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One thing that should be noted regarding Domain 3 – External Consistency. Comparisons with population based surveys can be tricky – you have to ensure that the data are truly comparable. Often, surveys take a couple of years to compile and publish so the values you are using are already quite dated relative to the routine data. Also, population-based surveys cover the whole population, while routine values from HMIS only cover the people who avail themselves of facilities that report to the HMIS (which often exclude many private health facilities) – not the whole population. In addition, survey aggregation units may not be the same as administrative units used to aggregate the routine data. All these factors should be considered when comparing the routine values to population-based survey values.
Anyone have experience conducting such comparisons and best practices they can share to facilitate them?
Dave
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One thing we have noticed in 10+ years doing data quality assessments is that identifying data quality problems is not sufficient to improve data quality. The work of improving data quality can be tedious and expensive, and as a result data quality improvement plans are sometimes not implemented. In the DQR we have tried to link the process with health sector planning so that identified problems can be discussed in planning events, and solutions can be budgeted for when resources are allocated. This step is critical for the success of the plan, since without funding and a responsible unit/organization, the plan will likely not be implemented. (see below a graphic that shows how the DQR can fit into the health sector planning cycle.) Anyone else have experience trying to get a data quality improvement plan adopted and funded in their country?
Dave
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I like to think of Enterprise Architecture as a blueprint for the health information system. You wouldn’t build your house without a blueprint, and you shouldn’t build your HIS without one either. I must confess that I find some of the language around EA to be opaque. I took and would recommend a course on The Open Group Architecture Framework (TOGAF) which really helped me to understand EA better.
To me, “Enterprise” just means that it’s holistic, you are talking about all the health information systems and data and the vision is big enough to accommodate the scale you want to achieve, i.e. the whole country with room to grow.
“Architecture” just means how it is put together. So the “Enterprise Architecture” is the blueprint, or plan, for putting together the health information system that considers how the data are generated (the business processes), where it needs to end up, who the users are and their data needs, and how it all should fit together rationally. It should maximize efficiency and accommodate growth (scale-ability).
Dave
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One problem of MFLs that needs to be emphasized (and I believe was touched upon in the opening presentation) is the need for clear ownership and management of the MFL. In one country I recently visited there was at least four versions of the MLF circulating, and all different. Which one to use for selecting health facilities for an assessment? These, of course, were not “Master” facility lists, but rather just “facility lists”. The ownership is what makes the list “Master”, that is, one authority to update and maintain the official country facility list. And standard mechanisms for updating them (e.g. an NGO does a facility survey and finds status changes in existing facilities, or new facilities have come online, or old ones have disappeared) and submits the new information to the MOH. Then the MOH validates the information by reaching out to the facilities or the DHMT in the respective districts. Only when the information is validated is the MFL updated. The updates should happen on a regular, predictable schedule.
Dave
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Agree with James – we are putting the cart before the horse when supporting wide-spread EMRs in places without the infrastructure to sustain them. And diverting precious resources that could be used to strengthen and integrate aggregate data systems!
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While development partners have played a roll in fragmentation by setting up program or disease specific information systems, there is increasing recognition of the problem internationally, and some notable development partners (e.g. Health Data Collaborative, WHO, the Global Fund, USAID, etc.) are taking pains to align with country systems.
The Health Data Collaborative (HDC) is a joint effort by countries, development
partners, civil society and academia to strengthen national health information
systems, improve the quality of health data and track progress towards the health related
Sustainable Development Goals. Arising from the call to action at the Measurement and Accountability for Health (MA4H) conference (2015), the HDC aims to (among other things),
“strengthen national and subnational systems for integrated monitoring of health programmes and performance. By helping countries collect, analyse and use timely and accurate data, the Health Data Collaborative will contribute to the goal of data-driven performance and accountability.”
(see the HDC factsheet here: https://drive.google.com/open?id=0B0kN6Fs9MipFdjN0OGZFN2Jzdkk)From the Global Fund’s policy paper “Supporting Countries to Build Resilient and Sustainable
Systems for Health – The Role of the Global Fund” (2015), “Integrating multiple data collection systems into one single national health management information system can improve decision-making and accountability, from individual health care workers in the community to sub-national, national, regional and global policy makers.”
(http://theglobalfund.org/documents/publications/other/Publication_RSSH_FocusOn_en/)From our work in the USAID MEASURE Evaluation Project we know that USAID is also promoting integrated national RHIS and/or interoperability to achieve de facto integration. In MEASURE Evaluation Phase III – at the request of USAID – we published a white paper on RHIS Data Management Standards which speaks to best practices for RHIS along 4 domains;
1. Users’ Data and Decision Support Needs
2. Data Collection Processing, Analysis and Dissemination of Information
3. Data Integration and Interoperability
4. Governance of RHIS Data Management
(http://www.cpc.unc.edu/measure/resources/publications/ms-15-99)Also in MEval Phase III we published a paper on standards and best practices for integration of HIV/AIDS information systems into RHIS (as a follow-up to the chapter on Integration and Interoperability in the RHIS Data Management Standards document).(http://www.cpc.unc.edu/measure/resources/publications/ms-15-103)
So, while international development partners have certainly played a role in the fragmentation of information systems in countries, they seem to have recognized their role and are now taking steps to bring these systems back into the fold.
Enjoying reading the contributions from all participants. Keep them coming!
Dave
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