The deployment of large language models, like the ChatGPT, has captured the interest of the healthcare industry. This technology utilizes deep learning algorithms to create responses to natural language inputs in a conversational way. Such large language models aim to improve the way patients interact with healthcare providers, enhancing the quality of healthcare services overall.
Although the potential benefits of the technology seem promising, it presents a bleak outlook for interoperability and fairness under the market structure of the healthcare system. In the US, the healthcare market is divided among providers, with limited competition against one another in their regions. This lack of competition means there are not enough business incentives for healthcare providers to undertake significant digital transformation projects to facilitate interoperability.
The COVID-19 pandemic accelerated the transformation of healthcare, leading to the emergence of new competitors that are not limited by geographical boundaries, like OneMedical, with a subscription-based primary care service provided by Amazon. These technology companies possess the resources, interest, incentive, and knowledge to leverage the use of large language models to facilitate interactions between patients and medical providers.
However, these large language models could pose an actual threat to the traditional business model of healthcare providers. Providers could delay the onset of the technology by reinforcing the protection of their medical data, since sharing medical data could be feeding a beast that will eventually turn against them. These language models could cause a hindrance in information exchange, leading to healthcare providers trying to delay interoperability through technological and legal means. Providers could even lobby to establish legal obstacles to hinder the progress of AI-powered medical services.
The impact of large language models on healthcare is complex, with concerns about exacerbating existing disparities in access to healthcare. Providers could create a two-tier system, with patients with better insurance being prioritized for in-person visits and those with lower socioeconomic status being left with AI-based chatbots.
As a technologist, Niam Yaraghi believes that healthcare services will be significantly improved through research and innovation in the long run. However, given the current financing of healthcare services in the US, the government’s role in supporting such efforts is imperative. The government should update its perspective on reimbursable medical services, recognizing the emergence of telemedicine and AI-enabled healthcare delivery methods and ensure that reimbursement is on par with conventional in-person visits.
To prevent providers from blocking the free flow of medical data, the government should actively enforce rules that prohibit such actions. Simultaneously, the government should re-evaluate old regulations that hinder the financial incentives for sharing medical data. In the long run, the focus should be maintained on value-based payment models that incentivize providers to optimize their services using technologies, such as AI and telehealth, to ensure providers have the freedom to use the technology that best suits their patient needs.
In conclusion, while the deployment of large language models like ChatGPT can improve healthcare development, their impact on the current health care systems in place is intricate and could pose a threat to the traditional providers. Encouraging interoperability while maintaining competition and fairness in the healthcare industry is crucial.
