Unreliable patient self-reporting complicates diabetes management. This study investigates how AI-generated summaries of patient-chatbot conversations can be structured to help healthcare professionals detect deception and non-adherence. To address this, we developed a novel pipeline by first identifying four key behavioral indicators from literature and then using advanced prompt engineering to automatically flag these in structured summaries. The system's effectiveness was evaluated in an annotation experiment using synthetic chat data. The results showed that the summaries did not improve detection accuracy, increased annotation time, and revealed a critically low inter-annotator agreement. These findings highlight the inherent subjectivity and complexity of the detection task, demonstrating that the developed summarization method is not an effective intervention. Although the approach was unsuccessful, this research contributes a novel summarization pipeline, an open-source annotation tool, and a synthetic dataset, establishing a baseline for future work in enhancing doctor-patient communication.