Leveraging artificial intelligence and data science techniques in harmonizing, sharing, accessing and analyzing SARS-COV-2/COVID-19 data in Rwanda (LAISDAR Project): study design and rationale.

BACKGROUND: Since the outbreak of COVID-19 pandemic in Rwanda, a vast amount of SARS-COV-2/COVID-19-related data have been collected including COVID-19 testing and hospital routine care data. Unfortunately, those data are fragmented in silos with different data structures or formats and cannot be used to improve understanding of the disease, monitor its progress, and generate evidence to guide prevention measures. The objective of this project is to leverage the artificial intelligence (AI) and data science techniques in harmonizing datasets to support Rwandan government needs in monitoring and predicting the COVID-19 burden, including the hospital admissions and overall infection rates. METHODS: The project will gather the existing data including hospital electronic health records (EHRs), the COVID-19 testing data and will link with longitudinal data from community surveys. The open-source tools from Observational Health Data Sciences and Informatics (OHDSI) will be used to harmonize hospital EHRs through the Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM). The project will also leverage other OHDSI tools for data analytics and network integration, as well as R Studio and Python. The network will include up to 15 health facilities in Rwanda, whose EHR data will be harmonized to OMOP CDM. EXPECTED RESULTS: This study will yield a technical infrastructure where the 15 participating hospitals and health centres will have EHR data in OMOP CDM format on a local Mac Mini ("data node"), together with a set of OHDSI open-source tools. A central server, or portal, will contain a data catalogue of participating sites, as well as the OHDSI tools that are used to define and manage distributed studies. The central server will also integrate the information from the national Covid-19 registry, as well as the results of the community surveys. The ultimate project outcome is the dynamic prediction modelling for COVID-19 pandemic in Rwanda. DISCUSSION: The project is the first on the African continent leveraging AI and implementation of an OMOP CDM based federated data network for data harmonization. Such infrastructure is scalable for other pandemics monitoring, outcomes predictions, and tailored response planning.

Towards Improving Data Quality in Electronic Medical Records: An Investigation of Data Completeness in a Tertiary Hospital in Rwanda.

Data quality is a primary barrier to using electronic medical records (EMR) data for clinical and research purposes. Although EMR has been in use for a long time in LMICs, its data has been seldomly used. This study aimed to assess the completeness of demographic and clinical data in a tertiary hospital in Rwanda. We conducted a cross-sectional study and assessed 92,153 patient data recorded in EMR from October 1st to December 31st, 2022. The findings indicated that over 92% of social demographic data elements were complete, and the completeness of clinical data elements ranged from 27% to 89%. The completeness of data varied markedly by departments. We recommend an exploratory study to understand further reasons associated with the completeness of data in clinical departments.

Integration of International Classification of Diseases Version 11 Application Program Interface (API) in the Rwandan Electronic Medical Records (openMRS): Findings from Two District Hospitals in Rwanda.

World Health Organisation (WHO) has updated the International Classification of Diseases to version 11 (ICD-11) which was recently adopted for use by countries in 2019. ICD-11 can be used in Electronic Medical Records (EMR) systems with support of extended technologies like Application Program Interface (API). Integration of ICD-11 in Rwandan EMR (OpenMRS) in two health facilities was conducted in July-October 2019. Findings indicated that adapting ICD11-API in EMR is feasible. More than 50% of diagnoses were recorded using ICD-11. Healthcare providers perceived ICD-11 API as easy to learn and useful for harmonization of diagnosis, data reporting and insurance reimbursement. Integration of ICD-11 API in EMR can be scaled up to all hospitals for use in Rwanda and other countries using similar system.