Cutting blood shortage and waste through field research

Ethnographic field research across donors, recipients, and blood banks that exposed a predictable supply–demand gap — and a smart-inventory service now approved and in execution by the Nashik city council.

My role

Design Researcher

Timeline

2016 · 10 wk

Org

TCS Digital Impact Square × MIT Media Lab

Methods

Ethnography, shadowing, 100 surveys, service design

The systemic problem

In a city of ~1.4M with only 13 under-equipped blood banks, the gap between supply and demand was structural, not random. I worked in a two-person team and led the design research end to end.

I deliberately scoped from the national problem down to one city, because a life-or-death systems problem can only be fixed where you can actually observe its mechanics.

Why deep field ethnography

Method I chose

Secondary research + ecosystem mapping → shadowing donors and nurses, interviews with 7 blood-bank in-charges, surveys of ~100 residents → empathy & affinity mapping

This is a behavior-and-coordination problem invisible from a desk. Shadowing in donation camps and maternity wards revealed real practice; interviews with in-charges exposed operational constraints; surveys quantified donor barriers. Together they located the gap precisely.

Constraints I balanced: Ten weeks and two people meant I sequenced secondary research first to focus scarce field time, and analyzed across five lenses (process, people, technology, policy, communication) to avoid a narrow fix.

Alternatives I considered

Build a donor-matching app (the obvious answer)

Why not: Existing apps already did this and failed — they served only one link in the chain. Research showed the leverage was in inventory, not matching.

Survey-only

Why not: Would quantify donor reluctance but miss the operational waste (expiry, camp planning) that field observation uncovered.

Key insights

Waste and shortage coexisted — a planning failure

Evidence: Of 152 units collected in one window, 31 expired (35-day shelf life) even amid high demand; camp timing, not donor supply, was the root cause.

Demand was predictable

Evidence: Demand spiked in June and October (monsoon, dengue); collection peaked in winter — a seasonal mismatch a prediction tool could close.

Point solutions fail a connected system

Evidence: Market analysis showed competitors each served one stakeholder; the system needed inventory intelligence linking donors, banks, and hospitals.

What changed

Approved by the Nashik city council and in execution — research translated into a deployed service.

Concept-tested three subsystems; the smart-inventory + camp-prediction tool won on feasibility, impact, and business viability.

Reframed the problem from 'recruit more donors' to 'predict and plan supply' — a higher-leverage bet.

Designed for the real operators (inventory in-charge, camp organizer), not an idealized user.

Reflection & self-critique

What I'd change: I'd build a lightweight pilot of the prediction tool with one bank's historical data to quantify waste reduction before the council review — evidence over projection.

What I'd keep: Resisting the obvious 'donor app.' Letting field research overturn the assumed solution is what made the outcome real.

What I learned: In systems problems, the leverage point is rarely where the pain is loudest — here, it was quiet operational planning, not donor recruitment.