This paper presents TimelinePTC, a web-based tool developed to improve the collection and analysis of Pathways to Care (PTC) data in first episode psychosis (FEP) research. Accurately measuring the duration of untreated psychosis (DUP) is essential for effective FEP treatment, requiring detailed understanding of the patient's journey to care. However, traditional PTC data collection methods, mainly manual and paper-based, are time-consuming and often fail to capture the full complexity of care pathways. TimelinePTC addresses these limitations by providing a digital platform for collaborative, real-time data entry and visualization, thereby enhancing data accuracy and collection efficiency. Initially created for the Specialized Treatment Early in Psychosis (STEP) program in New Haven, Connecticut, its design allows for straightforward adaptation to other healthcare contexts, facilitated by its open-source codebase. The tool significantly simplifies the data collection process, making it more efficient and user-friendly. It automates the conversion of collected data into a format ready for analysis, reducing manual transcription errors and saving time. By enabling more detailed and consistent data collection, TimelinePTC has the potential to improve healthcare access research, supporting the development of targeted interventions to reduce DUP and improve patient outcomes.

翻译：本文介绍TimelinePTC这一基于网络的工具，旨在改善首发精神病（FEP）研究中就医路径（PTC）数据的采集与分析。准确测量未治疗精神病持续时间（DUP）对于有效治疗FEP至关重要，这需要详细理解患者的就医过程。然而，传统的PTC数据采集方法主要依赖手动和纸质记录，耗时且往往无法全面反映就医路径的复杂性。TimelinePTC通过提供数字化平台实现协作式实时数据录入与可视化，从而提升数据准确性和采集效率，解决了上述局限。该工具最初为康涅狄格州纽黑文的“精神病早期专项治疗”（STEP）项目开发，但其基于开源代码库的设计使其可轻松适配其他医疗环境。该工具显著简化了数据采集流程，使其更高效、更易用，并自动将采集的数据转换为可供分析的格式，减少手动转录错误并节省时间。通过实现更详细、更一致的数据采集，TimelinePTC有望改善医疗可及性研究，支持开发针对性干预措施以缩短DUP并改善患者预后。