Assessment of Constraints to Data Use
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Assessment of Constraints to Data Use is a rapid assessment tool designed to identify barriers and constraints that inhibit effective practices in data use. http://www.cpc.unc.edu/measure/publications/ms-11-46-a
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Assessment of Constraints to Data Use
- 1. Data Demand & Use: Assessment of Data Use Constraints Webinar Series - #1 Tuesday, January 17, 2012 Presenters: Molly Cannon and Tara Nutley
- 3. Why improve data-informed decision making? Pressing need to develop health policies, strategies, and interventions
- 4. “… 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.” National-level Policymaker, Nigeria
- 6. Data-informed Decision Making Cycle
- 7. Improving Data-informed Decision Making
- 9. ASSESSMENT OF DATA USE CONSTRAINTS
- 10. What determines Data Demand & Use? * Based on PRISM analytical framework (LaFond, Fields et al. (2005). The PRISM: An analytical framework for understanding performance of health information systems in developing countries. MEASURE Evaluation). ORGANIZATIONAL TECHNICAL BEHAVIORAL
- 11. What determines Data Demand & Use? POLITICS CULTURE SOCIETY * Based on PRISM analytical framework (LaFond, Fields et al. 2005 The PRISM: An Analytical Framework for Understanding Performance of Health Information Systems in Developing Countries. MEASURE Evaluation). ORGANIZATIONAL TECHNICAL BEHAVIORAL
- 18. Assessment of data use constraints tool Technical Constraints Technical constraints are related to the ability to generate high-quality data and analyses. RA8 Have you ever had an experience while making a policy or program-related decision when you were concerned about the quality of the information being used? RA9 Are there multiple sources of information or statistics for issues of importance to you, and have you experienced any problems caused by having different estimates? RA10 I am interested in knowing about technical capacity for collecting and using information. Does your agency have the technical capacity to produce reliable information without a lot of external technical assistance? RA11 Does your agency have the technical capacity to ensure access to and availability of reliable data? RA12 Has there been an occasion when data quality or local technical capacity made it difficult for you to use information in making a decision? RA13 How would you have gone about preventing this situation?
- 19. Assessment of data use constraints tool Individual Constraints Individual constraints are related to the skills, attitudes, values, and motivation of individuals . RA14 What specific challenges have you experienced among your staff when it comes to using data? Probe respondent for the following items following their response: awareness of data sources, technical skill, motivation, time and workload, lack of incentives or knowledge of the benefit to using data for policy change and program management.
- 20. Assessment of data use constraints tool Organizational Constraints Organizational constraints are related to challenges in using information that are due to how your organization functions . RA 15 How does your organization support having the necessary information to make decisions? RA 16 How does your organization support the prioritization and use of information in decision making? RA 17 How does your organization support training of staff in skills for using information in decision making? RA 20 What are the challenges your organization/agency experiences in sharing survey and research data? RA 22 Are there risks associated with sharing information? If so what are they? Record the response and the respondent’s openness or reluctance to answering this question.
- 23. Sections of Key Informant Interview Guide Data Users Data Producers Logistics/Background Logistics/Background Information Use for Decision Making Data Information and Flow Technical Barriers to Information Use Data Utilization Organizational Barriers to Information Use Barriers to Data Use Other Barriers to Information Use Other Barriers to Data Use
- 24. Planning Matrix for addressing barriers to data use # Barrier Proposed Intervention Steps Involved Person Responsible Other Stake-holders Gen. Time-line
Notas del editor
- When welcoming folks, thank them for volunteering for the webinar. Introduce presenters and participants
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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).
- 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.
- 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.
- 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”
- 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.
- 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.
- 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.