Assessment of Constraints to Data Use

Editor's Notes

• #3: When welcoming folks, thank them for volunteering for the webinar. Introduce presenters and participants
• #4: Global health context- The need for quality health care services is intimately known by all of us. Global HIV epidemic. There were an estimated 33 million people living with HIV at the close of 2008, the majority of whom either need or will soon need treatment. Approximately, one-third of the world‘s population is infected with TB.. Each year, malaria causes nearly 1 million deaths, mostly among children under 5 years of age the health system is burdened by millions of clinical cases as well. In much of sub-Saharan Africa, the transition from high to low fertility has stalled. Also, young people—those below the age of 20—account for the largest proportion of the population. In the next few years, we will see larger numbers of people needing health services as this cohort ages. In the face of this demand we are experiencing Inadequate numbers and poor distribution of qualified health workers and an inadequate human resources system to support them. It is within this context of a high disease burden, a growing population, and insufficient health services, that it becomes extremely important for governments to make the best use of their limited resources. The need to develop strategies, policies, and interventions that are based on quality data and information is urgent.
• #5: The importance of data-informed decision making is expressed on this slide by a national-level policymaker in Nigeria who participated in a data use assessment conducted by MEASURE Evaluation. The assessment involved interviews with a range of professionals at the national, regional, and facility levels. The policymaker interviewed, stated… (READ SLIDE) “… without information, things are done arbitrarily and one becomes unsure of whether a policy or program will fail or succeed. If we allow our policies to be guided by empirical facts and data, there will be a noticeable change in the impact of what we do.” This statement nicely summarizes why we are here today to discuss the importance of improving data-informed decision making.
• #7: The framework presented here illustrates the entire cycle of evidence-based decision making, starting with basic M&E systems and the collection of information - through to the use of data and continued demand for data to repeat the cycle. This approach illustrates the ideal. You will note that in addition to the collection of quality data there are also the considerations of ensuring that the information is available and in a format that is easily understood by relevant stakeholders. This information is then interpreted and used to improve policies and programs The cycle supports the assumption that the more positive experiences a decision maker has in using information to support a decision, the stronger the commitment will be to improving data collection systems and continuing to use the information they generate. This leads to repeat data use. You will note that this cycle is supported by coordination and collaboration. This coordination is among data users and data producers as well as between management systems and other organizational supports that facilitate and support data informed decision making. Lastly, the cycle is supported by CB to ensure that individuals are equipped with the skills to collect and use data. It is important to note that there are many opportunities for this process to break down. In the best designed M&E systems you often find lacklustre data use. Data is not being used as often as it should be.
• #8: How do we improve DDU? Firstly, build upon a commitment and ongoing efforts to improve M&E and information systems – this is the foundation of all data use improvement interventions. Identify and engaging data users and data producers is also critical. By data users we are referring to those whose primary function is to manage data systems and by data users we are referring to those whose primary function is to use data to monitor and improve health service delivery. These two groups don’t always work closely together. For data use to function as we saw on the previous slide, regular collaboration between these two groups is critical. It is also important to apply tools, build capacity and strengthen organizational systems to support data informed decision making. In this webinar series we will be discussing tool application (the pink box) and the types of tools MEASURE Evaluation has developed to facilitate DDU. The last webinar session of this series will address capacity building and at a later date we will offer a webinar on strengthening organizational supports to improve data demand and sue. The combination of tool application, capacity building and strengthening organizations are all complimentary and necessary elements of any strategy to improve the use of data in decision making.
• #11: There are many factors that affect data use. Let’s consider why this happens. Here you see the three main determinants of a Routine Health Information System, including data use. We define ‘determinant’ as a determining or causal element or factor directly linked to data use. The three determinants highlighted are—Organizational, Technical, and Behavioral. Organizational determinants—these determinants relate to the organizational context that supports data collection, availability, and use, such as the identified procedures and the roles and responsibilities of those that collect, analyze, disseminate, and use data. Technical determinants—refer to the technical aspects of data collection processes and tools, such as the data collection processes, methods, and forms. Last, Behavioral determinants refer to the behavior of individuals who produce and use data. This would cover their skills, attitudes, values, and motivation.
• #12: In addition to organizational, technical, and behavioral determinants, we also need to consider that the political, cultural, and social contexts are important determinants of data demand and information use, because decision making, sharing of information, and data collection and reporting all occur within these contexts. It is important to address all of these areas when developing a strategy to improve data use. A full assessment of the routine health information system can be conducted to identify strengths and weaknesses in all of these areas, using the PRISM assessment developed by MEASURE Evaluation. However, we are going to present a rapid assessment that can be conducted. A future webinar will cover how to use PRISM.
• #13: The Assessment of Data Use Constraints is a tool developed by MEASURE Evaluation for rapid assessments; it assists users in improving understanding of the demand for data and the constraints on data use. These rapid assessment tools are based on the PRISM framework, there are specific tools that comprehensively assess a RHIS but this is designed to be a rapid, snapshot way of assessing the three potential groups of constraints. Specifically, it: Identifies existing barriers and constraints on data use. Identifies existing best practices in data use so these practices can be applied elsewhere. Formal planning should follow-up the mapping process. The information generated by this tool should be far more than a list of barriers and constraints. It should be forward-looking and prescriptive, showing ways that these obstacles and deficiencies can be overcome. These are areas that can usually be addressed with targeted interventions. The assessment is conducted by interviewing key informants at various levels of the health system. The assessment also can be used to examine processes within a facility or single organization and incorporated into health information and organizational capacity-building assessments at the national and subnational levels. The interview guide is organized by the three determinants of data use (as discussed in the previous slides).
• #14: As previously mentioned, the assessment of data use constraints is a series of questions asked of key informants. Depending on your needs, you can ask the questions to different types of key informants.
• #19: On this slide, you see an example of what the assessment tool looks like. As you can see, these questions are intended to identify technical constraints. The endemic shortage of computers is an obvious technical constraint, but there are other common technical issues that erode data quality. For instance, contributors could be defining health indicators differently, or using different sources for the same data element or indicator, or using different algorithms to report it.
• #20: Many information systems suffer from shortages of skilled people to manage, interpret, and use the data; and motivation and incentive to generate high quality data. For example, one health information unit, despite having an M&E system for HIV/AIDS, was still not getting the data it had requested from its service sites. Decision-maker attitudes Staff motivation Lack of “data culture”
• #21: Organizational processes might not support the use of data. For instance, officials might be reluctant to use data that has not been officially sanctioned. Perhaps the release of certain sensitive information—such as figures that reveal a measles outbreak—is tightly controlled. This information can be shared only by official protocol. More often, there are simply no channels or systematic processes to share data with people who could use it.
• #25: This is an excellent management tool – document so you are able to monitor progress, keep people on same page with common direction to create continued culture of data use. If you use this in your organization, you can also add another column for a measureable indicator so you can track progress over time.
• #26: This is an excellent management tool – document so you are able to monitor progress, keep people on same page with common direction to create continued culture of data use.