As a new model beyond conversational AI, generative AI (GenAI) is redefining patient experiences across healthcare organizations. GenAI is increasing the efficiency of patient-provider conversations through the handling of sequential exchanges and near-instantaneous consultation.
However, like all new things in science, it also has its drawbacks that the healthcare professionals have to solve to enjoy the innovation to the maximum.GenAI has immense value in transforming simple and routine patient/provider interactions.
Digitization allows patients to communicate with the healthcare system through chatbots, symptom checkers, call center automation, and enhanced patient portals that do not necessarily involve one-on-one contact with a provider.
The Risks of Using Generative AI in Healthcare
1. Medical Misinformation
These are all truths that any Generative AI system is only as good as the data the system is trained on. With the data perhaps a bit outdated or inaccurate, these tools become misleading to the patients. For example, the errors in a GenAI chatbot might stem from the fact that it is loyal to outdated advice on preventive care screenings.
These are risks that patients know and understand. The Wolters Kluwer Health survey reveals that 49% of the healthcare users are concerned that GenAI may provide misinformation. In order to overcome this issue, care providers can prompt the patients to check the information that was given by the AI and consult with their doctor.
2. Algorithmic Bias
One of the problems AI faced is algorithmic bias. Since these Generative AI systems can be trained on biased data sets, they augment inequalities in the healthcare field.
For instance, AI employed for the diagnosis of diabetes and risks involved is biased primarily based on the race that results in wrong treatment. Likewise, machine bias leads to improper distribution of patients regarding their treatments or provision of medical advice to them.
IT teams in healthcare need to remain vigilant about bias in AI tools and ensure that they start training these tools on diverse data sources. This can assist in the development of more equal systems that offer quality care to all.
3. Lack of Transparency
The last element that shall help build trust in Generative AI relates to being transparent. About a third of patients continue to feel confused about the application of AI within the medical field. According to the survey conducted by Athenahealth, 46% of participants reported that they had no idea how GenAI was being utilized.
To this, the healthcare providers and organizations should explain the application of GenAI in algorithms to triage patients, supplement chatbots, or in managing call centers. Understanding how these tools operate and being open about it could help patients to accept the use of these systems.
4. Limited Clinician Oversight
Some of the GenAI tools, like ChatGPT, have quickly indicated the possibility of responding to patient portal messages. In fact, some research findings indicate that some of these tools yield better empathetic responses than hurried professionals.
However, putting stock in just AI for such interactions is dangerous. While responding to patients’ messages, AI may provide incorrect or context-free information that must be reviewed by a human before being sent to the patient.
Healthcare providers need to determine the benefits and drawbacks of using GenAI and, at the same time, decide whether or not to disclose the use of this tool in communication with patients is appropriate.
5. Deepening the Digital Divide
As it stands, Generative AI threatens to deepen the digital divide in healthcare—the divide between ‘technological haves’ and ‘have-nots.’
Recent polls point to the effect of age and income; young and well-endowed clients trust GenAI more than old and low-income patients. This could mean that the currently marginalized communities would be denied access to the AI tools, hence aggravating the healthcare inequalities.
In light of this, it is imperative that all patients be informed of GenAI tools by the health care providers independent of their computer literacy levels. Education and making sure all our patients are exposed to the technology can help fill this gap.
Managing Risks for Improved Results
Although GenAI may drastically transform the whole paradigm of patient engagement, there are certain risks that need precautions taken by the healthcare providers. Some key steps include:
- Encouraging Verification: Inform patients that information they receive from AI is accurate but should be discussed with their clinician before implementation.
- Ensuring Bias-Free Training: Train AI models with various datasets and check algorithms for bias on a frequent basis.
- Being Transparent: Such information should be presented clearly to ensure the patients can understand how GenAI is applied in patient care, thus gaining their trust.
- Maintaining Oversight: The patient correspondence should be reviewed by people in order to eliminate mistypes and avoid sounding robotic.
- Bridging the Digital Divide: Promote patient education and support in order to ensure the feasibility of artificial intelligence for everyone, young and old, rich or poor.
Conclusion
In application to digital patient engagement, generative AI presents great prospects for making patient access easier, faster, and more customized. However, healthcare professionals need to be careful so as not to fall into such things as misleading information and bias and unfair treatment of candidate genes. With proper measures put in place, GenAI can be used to the maximum to help deliver proper and fair health care services to the patients.
