Real-Time AMR Monitoring through Integrated Data Network in Rwanda

Harnessing the power of harmonized and federated hospitals electronic health records (EHRs) data from health facilities, linked to pathogenes genomics data, communitity residues biosamples data, and leveraging mixed data analytics, to provide real-time insights on the antimicrobial resistance  and bacterial infections burden in Rwanda yielding informed decision-making and targeted interventions.

Prof Dr Marc Twagirumukiza, MD, PhD. Principal Investigator

AMR burden in Rwanda, like in other developing countries, remains poorly documented due to inadequate surveillance systems. Antibiotics are readily available over-the-counter, exacerbating overuse and misuse issues. Substandard and falsified medicines are prevalent, with 18.7% regional prevalence in Africa and 12.4% in antibiotics. Recent studies in Rwanda show high resistance rates; 31.4% and 58.7% of Escherichia coli and Klebsiella isolates are resistant to third-generation cephalosporins, respectively. Additionally, 8% of E.coli isolates are resistant to imipenem, and Staphylococcus aureus shows significant resistance to oxacillin (82%) and vancomycin (6%). Pathogens genomics data, community data from wastewater, urine or stool samples detecting antimicrobials residues are untapped environmental data in Rwanda, which provide new knowledge of the extent of AMR in human and animal populations (One Health). To make this community data useful, it must be integrated or linked with other patient-level data (electronic Health Records -EHRs). However, these EHRs data collected routinely by health facilities and infectious diseases laboratories in Rwanda are currently fragmented, stored in different formats and structures, and dispersed across various locations, limiting their accessibility and usage. Innovative data harmonization technology such as OHDSI/OMOP CDM has shown power to leverage these scattered data effectively.

Our solution serves on top of all, patients and community but also healthcare providers, policymakers, researchers, public health officials, and the general population of Rwanda.

For patients, integrating EHRs, pathogenes genomic, and antimicrobial residue biosample data, healthcare providers gain insights into AMR patterns, enabling personalized treatment approaches and enhancing patient care (right medicine for the right condition). Our solution could ultimatelly contribute to the design of next generation of Clinical Decision Support Systems.

For healthcare providers, our solution addresses the need for comprehensive data analytics tools to support evidence-based decision-making, improve clinical outcomes, and enhance antimicrobial stewardship practices. 

Policymakers benefit from our solution by gaining access to timely and accurate data on antimicrobial resistance trends and prevalence that informs policy development, resource allocation, and intervention strategies to mitigate the impact of antimicrobial resistance on public health and healthcare systems.

Researchers benefit from access to integrated health data for epidemiological studies, pharmacovigilance, and antimicrobial resistance surveillance. 

Public health officials can utilize our solution to monitor in real-time the antimicrobial resistance at the community level, identify hotspots of resistance, and implement targeted interventions to control the spread of resistant pathogens, develop evidence-based strategies for antimicrobial stewardship and infection prevention and control measures.

Our solution provides a public good in the form of actionable insights and evidence-based strategies to combat antimicrobial resistance (AMR) and bacterial infections based on open source infrastructure. 

Data portal access for visualization will be public for everyone, contributing to the science and service to the community. The community will benefit from a rea-time view of the resudues biosapmles analysis data, reflecting antimicrobial consomtion in their region but also the budrne of infectious diseases.

Data extract for subsquent analysis will be restricted to only policy makers and researchers with a prior online request and approval (using the same approach as for the UK CPRD or EU CDC).

By integrating diverse datasets, including electronic health records, genomic data, and antimicrobial residue data, our solution generates real-time analytics and decision support tools accessible to healthcare providers, policymakers, researchers, and public health officials. 

These resources empower stakeholders with the knowledge, collaboration and tools necessary to improve antimicrobial stewardship, enhance infection prevention and control measures, and mitigate the impact of AMR on public health. This public good is globally accessible, providing valuable insights and resources to address the AMR crisis across regions and populations, under fair, reasonable, and non-discriminatory terms.

Our solution will create tangible impact by empowering healthcare providers, policymakers, and public health officials in Rwanda to make informed decisions and implement targeted interventions to combat AMR and bacterial infections.
By integrating EHRs, genomic data, and antimicrobial residue data, our solution provides actionable insights and decision support tools that enhance antimicrobial stewardship, improve infection prevention and control measures, and optimize treatment outcomes. This will lead to a reduction in the incidence of AMR-related infections and improved patient outcomes.

Our solution will ultimatelly help doctors to chose the right medicine for the right patient, improving the quality of life of patients, reducing the amount of re-prescriptions, and supporting the more efficient use of healthcare resources. 

Additionally, our solution fosters collaboration and knowledge-sharing within the global health community, contributing to the development of evidence-based strategies to address the AMR crisis globally. Through these activities, our solution will have a tangible impact on improving public health outcomes and promoting sustainable healthcare practices in Rwanda and beyond.

Over the next year, we will scale our impact by expanding data integration efforts to additional healthcare facilities in Rwanda, increasing the coverage and depth of our data network. Through the community & Policymakers engagment program, we will strengthen partnerships with governmental agencies, international organizations, and local stakeholders to promote adoption and utilization of our solution embedded in other running efforts such as AWaRe (Access, Watch and Reserve classification of antibiotics) and antibiotic stewardship interventions. Additionally, we will conduct capacity-building initiatives and training programs to empower healthcare providers and public health officials in leveraging our tools effectively.

Over the next three years, we will further scale our impact by extending our solution to other countries in sub-Saharan Africa (SSA), fostering regional collaboration and knowledge-sharing. This effort will build on existing network in EDCTP network but also in OHDSI African Chapter.

We will continue to enhance our data quality and analytics capabilities, incorporating advanced machine learning and predictive modeling techniques to improve the accuracy and relevance of our insights. Through these efforts, we aim to catalyze a paradigm shift towards data-driven approaches to AMR management, leading to sustained reductions in AMR incidence and enhanced patient outcomes across Rwanda, SSA, and beyond.

We measure success against our impact goals through a comprehensive monitoring and evaluation framework that encompasses both quantitative and qualitative indicators.

Quantitative indicators include:

1. Reduction in AMR prevalence: Measure changes in antimicrobial resistance rates over time, focusing on key pathogens and antibiotics.
2. Improvements in patient outcomes: Track metrics such as infection recurrence rates, hospital readmission rates, and mortality rates associated with antimicrobial-resistant infections.
3. Adoption and utilization of decision support tools: Monitor the number of healthcare providers using our analytics platform, frequency of tool usage, and adherence to evidence-based recommendations.
4. Cost savings and resource utilization: Assess the economic impact of our solution by measuring reductions in healthcare expenditures related to antimicrobial-resistant infections and improvements in resource allocation efficiency.

Qualitative indicators include:

1. Stakeholder satisfaction: Gather feedback from healthcare providers, policymakers, and public health officials through surveys, interviews, and focus groups to assess satisfaction with the usability, relevance, and effectiveness of our solution.
2. Knowledge transfer and collaboration: Measure the extent of knowledge sharing, collaboration, and capacity-building activities facilitated by our solution, including the number of workshops, training sessions, and research collaborations initiated.

We will continue to refine our monitoring and evaluation metrix to ensure ongoing assessment and optimization of our impact.

When it comes to healthdata use and re-use, standards barriers are known that may impede the accomplishment of our goals in the next year and the next three years:

1. Intra-muros Data Fragmentation and Accessibility: Even inside one signle health facility, currently, electronic health records (EHRs) and other health data in Rwanda are fragmented, stored in disparate tables/computres/servers. This fragmentation hinders data accessibility and applying the same ETL for all, making the ETL development time long.

   Overcoming Barrier: We plan to address this by anticipating a needs assessment and feasibility study in each participating health facility, where we will know upfront the status of the data, their structure and their storage and develop ETLs accordingly.

2. Data Privacy and Security Concerns: Although the federating data in a distributed manner presents a solution for maintaining data privacy while enabling comprehensive analysis, the awarness in Rwanda is low about the power of this methodology.

   Overcoming Barrier: On top of all, we will implement policy engagement program. 

3. Resource Constraints: Limited resources, including financial, technological, and human resources

   Overcoming Barrier: We plan to leverage partnerships and collaborations within the consortium, building from previous networks. Additionally, capacity-building initiatives and knowledge-sharing programs will empower local stakeholders.

This Trinity Challenge call is a perfect match with the aims and goals of our proposal. Both aim addressing the AMR through new paradigm shift, involving the innovative methodes and leveraging new data. This Trinity Challenge on Antimicrobial Resistance is calling for data-driven solutions that will help tackle the global threat of antimicrobial resistance. Since 2020, our teams are fostering data-driven pandemics/epidemics preparedness digital solutions, and since last decade we are working on a data-driven, scalable, sustainable AMR solution, based on open sources components and ready to use for all settings, specially by low-and-middle income countries (LMIC). In LMIC the biggest barrier in AMR is data collection. Testing all samples are very expensive and continous monitoring is out of countries health budgets.

We are applying to The Trinity Challenge because it provides a unique opportunity to address this critical barriers hindering our efforts to leverage data in fighting AMR in Rwanda, which will be an example for other countries. 

By participating in The Trinity Challenge, we will be well connected with other actors, in similar-goal network wherein we can leverage collaborative resources, networks, and expertise to accelerate our progress in combating AMR and improving public health outcomes in Rwanda.

Three key organizations we would like to initiate collaboration on our proposed solution, whic are actively involved in antimicrobial resistance (AMR) initiatives in Africa are:

1. Africa Centres for Disease Control and Prevention (Africa CDC): As the continent's premier public health institution, Africa CDC plays a crucial role in coordinating AMR surveillance and response efforts across Africa. Collaborating with Africa CDC can provide us with access to valuable resources, expertise, and networks to scale our solution. Through Africa CDC, our solution will align with the African Society for Laboratory Medicine (ASLM) that works to strengthen laboratory systems and capacity across Africa, including for AMR surveillance and monitoring.

2. World Health Organization Regional Office for Africa (WHO AFRO) and Rwanda Office: WHO AFRO is actively involved in various initiatives (Antimicrobial stewardship and AWaRe) to address AMR where our solution will have a room to shsow its added value and can be scaled.
3. The Observational Health Data Sciences and Informatics (OHDSI) community which is a multi-stakeholder, interdisciplinary collaborative to bring out the value of health data through large-scale analytics. Is hebind used technology of OMOP CDM with a central coordinating center housed at Columbia University.
4. Happy to get other collaborators from your list.