healthcare patients can access, and how they can access it. With 88% of U.S. adults lacking sufficient Healthcare literacy to navigate Healthcare systems, LLMs can assist in areas of triage to guide patients to the right level of care at the right time.

They can also be used to facilitate and simplify materials related to medical conditions, while speech-to-text (STT) and text-to-speech (TTS) Features allow LLMs to hear us and talk back—a mode of communication that is so valuable for people with certain disabilities.

Moreover, the ability of LLMs to provide fast and accurate language translations can also improve accessibility. It’s been a long-term goal to stop treating patients as a number and start giving truly personalized care. But, until now, this has simply not been feasible due to financial constraints, physician shortages, overburdened systems and many Other factors.

With the emergence of LLMs, personalized Healthcare is more within reach. LLMs can process and analyze vast amounts of patient data, such as genetic makeup, Lifestyle, medical history, current medications and much more. Imagine if, for each patient, all of these factors were taken into account every time.

LLMs can flag potential risks and suggest checkups or preventative care. They can also analyze data from patient demographics to benefit the wider community. They can help in the creation of tailored treatment plans for chronic conditions, which could then be approved by a medical professional. For example, a recent paper on hemodialysis highlights the effective use of generative AI in addressing the challenges nephrologists face in creating personalized patient treatment plans.

Patients who are more engaged with their Healthcare provider and decisions about their Health tend to have better Healthcare outcomes. This is because they often have higher engagement with preventative services, as well as better adherence to treatment processes. Improving access to medical care and tailoring that care to specific needs are two crucial factors in keeping patients engaged and empowering them to be more involved in decisions that affect their Health.

On top of that, simple procedures that are now either missing or time-consuming for providers can be automated (yet tailored) by LLMs. For example, appointment scheduling, reminders and follow-up communication can all be taken on by LLMs, not only removing the burden from providers but also assisting in the tailoring of the messages and communication that keeps the patient at the center.

Maturity And Limitations Of LLMs

While there are various positive aspects, it’s vital to acknowledge the limitations of LLMs in their present state and to implement additional safeguards that mitigate the risks associated with relying too heavily on AI. LLMs create their responses based on vast quantities of free text, so there is potential for bias in their output. For example, if certain demographics are underrepresented or there’s a preference towards particular treatments in the data, the LLM draws information from this, resulting in inaccuracies in providing the best medical responses.

Furthermore, another concern is hallucinations, which are “outputs from an LLM that are contextually implausible, inconsistent with the real world and unfaithful to the input,” according to a recent paper. Hallucinations can have serious consequences in Healthcare if they provide an inaccurate diagnosis or recommend the wrong treatment plan. To address these issues, rigorous testing, validation, and involving medical professionals in the development and supervision of LLMs is essential.

Recognizing and addressing concerns related to data privacy and Security is a must for all healthtech companies. To achieve this, developers need to be transparent about their use of such technologies and how they function—and share the knowledge of potential risks openly.

For example, some studies suggest that due to LLMs relying on “memorizing” vast quantities of data, there is a possibility that they could memorize personal information—creating the risk that this private data could then be recycled back into the training data and made public. Developers must now consider options to combat such risks and maintain compliance with regulators, such as HIPAA or GDPR. Preventative measures also need to be taken to ensure that data is collected, stored and used correctly and with explicit consent.

In addition, regular scrutiny and tests must be carried out to ensure the highest level of data privacy is being maintained, with strong encryption methods being vital to protect against external attacks.

LLMs—Ready For Health Care?

It’s exciting to visualize the improved patient experience that LLMs can offer. When applied with caution and integrity that protects patients from the current limitations, LLMs will transform patient care as we know it via personalization, opening up access and helping patients to become more engaged with their Health.

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Source: forbes