Opinion: Ashok Alexander
It is possible to address the micro-data gap in healthcare delivery with creativity and diligence 
Thursday, September 28, 2017

One of the biggest problems we face in public health is micro-data. By micro-data, I mean data collected and used by frontline health workers. There are three categories of Front Line Workers (FLW):Anganwadi Worker (AWW), who is responsible for nutrition and health monitoring of young children and their mothers; Accredited Social

Health Activist (ASHA), who mobilises the community to participate in healthcare programs and Auxiliary Nurse Midwife (ANM), who provides basic health diagnosis, treatment and referrals.

Data collected by FLWs is arguably more important than, say, data on facilities or data higher up the health system because it is the last mile, i.e., it’s where the rubber hits the road. If this data doesn’t have integrity, it weakens the whole system.

Just like a pharma salesman would need to know the number of doctors in an area, medicines that the doctor wants to stock, the doctor’s knowledge about the medicines, the number of patients and changing market conditions, each frontline worker has specific data needs. She should have data on pregnant-lactating women, high-risk pregnancies, children and the stateof their nourishment / malnourishment.

This is not an infinite amount of data.If you take a village of 1000, FLWs deal with around 20-30 pregnant women and 25-30 infants per year. Yet, data is often missing. For instance, ASHA workers visit ten houses each day, following a serial number sequence (1-10, 11-20 and so on). If a severely malnourished child is in house 58, she’ll get to the child’s house rather late due to the lack of actionable data. This may be attributed to arcane data collection methods. 

Secondly, even if FLWs have data, they do not share among themselves, despite complementary needs. For instance, the AWW takes the weight of kids attending classes at the Anganwadi each day. She may mark a child as malnourished based on low body weight. The ASHA then uses MUAC (Mid-Upper Arm Circumference) measurement to determine whether the child is malnourished. So, we have a situation where two workers have different assessments of whether a child is malnourished, though ideally, both measures must be taken into account. Finally, the ANM, the captain of the team, is meant to have this information to prioritise treatment of the child in question.

Further, for this granular data to have integrity, it must also be recorded well. Right now, FLWs across India have numerous thick registers to record information. In fact, each Anganwadi worker used to maintain 17 registers until recently. It’s down to 11 now. ANMs maintain different sets for each village under their jurisdiction. Within these registers, data is kept in a haphazard manner. A malnourished child’s data appears in seven different places in registers, on occasion. All of this raises questions of data integrity. 

Now, there is some good news. In our program, Akshada, (The Antara Foundation operates this program in partnership with the Government of Rajasthan and Tata Trusts), we began by simplifying the ANM’s service delivery register.This was adopted by the Government of Rajasthan for use throughout the state.It wasn’t terribly complicated – we simply eliminated duplication and had more logical formats –but was begging to be done.

The second step is more interesting. The three FLWs get together to create a map of each village. At least in Northern India, villages are not mapped. After creating the map, they use bindis to mark out where each of the needy cases are. For example, if you go back to that malnourished kid in house #58, a yellow bindi may be used to indicate that the child is malnourished.That visual representation is then placed into a household visit calendar that ASHAs use to plan their visits. Based on this, the ASHA is likely to visit this house in the first and not the sixth week. 

Now, things are getting even more interesting. We’ve developed a three-way integrated AAA app (I4A) which links the three frontline workers, enabling seamless sharing of data.This, in turn, strengthens data integrity while making frontline workers’ lives easier. 

The implicit lesson here is that innovation in public health is an evolutionary process: a second generation solution building on the first, third on the second etc., and a solution thus evolved, would likely be better than a brand new product since it is adapted by change. 

To sum up, invaluable micro-data is not being collected, recorded or shared effectively. Aggregates of these become India’s health statistics and ought to be a cause of grave concern. There are no processes in place for using data as a problem-solving aid in rural health delivery. The good news is that it can be addressed with some creativity, more diligence and lots of luck.


The writer is the founder-director, Antara Foundation, a non-profit which designs and implements innovative solutions in healthcare delievery at the grassroots.  As India country office head for the Bill and Melinda Gates Foundation, he led Avahan, one of the world’s largest HIV prevention programs.


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