TimelinePTC: Development of a unified interface for pathways to care collection, visualization, and collaboration in first episode psychosis

TL;DR

TimelinePTC is a web-based platform that streamlines collection, visualization, and collaboration on pathways to care data in first episode psychosis, aiming to improve data accuracy and reduce time in research and clinical settings.

Contribution

The paper introduces TimelinePTC, a digital, open-source tool that enhances data collection and analysis of pathways to care in psychosis research, addressing limitations of manual methods.

Findings

Significantly improves data collection efficiency.

Reduces manual transcription errors.

Facilitates detailed analysis of care pathways.

Abstract

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.