The Influence of Internet-Derived Misinformation on Medical Treatment Decisions: A Social Learning Perspective

Purpose “Internet-Derived Information Obstructing Treatment” refers to the phenomenon where patients make their health decisions based on misinformation found online, leading to poor or harmful medical choices. DeGroot’s social learning model is used in this study to enquire at how false information spreads on the internet and influences people’s choices about treatment. We examine how likely patients will choose the wrong treatment, impacting their mental health, such as by making anxiety and depression symptoms worse. In addition, we examine the dynamics of low, high, and mixed levels of influence in the spread of false information. Methods We derived a mathematical framework to analyse the impact of peer-to-peer information sharing on medical decisions using DeGroot’s social learning model by modelling the process of belief updates in digital health networks. Each person’s decision-making is impacted by the views and information they encounter from others; this is known as the multiplier effect of social learning and is incorporated into the model. This method mimics the increasing likelihood that patients will encounter health-related disinformation as they engage with various online communities. Low misinformation influence, high misinformation influence, and mixed initial beliefs were the three scenarios that were modelled in the study. Treatment choices and mental health results were evaluated in light of each scenario. Based on patients’ exposure to false information through internet platforms, the odds ratios (ORs) for negative medical decisions, anxiety, and depression were computed. Results In all of these situations, people were much more likely to make a bad treatment choice when they had incorrect information. Repeated interactions with peers who had been influenced by online false information raised patients’ chances of making bad treatment choices by 50% in the high misinformation scenario (95% confidence interval [CI]: 1.25–1.80). Some patients who were given false information had 40% more anxiety and 35% more depression (OR: 1.40, 95% CI: 1.10–1.70 and OR: 1.35, 95% CI: 1.05–1.65, respectively). According to the model, social learning made false information more powerful, and this was most clear in situations where false information was common. Conclusion DeGroot’s social learning process is a good way to explain how false information found on the internet can affect a patient’s choice of treatment and mental health. Misinformation from the internet can lead to bad medical decisions and higher mental health risks, especially in networks where peers have a lot of power. The results stress the need for targeted interventions to improve digital health literacy and stop the spread of false information in online health communities. Future research should look into ways to stop the social learning processes that keep spreading false information.