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

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.

Fig 1. TimelinePTC in different phases of use.

1a. Initial screen for baseline information 1b. The timeline drawn from these dates 1c. The interface that pops up when the timeline is clicked 1d. The timeline showing an event that has been entered 1e. A more robust pathway to care 1f. PTC data extracted to comma-separated values (CSV) format.

Fig 2. A directional graph of all PTCs collected in this study (from Mathis et al, 2022 [8]).

This is an example of the analytic output possible with robust collection of high-quality PTC data. Arrows depict the sequential progression individuals took from Onset to STEP enrollment. Clinical nodes are on the top, community nodes on the bottom. The thickness of the edge (line) between nodes reflects the frequency of traffic between them, and the size of each node reflects the cumulative number of interactions with the node type across all PTCs.