Murielle Ettiene, DCDJ Data Fellow

Increasing Data Quality at the Clinic to National Level in Côte d’Ivoire

January 19, 2021 DCDJ
Lindsey Fincham
Program

Murielle Ettiene, a DCDJ Data Fellow was placed with the National AIDS Control Program, where she improved data quality through clinic-level trainings.

Background

The National AIDS Control Program (Plan National de la Lutte Contre le SIDA/VIH, PNLS) coordinates the national response to the HIV/AIDS crisis in Côte d’Ivoire. PNLS is responsible for collecting and reporting logistical and clinical data from 33 regions and 113 districts, as well as creating and executing the National Strategic Plan (Plan National du VIH/SIDA, PNS) to fight HIV/AIDS, which is the national roadmap for the next five years of Côte d’Ivoire’s epidemic response.

Des Chiffres et Des Jeunes (DCDJ) aims to bolster the subnational supply and usage of data for citizens of Côte d’Ivoire, engage youth as champions of these services, and fuel innovation to address rising data and information needs. Through the DCDJ Fellowship Program, Ivorians between 18 and 34 years old spend two months in intensive data science and analytics training. Following the training, Fellows are placed in internships to support their hosting organizations in making data-informed decisions. DCDJ (Des Chiffres et Des Jeunes) is a project of the Data Collaboratives for Local Impact (DCLI) program, a partnership between PEPFAR and the Millennium Challenge Corporation (MCC) that aims to increase expertise and resource availability in Côte d’Ivoire.

Before applying to become a DCDJ Data Fellow, Murielle Ettiene had earned a BA in Economics and Statistics and was preparing to start a Master’s Degree in Finance and Banking. She already had experience working in a financial institution, and hoped that the program would help her towards a career as a Financial Analyst. After being accepted into the program and completed her training, Murielle was placed at PNLS. She worked with both the Monitoring and Evaluation Unit and the Care and Treatment Department.

Problem

While working in both departments, Murielle saw data quality to be the biggest challenge, specifically in evaluating the systematic care of HIV/AIDS patients. She realized that the mistakes came from detail processing at the facility level where doctors, social workers, and other clinic staff record data. After investigating and visiting the clinics, Murielle understood that the real problem was a lack of training. 

Solution

To support better data quality and more effective data use for decision-making, Murielle developed a training for staff in her department at PNLS, focused on using Excel and PowerPoint. After the initial training at PNLS, she set her sites on the deeper problem – poor quality data coming from the HIV/AIDS clinics and care sites. After completing the DCDJ Fellowship, she continued working with PNLS as a volunteer. It was at this point that Murielle started working directly with the clinics and care sites, providing similar training with a focus on data quality. 

Outcomes and Impact

The data collected by each clinic is reported to PNLS, who uses it to inform budgets, programs, and long term planning. With Murielle’s help, 10 individuals were trained in computer literacy and data quality at PNLS. Additionally, she contributed to the evaluation of the quality of services in 13 health centers for the management of HIV/AIDS, and to the Data Quality Assessment and subsequent training at 12 sites at the national and sub-national levels.

Ultimately, the training resulted in a higher quality of data coming from the clinics and a better quality control system at PNLS. Now, people who were not previously involved in data processing and data management – due to a previous lack of training – can contribute to the process. With data quality control managed closer to data reporting, identifying and cleaning errors takes less time, which means that processing at the national level is a smoother process.

Murielle was most focused on data related to systematic care. Systematic care data collected by PNLS is used to understand how and where interventions are working, and provides the evidence needed to update or change policies where interventions are not working.

Conclusion

Murielle was in a unique position as a Fellow at PNLS. Because her role was to evaluate and improve the data ecosystem, she had the flexibility to provide facility-level training. After seeing changes in data quality at the facility level, Murielle said, “If the data at the bottom of the ladder is broken, it will impact the results at the top” adding the insight about her work, “through each line of data, you see the lives of the people you are impacting.”

Also, because women in data science are somewhat rare in Cote d’Ivoire, Murielle had not originally considered a career in this field. The Fellowship opened her eyes to that potential, especially a career in monitoring & evaluation.

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