Skip to content

Strengthening Health Systems Through Improved Data Quality in Botswana

by on November 2, 2012

Guest blog contribution by Amanda Makulec, Monitoring & Evaluation Associate

Information has long been recognized by the World Health Organization and health experts as a key building block within a health system. To function efficiently, a health system requires good data from the local facility level to the national level. When analyzed and used systematically, routine health information can be a powerful tool for smart, evidence-based policy and budget decisions. For those decisions to be truly strategic though, the quality of the data being aggregated through the health information system (HIS) must be high: accurate, timely, and complete.

In Botswana, staff at the Department of Health Policy, Monitoring and Evaluation inside the Ministry of Health (MoH) decided that a system to routinely assess and maintain data quality was necessary.  To support high quality data, the Botswana MoH worked closely with technical experts from MEASURE Evaluation to develop a concrete protocol and series of considerations for M&E officers and others to follow when transmitting, aggregating and analyzing health-related data.

The Botswana MoH identified six key functional areas of data quality to focus on in the assessments, ensuring that the information collected reflects local priority areas. Data verification exercises will also be conducted to determine the accuracy, completeness, and timeliness of the data. Results from the assessments, displayed on simple data dashboards, will be used to generate action plans to improve data quality across the health system. Ultimately, the process aims to improve the quality of data available for budget and program decisions across health program areas.

Following the approach of adapting tried and tested tools, rather than re-inventing the wheel, the team adapted global data quality assessment methodology into national protocols and user-friendly tools, including both standard operating procedures and a country-specific adaptation of the global routine data quality assessment (RDQA) methods. The tools are flexible and can be adapted to assess data quality for any program area, as long as source documents are available at the facility level, and can assess up to four indicators in one assessment.

The Botswana MoH has taken a truly unique and laudable approach to address challenges around data quality by developing this national procedure for routine monitoring of data quality with support from MEASURE Evaluation, and providing specific guidance on developing action plans to address challenges using a bottom-up approach. The protocols decentralize the process of planning targeted activities to improve data quality, allowing facilities and district-level officials to take ownership of data quality in a systematic and structured way, allowing facilities and districts to develop their own recommendations and action items.

In November, MEASURE Evaluation will facilitate the first training on the new procedure for district and national-level M&E officials, with support from the national M&E team. Over the next year and beyond, results of regular system assessments and routine data verification exercises conducted could be analyzed to demonstrate the impact of the standard operating procedures and use of the RDQA process. In due time, the effects of the data quality protocols could be evaluated for their impact on the quality of the data in the health information system, making this data quality work a curiously quantifiable health system improvement.

From → Data, Evaluation

Leave a Reply

Fill in your details below or click an icon to log in:

WordPress.com Logo

You are commenting using your WordPress.com account. Log Out / Change )

Twitter picture

You are commenting using your Twitter account. Log Out / Change )

Facebook photo

You are commenting using your Facebook account. Log Out / Change )

Google+ photo

You are commenting using your Google+ account. Log Out / Change )

Connecting to %s

%d bloggers like this: