Abstract
In the recent past, teleconsultation has emerged as one of
the main ways through which the patient and the healthcare provider
communicate. Nevertheless, there is a challenge in telemedicine since the
doctor and the patients hardly get to communicate including when medical terms
are used commonly. Such situations create misconceptions regarding the patient,
dissatisfaction and compromise the general wellbeing of the patient. The best
solution to this problem today, which is common among Generative AI, especially
Large Language Models (LLMs), is to provide more clarity and translation of
medical terms to patients, which will increase overall comprehension and
satisfaction. It will be possible due to the relatively recent LLMs, including,
for instance, OpenAI’s GPT models, which possess the ability to analyse, as
well as simplify, the meanings of medical information, so that patients with
different levels of education or literacy, could easily grasp them.
The communication problem can thus be solved by having LLMs
to be the middle link between the health givers and patients to facilitate the
realization of the objective set during consultation. These models can
translate, in real time, clinical language into language comprehensible to
patients, by analysing and distilling clinical language into easily-consumable
text. This is particularly relevant in telemedicine consultations, because
without the physical interaction with the provider, patient may struggle to has
questions or express confusion, discomfort or uncertainty. LLMs can also be
used as a bot to answer back patient enquiries to avoid mishap and confusion
with the wrong information but provide an accurate outcome to satisfy the
patient need.
Moreover, LLMs can be incorporated into applications of
telemedicine to aid in producing less complex material such as discharge
information, medication information and follow-up care plans, enhance patient
understanding and adherence to recommendations respectively. The introduction
of LLMs to this discussion and their application in improving the profession
AIDS patient interaction also improves the overall the general health of an
individual as a result of informed decision making and improved compliance with
health care plans. With the increasing uptake of telemedicine, value of
technology-aided communication models will be critical in the process of
enhancing the access, quality and patient satisfaction in remote care delivery.
Keywords: Generative
AI, Large language models, Telemedicine, Patient-provider communication,
Medical jargon, Healthcare satisfaction
1. Introduction
Online consultations have become an essential part of the
contemporary medical world primarily due to the COVID-19 pandemic that forced
the acceleration of the telemedicine trend. As a result, one of the tasks that
remain more pressing as healthcare services move online is the nature of
‘voice’ patients and their healthcare providers use to communicate. Apparently,
in a telemedicine consultation, misunderstanding is more likely to occur
because the physician does not see his or her patient, which is more likely
going to happen with the use of complicated medical terms or acronyms. This is
rather troubling, as many patients struggle with comprehending a large portion
of the medical terminology and concepts that doctors and other healthcare
workers use; when a patient has difficulty understanding what their doctor or
nurse is saying, that patient is likely to become stressed and the likelihood
of them receiving inadequate care is greatly increased.
Hearing has been referred to being central to the overall
practice of care given that it determines patients’ understanding and
satisfaction as well as the ultimate outcomes of a health care process. As much
as this is true, according to the Institute of Medicine, effective
communication must always be promoted as a way of enhancing safety of patients
and subsequent health outcomes. However, due to complexity it becomes difficult
for many patients to comprehend different medical terms which in turn hinder them
from making right decisions concerning their wellbeing. Studies reveal that
there are dramatic differences in the way different individuals or groups
comprehend and process health information and that those with lower health
literacy are sicker, hospitalized more often and spend more on healthcare.
Of all the generative AI models, the current state of Large
Language Models (LLMs) holds the potential to solve this communication issue
effectively. The LLMs, which are based on tons of textual data, are ready to
produce the responses that seem to come from a flesh-and-blood doctor and
interpret the complicated medical jargon for patients. Combined with medical
records and clinical guidelines or treatment plans, LLMs are ready and able to
assist healthcare delivery professionals to provide real-time ‘translation’ of
medical narratives into lay language so that they can better explain to
patients. Moreover, these models can produce concise summaries of consultations
that patient can easily understand when it comes to their diagnosis, what
treatment is suitable for them and the recommended care.
The possibility of LLMs in improving patient-provider
communication in telemedicine is great. The use of these models can prompt the
important processes wherein complicated information may be converted in a way
that does not mislead the patient but is also adapted to the patient’s
comprehension. While telemedicine is gaining popularity, the integration of
LLMs into these consulting processes can help increase patient interest and
satisfaction and, therefore, the effectiveness of the consultations. By thus removing
the disconnect between the healthcare providers and the users, the advanced AI
solutions can ease the communication barrier that they continue to experience.
2. Literature Review
Interpersonal communication continues to be a critical
component in the delivery of quality health services particularly with the
emerging use of Telemedicine. However, the conversation continues to be laden
with medical terms and this poses a lot of the challenge in relationships
between patients and care providers. Studies have long pointed out the
discrepancy between the medical jargon employed by doctors and the
comprehensibility thereof by the patients. Research done on health literacy
demonstrated that more than 80% of the adult population in the United States
needs help to understand health information and that results in high risks and
worsened health care for the patients who comprise this population1. These discoveries are especially worrying
considering the situation in telemedicine where at least clients and healthcare
providers are able to communicate directly face-to-face.
The use of GPT-3 and other Large Language Models (LLMs)
have been realized in general healthcare domain especially in NLP area. These
are models designed from large volumes of text data and are capable of analysing
text and drawing insights from large chunks of information which are perfect
for translating content into language that is easily understandable by average
citizens. It has also been shown that AI language tools could paraphrase the
specialist’s medical terms, making patients better understand during
consultations2. Using LLMs, one can
obtain explanations, diagnoses and treatment plans in a manner understandable
by a patient as well as explain the potential dangers to the patient.
Furthermore, it is because of LLM’s natural interaction
skills that more interaction-based patient education can now be possible.
Responsive virtual assistants have been tested across several healthcare areas
such as telehealth to ensure that healthcare clients obtain prompt responses to
their inquiries. Variety of tasks: such assistants can help to explain medical
information, advise and direct a patient through the decision-making process,
thus increasing the patient’s participation in treatment3. In addition, there is evidence that the use
of AI increases patient satisfaction with the care process because consumers
receive more clear and easily understandable knowledge regarding their
conditions and treatments4.
Apart from helping to enhance consultation interaction,
LLMs can also be useful in the production of letter, handout and brochure
material, including discharge information and post-consultation care
directives. These AI-developed content may further be presented in a manner
that is patient-sensitive, in accordance to the patient’s cognitive capacity,
in order to make it easy for the patent to make proper decisions from the read
resources. Significantly, the study has highlighted that where patients are
exposed to health education material that they understand and consider relevant
to their circumstances they are more compliant regarding medication regimen,
clinic appointments and generally healthier5.
3. Problem statement
Yet, when it comes to breaking barriers in communication,
telemedicine still presents major hurdles, notably those to do with the medical
terminology used. Because healthcare providers tend to use technically dense
language, patients are frequently bewildered, apprehensive and nonadherent.
Such a circumstance is also compounded by the lack of time to help clear the
misunderstanding since during virtual consultation, patients may not ask
questions or sometimes they may not fully comprehend what the health care
professional is trying to explain to them. This is even more so the case with
low health literacy patients, elderly people and those with chronic diseases
who need regular checkups.
Furthermore, medical information is usually complicated and
thus, it is usually misinterpreted, meaning that people will make poor health
decisions. Patients may fail to adhere to the correct dosage regimens, clinic
visits or prescribed therapies and therefore influence the health status of the
patients in facility. A study shows that patients who have difficulty in
following their physicians’ instructions have worse outcomes including high
risk for hospitalization and high cost of healthcare6. Given the increasing utilization of telemedicine,
it is important to overcome these communication barriers with a purpose of
ensuring patients understand their medical condition and the processes involved
in treatment.
The challenge is especially acutely felt in telemedicine,
where direct contact is restricted and additional cues that might clear up
confusion do not apply. Thus, the need for new ways on fostering or enhancing
the communication between the patients and the providers as well as enhancing
the availability of the health information. A viable approach can be found in
Generative AI and Large Language Models as tools that can be used to translate
between doctors and patients, to find ways to address the issue of complex
medical lexicon that may be understood in a very different way by the latter.
4. Solution
LLMs of the generative AI are particularly useful in
answering this call to enhance telemedicine by translating the medical
terminology and explanations into understandable patient language. That is why
LLCs is the most effective tool when addressing a large number of people, as it
helps to understand medical terms and rephrase them, putting into words that
convey the same information effectively. Patients who undergo
smartphone-equipped LLMs can give them an easy-to-understand diagnosis
explanation based on the telemedicine consultation7.
The real-time information simplification assists patients in comprehending
medical conditions and possibilities of treatment and considerably improve
patients’ awareness of the healthcare they receive.
In addition, LLMs may be used to improve patients’
experience by offering them accurate multimedia replies to inquires at a
real-time manner. These artificial intelligent systems, can be trained to
producing responses, which are patient friendly, addressing the particular
questions patients have. For instance, an AI-based chatbot, for instance, can
provide answers to questions regarding medications, discuss the possible side
effects and give recommendations regarding home remedy for the symptoms8. Not only does this facilitate the conveyance
of medical knowledge from the patient’s perspective, as well as relieve the
patient’s stress and increase their confidence in telemedicine.
Apart from enhancing the timely communication, LLMs can
produce individual written documents including discharge instructions, further
management advice and medication regimen. These are mostly written materials
that can be made consistent with the health literacy level of the intended
patient to have an easy time understanding instructions given to him/her. The
use of such an approach increases patient compliance, evades mistakes and
enhances desired health results9.
In general, the combination of Generative AI and LLMs into
the consultation used in telemedicine solves the problem of communication
barriers that affect patients’ understanding. In this paper, LLMs will be shown
to have the potential to increase the quality of care and patient satisfaction
in telemedicine through the reduction of medical complexity, the increase in
engagement and the preparation of information to be understood and readily
usable.
5. Conclusion
Holding the benefits of using Generative AI to
telemedicine, especially Large Language Models, it can be stated that the
option has a high possibility to improve the process of interaction between the
patient and provider. Because LLMs can easily translate medical terms and
conditions into layman language that patients understand their doctors’ advice,
they can enhance the consultative process, enhance patient satisfaction and
thus boost patient health. If providers could explain medical terminology in simple
terms as they speak, the chances are that the patients will be in a better
position to understand their doctors and even their treatment plans. This is
especially important in telemedicine since there are no face-to-face contacts
and a patient may not want to ask questions or admit to not understanding
something.
There is evidence that clearly suggests that lack of
effective communication between patients and healthcare providers results to
poor patient satisfaction, increased levels of health-related anxiety and
overall poor health. Thus, to eliminating such problems, telemedicine platforms
can offer a number of improved and accessible LLMs. Also, the individual
patient-friendly written materials allow for full patient comprehension of
their post-treatment recommendations, which sustains patient compliance with
treatments and follow-up recommendations.
In conclusion the study shows that LLMs are beneficial in
enhancing communications on a range of conditions that are significant in
improving the lives of patient and should also be understood that while such
systems hold substantial gain in enhancing communication, they are not to be
seen as a substitute for human health care professional, but a tool that will
help in supplementary care. It is clear that LLMs can help providers deliver
accurate, accessible and relevant information, but the way bringing human touch
to patient-clinician communications affects trust, empathy and understanding in
the context of patient care. However, AI communication tools in patient care
require constant improvements in the area of enhancing the precise and
efficient detection of all relevant patient needs.
Therefore, Telemedicine innovation through the Generative
AI brings optimism to the delivery and focus of healthcare with greater patient
experiences. Through increasing understanding enhancing patient interaction,
LLMs can help fill the gap and create better healthcare throughout the nation
for the digital age.
6. References