The Indian Healthcare System

The healthcare system in India is structurally a complex hierarchy of different government and private agencies at centre, state, district and village levels.  Given India’s poor global standing in public healthcare and the nature of bureaucratic information flows, data-driven decision making is being pitched as a pressing need for this sector. Particularly in rural areas where last-mile connectivity is a major problem, accurate and extensive data on grassroots level performance of healthcare programs is gaining emphasis.

To get a grounded perspective on how data collection and decision making works for health care policy, we spoke to Mr Thirumalai, who is a Monitoring and Evaluation Specialist at the India Health Action Trust (IHAT).

healthcare, data, health care policy

Introduction To UPTSU

The Uttar Pradesh Technical Support Unit (UPTSU), operated by the India Health Action Trust (IHAT), is a first-of-its-kind collaborative setup of the UP state government and Bill & Melinda Gates Foundation. The IHAT team is working in 100 high priority blocks in 25 districts of UP with a mandate to reduce the Infant Mortality Rate (IMR) and Maternal Mortality Ratio (MMR) in rural areas. They are supporting the state government at various levels – from the directorate and chief medical officers to Auxiliary Nurse Midwives (ANMs) and nurses in the villages. They carry out data-intensive operations to assist the government in identifying health care policy issues in the field sites and suggest solutions.

How Do They Collect Data?

  • Government Systems – Health Management Information System (HMIS) and Mother and Child Tracking System (MCTS)
  • Regular Monthly Program Monitoring
  • Need-Based Surveys
    • Village Level Surveys: To capture the local health behaviour
    • Facility Level Surveys: To collect clinical information on implementation of protocols on infection prevention, biomedical waste, etc.

While the monitoring data is directly collected and analysed in their internal MIS, nearly ninety percent of surveys are conducted using mobile data collection tools.

How Do They Use This Data?

The data analysis is carried out with an objective to identify action points for improving health services at block, village and facility levels. For example, if the survey data shows a particular health service like surgery is not being delivered in a facility, there can only be a few possible reasons for it – lack of skills, absence of a specialist and/or infrastructure reasons. They use this information to improve planning and resource allocation.

In terms of driving decision making and policy reforms, their experience suggests that data seems to acquire very different meanings in collection, analysis and policy making phases. Once they come up the health indicators, they have to fine tune and pitch their insights and recommendations well to convince people at different levels of the government to actually drive decision making. They also have to create detailed documentation of the survey process to convince policy makers of the authenticity of the data.

While this model may work for a firefighting mode of problem solving, it falls short of creating an ecosystem where data and policy are part of an integrated framework. At a different level, they understand that this problem calls for innovative storytelling to connect the dots between grassroots data and making health care policy.

Mobile vs. Paper Data Collection

Though it’s still at a nascent stage, their experience of moving from paper to mobile based surveys has been full of learnings. There have been distinct advantages in terms of logistics and time. Data entry is an extremely cumbersome process, and to be able to get rid of that all together has been a significant value add for them. Time taken for transmission of information from field to their computers has been drastically cut. Additionally, digital tools have offered flexibility to make last-minute edits to the questionnaire, which was not possible with the paper-based surveys.

mobile, mobile for good, data collection, save money

Challenges of Implementing Mobile-Based Data Collection Tools

The transition from paper to mobile came with challenges such as network connectivity, battery life, and most importantly the hired surveyors’ lack of training for using these tools. Thus, it’s extremely important that technology is built keeping in mind the conditions on ground. (Read about how SocialCops accounted for the challenges of rural India here.)

In the analysis phase, they face challenges in cleaning the data and arriving at meaningful denominators to compute health indicators. They hope to streamline data collection and analysis processes over time. However, they realize that technology alone cannot address complex policy issues.

Data and Policy: The Big Picture

At a deeper level, this narrative can be read as a reminder of the fact that the data-silos style of classificatory imaginaries have been features and not bugs of modern bureaucracies[i]. Information and data collection systems historically have played crucial roles in shaping the structure of policy making and governance[ii]. So if we integrate the data silos, it will not just add efficiency to the present decision making systems. Rather, it’ll reconfigure them. In other words, a digitally streamlined survey system in the long run can lead to a new policy system. And thus the UPTSU model holds interesting insights for both short and long term potentialities.

This is a part of our ‘Data Ecosystem’ series, an effort to highlight organizations & non-profits leading the curve of evolution towards data-driven decision making.

Catch the full series here:

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[i] See, for reference:
– Bowker, G. C. (2000). Sorting Things Out – Classification & its Consequences. Cambridge, Mass.: MIT Press.
– Hull, M. (2012). Government of Paper – The Materiality of Bureaucracy in Urban Pakistan. Berkeley: University of California Press.

[ii] See, Bayly, C. A. (1999). Empire and information: intelligence gathering and social communication in India, 1780 – 1870. Cambridge.: Cambridge Univ. Press.