# E-health supported referral for patients with breast abnormalities at primary healthcare facilities in Ethiopia: protocol for a cluster-randomised controlled trial

## TL;DR

This study tests a digital referral app to improve early detection of breast cancer in Ethiopia by linking primary healthcare with diagnostic facilities.

## Contribution

The study introduces a digital referral system (DINKNESH) to enhance follow-up and reduce delays in breast cancer diagnosis in low-resource settings.

## Key findings

- The effectiveness of the DINKNESH app in improving referral completion will be evaluated using a cluster-randomized trial.
- Qualitative interviews will assess the acceptability and scalability of the app-based referral system.
- Findings will inform strategies to support early breast cancer detection aligned with WHO goals.

## Abstract

A weak referral system combined with limited diagnostic facilities is among the key systemic barriers to the early detection of breast cancer. Strengthening patient pathways is essential to improve follow-up and reduce delays in cancer diagnosis and care. This study aims to assess the effectiveness of the DINKNESH referral and follow-up app, a digital, app-based patient referral system linking early detection of breast-related abnormalities at primary healthcare settings with diagnostic facilities in Ethiopia.

A two-arm cluster randomised trial with an embedded qualitative study is being conducted at eight primary health facilities and affiliated satellite hospitals in Ethiopia. The study includes women aged ≥18 years presenting with breast abnormalities, as well as women aged ≥30 years with positive findings on clinical breast examination. In intervention cluster facilities, the referral process for further diagnosis is supported by the DINKNESH referral and follow-up app, which facilitates patient registration, data transfer, and reminder services. This is compared with the routine paper-based referral process in the control clusters. The primary outcome is the proportion of completed referrals. All outcome measures will be analysed using IBM SPSS Statistics 25.0. A mixed-effects logistic regression model will be applied, adjusting for potential confounders and accounting for clustering at the facility level. At the end of the intervention period, qualitative interviews will be conducted using the RE-AIM framework to explore the acceptability, challenges, sustainability, and scalability of the intervention.

This study will provide robust evidence on whether app-based referral systems for women with breast symptoms can improve follow-up and facilitate early breast cancer detection in low-resource settings such as Ethiopia. The findings will support the WHO Global Breast Cancer Initiative’s goal of diagnosing more than 60% of breast cancer cases at an early stage.

PACTR202411893209747. Registered on 25 November 2024, https://pactr.samrc.ac.za/