ABSTRACT
INTRODUCTION: Front-line health workers in remote health facilities are the first contact of the formal health sector and are confronted with life-saving decisions. Health information systems (HIS) support the collection and use of health related data. However, HIS focus on reporting and are unfit to support decisions. Since data tools are paper-based in most primary healthcare settings, we have produced an innovative Paper-based Health Information System in Comprehensive Care (PHISICC) using a human-centred design approach. We are carrying out a cluster randomised controlled trial in three African countries to assess the effects of PHISICC compared with the current systems. METHODS AND ANALYSIS: Study areas are in rural zones of Côte d'Ivoire, Mozambique and Nigeria. Seventy health facilities in each country have been randomly allocated to using PHISICC tools or to continuing to use the regular HIS tools. We have randomly selected households in the catchment areas of each health facility to collect outcomes' data (household surveys have been carried out in two of the three countries and the end-line data collection is planned for mid-2021). Primary outcomes include data quality and use, coverage of health services and health workers satisfaction; secondary outcomes are additional data quality and use parameters, childhood mortality and additional health workers and clients experience with the system. Just prior to the implementation of the trial, we had to relocate the study site in Mozambique due to unforeseen logistical issues. The effects of the intervention will be estimated using regression models and accounting for clustering using random effects. ETHICS AND DISSEMINATION: Ethics committees in Côte d'Ivoire, Mozambique and Nigeria approved the trials. We plan to disseminate our findings, data and research materials among researchers and policy-makers. We aim at having our findings included in systematic reviews on health systems interventions and future guidance development on HIS. TRIAL REGISTRATION NUMBER: PACTR201904664660639; Pre-results.