Department of Psychiatry, Yale University School of Medicine, New Haven, Connecticut, United States of America.
Program for Specialized Treatment Early in Psychosis (STEP), New Haven, CT, United States of America.
PLoS One. 2024 Jul 19;19(7):e0302116. doi: 10.1371/journal.pone.0302116. eCollection 2024.
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.
本文介绍了 TimelinePTC,这是一个基于网络的工具,旨在改进首次发作精神病(FEP)研究中路径到护理(PTC)数据的收集和分析。准确测量未治疗精神病持续时间(DUP)对于有效的 FEP 治疗至关重要,需要详细了解患者的护理路径。然而,传统的 PTC 数据收集方法主要是手动和基于纸张的,既耗时又常常无法捕捉护理路径的全部复杂性。TimelinePTC 通过提供一个用于协作、实时数据输入和可视化的数字平台来解决这些限制,从而提高数据的准确性和收集效率。该工具最初是为康涅狄格州纽黑文的专门治疗早期精神病(STEP)计划创建的,其设计允许通过其开源代码库轻松适应其他医疗保健环境。该工具大大简化了数据收集过程,使其更高效、更用户友好。它自动将收集的数据转换为可用于分析的格式,减少了手动转录错误并节省了时间。通过实现更详细和一致的数据收集,TimelinePTC 有可能改善医疗保健获取研究,支持制定有针对性的干预措施来减少 DUP 并改善患者的预后。