JMIR Publications

ABSTRACT

Background:

The rising number of cancer survivors and the shortage of healthcare professionals challenge the accessibility of cancer care. Health technologies are necessary for sustaining optimal patient journeys. To understand individuals' daily life during their patient journey, qualitative studies are crucial. However, not all patients wish to share their story with researchers.

Objective:

To analyse colorectal cancer (CRC) patients' experiences, we used a novel machine learning-driven approach: 'patients community journey mapping.'

Methods:

The CRC patient forum posts from the Cancer Survivors Network USA was used. Topic modelling, as a part of machine learning, was used to recognise the topic patterns in the posts. Researchers read the most relevant 50 posts of each topic, dividing them into ‘home’ or ‘hospital’ contexts. A patient community journey map, derived from patient’s stories, was developed to illustrate patients’ experience. CRC medical doctors and a quality of life expert evaluated the map.

Results:

Based on 294.166 posts, 37 topics and 10 upper clusters were produced. Dominant clusters include ‘Daily activities while living with CRC (18.3%) and ‘Understanding treatment including alternatives and adjuvant therapy’ (14.9%). The topics discussed related to the home context have a more emotional content compared to the hospital context. The patient community journey map was constructed based on these findings.

Conclusions:

A machine learning-driven approach is a promising solution to analyse patients’ experiences. The innovative application of patient community journey mapping provides a unique perspective into the challenges of patients’ daily lives, essential for guiding the right support at the right moment.