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In recent years, evidence for optimizing drug therapy has accumulated, making it possible to implement drug treatments tailored to individual patients. In this context, to achieve optimization of treatment, it is essential for patients themselves to actively participate in their treatment and receive care in accordance with those decisions (a concept known as adherence). It is important for healthcare professionals to accurately capture and address patient symptoms, side effects, and treatment-related concerns and needs. However, it is known that patients do not communicate much of this information to healthcare professionals. The lack of medical communication and barriers to patients sharing information with healthcare providers are major challenges hindering the optimization of treatment.

To solve these issues, I established the "Patient Salon" as a third place where people can learn and talk about health and medical care, and it has been held monthly for 11 years. Many healthcare professionals also want to understand patients' thoughts outside of the typical clinical relationship between provider and patient. Today, it has become a place where both parties gather to understand each other's perspectives and engage in flat dialogue.

Opportunities for patients to share their illness experiences on internet social media (blogs, SNS, YouTube, etc.) are increasing. In these information sources, thoughts on illness and treatment, as well as daily life difficulties, are shared candidly. Consequently, patients use social media as a source for medical information, but it is difficult to find the specific information they need from the vast amount of data available. Therefore, our lab has aimed to develop and utilize models that find medically valuable insights for patients from the massive amount of text they generate. Specifically, we are building models using natural language processing to extract information on drug side effects, treatment concerns, QOL, and practical knowledge from text posted by patients on social media and other platforms.

Furthermore, we regularly hold exchanges and discussions between patient groups and lab students to obtain feedback from a patient perspective. Utilizing this model, we are exploring ways to make it easier for patients to connect with appropriate medical care and social support at an early stage. To take the optimization of treatment one step further, it is desirable to improve patient adherence and propose new designs for medication support that incorporate the patient's perspective.