Meet Niti, a frontline worker for a grassroots NGO focused on improving health outcomes in India. During the Village Health, Sanitation, and Nutrition Day (VHSND) event, she helps ensure essential services like antenatal check-ups and immunizations are delivered efficiently by Community Health Workers. To support her, she carries a checklist designed to ensure no important task is overlooked.
In theory, Niti’s checklist should be her ally—a tool that helps her stay organized and thorough. But over time, this simple checklist has morphed into something else: a data collection mechanism, burdening her with administrative tasks and pulling her away from her primary responsibility of delivering care. What was once a guide has become an obstacle.
This story is not unique to Niti. Across the development sector, checklists, originally meant to support workers, are increasingly being repurposed into tools of excessive data extraction, undermining their original intent. This article explores how checklists, originally designed to support workers, are increasingly burdened with data collection requirements, ultimately undermining their effectiveness.
What is a Checklist?
Oxford Learners’ Dictionary defines a checklist as “a list of the things that you must remember to do, to take with you, or to find out.”
The idea of checklists as essential tools gained prominence in 1935 , when a Boeing test flight of the Model 299 crashed due to human error—the crew had forgotten a crucial step in the plane’s operation. To address this, Boeing introduced the first-ever checklist. It helped pilots manage complex tasks without missing key steps, and this simple innovation enabled the successful use of the Model 299 in aviation.
Since then, checklists have found their place across industries, from aviation to healthcare, serving as critical tools to prevent errors and ensure key steps are completed efficiently. But over the years, in the development sector, checklists have begun to serve a dual purpose—not only as tools for action but as instruments of data collection. The problem arises when this second role overwhelms the first. To explore the effectiveness of checklists and their intended function, I recommend reading Atul Gawande’s The Checklist Manifesto: How to get things right.
The Problem: Data extraction overload
While the definition above does not explicitly state “collect data”, “analyse trends”, checklists in the development sector are increasingly being overloaded with demands for data collection, often at the expense of service quality. This shift leads to several critical issues:
- Unnecessary data collection: Thousands of field staff, like Niti, spend more time entering data than delivering/ improving services. Much of this data has little relevance at the local level and is collected primarily for distant reporting.
- Impact on service quality: As checklists become tools for data extraction, the quality of interactions between field staff and program’s beneficiaries suffers. Instead of being a quick reference guide, the checklist becomes a distraction, pulling attention away from delivering interventions.
- Demotivation and errors: The complexity of data-heavy checklists increases the risk of errors. Frontline staff, feeling pressured to complete forms, rush through them, leading to inaccurate data and feelings of demotivation (I have seen checklists which require you to record over 200 data points!).
- The “Big Brother” effect: Field staff often feel under constant surveillance when checklists are used primarily for data monitoring. This sense of constant oversight reduces motivation leading to compliance for the sake of compliance, rather than genuine engagement with their tasks.
- Disconnect between the prescribed Checklist and practical needs: Many field staff keep separate records of what they truly need for their tasks —revealing the growing gap between what is asked for and what is needed.
Why is this happening?
Several factors contribute to this ‘abuse’ of checklists:
- Lack of clarity on outcomes: Unclear links between program interventions and their impact often result in collecting every possible data point, skewing analysis and burdening staff with unnecessary work.
- Pressure from within and outside the organization: While funders demand data to justify investments, they are often unwilling to support dedicated monitoring/ evaluation workstreams. NGOs then rely on program staff to double as data collectors, stretching them thin. Even within the organization, multiple stakeholders (skewed towards the leadership), feel the need to regularly collect hundreds of data points, often without a clear analysis and reflection plan in place.
- Unintended impact of digitalization: Digital checklists, while more convenient, have turned checklists into expansive data-gathering tools, making frontline staff feel more like data clerks than service providers.
A call for intentional design
Systemically, doubling up checklists as data collection tools reflects the extractive mentality that the development sector often suffers from. This mindset—” Go into the community, gather data points, aggregate them, and another team sitting in HQ will look for patterns”—comes at the cost of sensitivity to the original cause. While evidence collection is critical, it should not undermine program implementation. I have encountered instances where individuals spend over half their time focused on the questions on their mobile screens rather than interacting with the communities they aim to serve. And there are multiple layers of this extractive nature, community to the frontline staff, from frontline staff to the management, from management to the leadership.
Checklists should support service delivery, not overwhelm it. To restore their original purpose, organizations must focus on intentional design, ensuring that checklists remain tools for guidance rather than data extraction. Their primary goal should be to help staff members like Niti perform tasks efficiently without drowning them in data demands. If data collection is necessary, it should be a separate, thoughtfully designed process that minimizes the burden on field staff.
Here are some questions to guide this intentional design across four levels:
- Assessing the primary need for checklists (service delivery):
o Does the task genuinely require a checklist? Is the checklist “enabling” better program delivery by making it more efficient, planned, and less prone to error?
o Has the frontline staff expressed a need for a tool to help them plan and deliver tasks more efficiently? Are they already using informal checklists which need to get institutionalised?
- Assessing the secondary need for checklists (as data collection tools):
o Are there existing mechanisms (primary or secondary) that can support data collection to assess impact without burdening frontline staff? Is there capacity in the organization to handle data collection separately?
o If resources are limited, can the organization create intentional spaces for periodic data collection—such as dedicating one week every three months for observation and reflection, rather than ongoing data extraction during program implementation?
- Balancing the primary and secondary needs:
o How intensive is the data collection process? As a rule of thumb, it should not take more than 20% of the worker’s time in the community.
o Is data collection compromising the quality-of-intervention delivery? Are frontline staff feeling that they are data collectors and not implementers?
o Is this data collection introducing bias in how they perceive/ improve the delivery of the intervention
- Giving it back: creating analysis and reflection spaces:
o Are key stakeholders (field staff, program heads, donors, or government entities) regularly looking at the collected data points? Are they able to draw meaningful conclusions from the emerging patterns? Are those patterns leading to actionable insights leading to improved program design/ implementation approaches?
o Does the community ever look at this data? Do they have a point of view of the emerging patterns?
Reimagining the role of checklists will require effort from both within and outside social impact organizations. Internally, organizations must clarify their data collection goals, ensuring only essential information is gathered. Shifting to periodic data collection, rather than extracting information daily through checklists, could significantly reduce strain and allow field staff to focus on implementation. Externally, key stakeholders such as funders must also play their part by supporting dedicated data collection efforts, rather than relying on overworked field staff to do both service delivery and data reporting.
“You either die a hero, or you live long enough to see yourself become the villain.” What was once a tool for good has, in many cases, become a burden. It’s time to reclaim checklists for their original purpose: to empower frontline staff, not inhibit them.
Let checklists be checklists!
Keshav Sahani leads digital health at The Antara Foundation, using tech and systems thinking to improve health delivery in rural and underserved communities.
Antara Foundation March 2022