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EDITORIAL
Year : 2016  |  Volume : 60  |  Issue : 1  |  Page : 1-3  

Improving the quality and use of routine health data for decision-making


Indian Institute of Public Health; Public Health Foundation of India, New Delhi, India

Date of Web Publication23-Feb-2016

Correspondence Address:
Sanjay P Zodpey
Indian Institute of Public Health; Public Health Foundation of India, New Delhi
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/0019-557X.177248

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How to cite this article:
Zodpey SP, Negandhi HN. Improving the quality and use of routine health data for decision-making. Indian J Public Health 2016;60:1-3

How to cite this URL:
Zodpey SP, Negandhi HN. Improving the quality and use of routine health data for decision-making. Indian J Public Health [serial online] 2016 [cited 2020 Feb 19];60:1-3. Available from: http://www.ijph.in/text.asp?2016/60/1/1/177248

Health systems are responsible for providing good-quality and easily accessible health care that is responsive to the population's needs. Each building block of the health system [1] interacts with the other building blocks to eventually respond to these population needs. Resilient health systems are a prerequisite for improving the population's health. While there is substantial interest and investment in health programs targeting priority conditions, strengthening of the health system receives considerably lower attention from health planners and policymakers. Health information is considered a national asset, [1] is useful to multiple stakeholders for tracking the health system's performance, [1] and provides sound and reliable information for decision-making across all building blocks. [2] It also provides information on changes in health status over time, which is vital for monitoring the overall objective of health systems. The health information system has four key functions: [2]

  1. Data generation,
  2. Compilation,
  3. Analysis and synthesis,
  4. Communication and use.


Decision-makers are always hard pressed for health information. [3] Frequent laments in policy corridors are "we do not collect this data," "we are not sure whether these data are available," "these data are not reliable," or "these data are not valid." Decisions in such scenarios are ad hoc and/or based on best judgement or the limited data that are available for decision-making. In such a scenario, cross-sectional survey data offer a panacea and are frequently referred and cited while making policy decisions.

Large cross-sectional surveys are costly, not conducted annually, cannot collect data on all parameters of importance, are not good at following trends in real time or over short periods of time, [4] and individual surveys generally cannot provide strong evidence of cause and effect. [4] Cross-sectional data, however well-designed, are not a replacement for good-quality longitudinal data. In the absence of good-quality longitudinal data, cross-sectional data can provide some data to decision-makers for evidence-based programming. Survey data are given a higher importance by decision-makers as they are considered more reliable (than currently available routine data), have a wide consultation before the data collection, are undertaken in "mission-mode," and provide results in a predictable time frame.

The routine health data in India are of perceptibly questionable quality (for several conditions/diseases). The completeness, reliability, and validity of such data are also frequently debated. These are predominantly public sector-driven, with data collectors (health workers) not seeing any value in collecting and reporting this information leading to a casual effort in data collection, are poorly monitored, slow to be transmitted to the higher levels, and rarely available when needed the most. Consequently, the culture of "not relying on routine data" pervades our health systems. We seldom talk about strengthening routine health information systems (RHIS) but rather invest in creating parallel information capturing systems for individual health programs or we go back to relying on survey data.

The predicament as we see it is that "health systems continue to generate poor quality routine data that is seldom used." The cycle is self-perpetuating since infrequent data use further demotivates the data collectors and monitors. Improving the quality and use of routine health data for evidence-based programming, resource allocation, and policy-making is in our opinion extremely vital. We see three potential areas for action.

First, we should address the valid concern of "poor quality data." Data that are invalid, imprecise, or falsely reported should find no place in India's health information system. This will need a relook at our current data collection forms. Global [5] and Indian [6] experiences suggest that although we collect information on several parameters, data from a handful of these parameters are actually used by health workers and district level managers. There is also an excessive duplication in efforts to capture this data. An even smaller proportion of data actually goes above the district level. There is an urgent need to simplify the formats, remove redundancies, and avoid duplication. This work needs to be undertaken by routine health data champions who should consult all stakeholders ranging from the humble data collectors (health workers) all the way to the decision-makers. We can strengthen the data quality by stringent monitoring of data collection and reporting (not just indicators for vertical programs or select key indicators of concern to the state/national government). We will have to systematically encourage capacity-building in assessing and managing our information needs within health systems. Specific information needs of the health system can be met through short-term training and building specific competencies among on-campus degree program graduates.

Second, we need to catalyze a culture shift within the health system where we are constantly encouraged to use our own data for making decisions. There is a considerable merit in longitudinal data that no cross-sectional survey data, of however high a quality, can ever surpass. In such a scenario, the state and district level officials will have to invest time and effort in reexamining the routine data collection and reporting processes within their states.

In order to encourage health systems to generate and use their own data, we see information communication technology (ICT) as an opportunity. ICT services are now cheap, expertise is easily available, and know-how among health system staff is higher today than what it was a decade ago. ICT can facilitate a quicker analysis and a prompt information-transmission for feedback and reporting. Decision aids can be built into such systems, which can help health workers to monitor their populations. In simpler terms, we can recreate "family folders" digitally, and multiply their usage and transmission. On a cautionary note, information technology (IT)-generated data are often mistakenly understood by decision-makers to be a surrogate of "quality data." We need to remind ourselves that IT-generated data too need stringent monitoring. We need to harness this advantage for better communication and usage of data, not as a surrogate for quality data.

Third, this activity needs to be undertaken in a partnership mode that encourages sharing of learning and resources. The strengthening of RHIS needs extensive engagement with a wide range of stakeholders that also includes universities and academic institutions. These two sets of stakeholders in collaboration with the government and health systems can proactively identify RHIS competencies. These competencies should then be translated into learning outcomes that address the needs of the health systems. The strengthening of academic programs can provide the health system with future leaders. This routine information revolution will need active engagement of the state health departments. Professional associations such as the Indian Public Health Association (IPHA) and the Indian Association of Preventive and Social Medicine (IAPSM) can play an important catalytic role in advancing the agenda of RHIS. The associations can provide a platform for discussing RHIS activities in the public health expert community as well as create an RHIS working group that can champion the RHIS revolution within India. The associations can consider offering short-term training in RHIS during their annual conferences. Both the associations publish quarterly journals, which can be used as a medium for capacity-building as well as documenting RHIS success stories.

We will need to identify RHIS champions who can advocate the merits of routine data. Departments of Community Medicine/Preventive and Social Medicine in medical colleges of India can work closely within their served districts by assisting districts to revisit their information needs, audit current health information system within the district, provide technical support toward optimizing the routine data collection and its use, and participate in supportive supervision. In the words of Theo Lippeveld, [7] "…Using information does not stop with problem identification; it involves problem solving and action." We need a systematic effort that is consciously and strategically driven toward strengthening RHIS. This is a necessary step in our progress toward evidence-based programming and decision-making.

 
   References Top

1.
The WHO Health Systems Framework. WPRO. Available from: http://www.wpro.who.int/health_services/health_systems_framework/en/. [Last accessed on 2016 Jan 22].  Back to cited text no. 1
    
2.
WHO. Monitoring the building blocks of health systems: A handbook of indicators and their measurement strategies. WHO. Available from: http://www.who.int/healthinfo/systems/monitoring/en/. [Last accessed on 2016 Jan 22].  Back to cited text no. 2
    
3.
Boerma JT, Stansfield SK. Health statistics now: Are we making the right investments? Lancet 2007;369:779-86.  Back to cited text no. 3
    
4.
LSHTM. Limitations of surveys [Internet]. Available from: http://conflict.lshtm.ac.uk/page_26.htm. [Last accessed on 2016 Jan 22].  Back to cited text no. 4
    
5.
JLN. A Quiet Revolution: Strengthening the Routine Health Information System in Bangladesh. Joint Learning Network. Available from: http://www.jointlearningnetwork.org/resources/a-quiet-revolution-strengthening-the-routine-health-information-system-in-b. [Last accessed on 2016 Jan 22].  Back to cited text no. 5
    
6.
National Health Mission. Health Management Information System. Available from: https://nrhm-mis.nic.in/SitePages/M&E_MajorME_Activities.aspx. [Last accessed on 2016 Jan 22].  Back to cited text no. 6
    
7.
Lippeveld T. The Time is Now for Improving Routine Health Information Systems. Available from: http://thepump.jsi.com/the-time-is-now-for-improving-routine-institution-based-health-information-systems/. [Last accessed on 2016 Jan 22].  Back to cited text no. 7
    




 

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