Bulk Sample Transfer
Visually guided sample transfer tool for lab technicians performing pipetting operations
Challenge
During the end-to-end sample workflow, a batch of samples must be transferred manually from one container to another, and the lab operator manually tracks the source and target container positions with the help of spreadsheets and physical notes. This may result in human errors like pipetting patient samples in wrong target wells (positions).
Solution
A feature within the application that guides the user visually to transfer samples from one or more source containers to target containers was developed. This completely replaced spreadsheet tracking, and supported users to choose the pipetting method and recommended target positions.
Project goal
At a certain point in the sample sequencing workflow, patients' samples need to be manually pipetted from one output container to another input container to proceed further. For this, the users organize the source containers in a rack and write down the sample ID/ source position in the target container position to ensure that the intended samples from the source reach the designated position in the target container. This manual method creates a high risk of human error and a big cognitive load on the user. The goal was to provide a solution for sample transfer through the application that should guide the user and handhold them throughout the process which could replace the planning and execution of the manual pipetting process.
Research & Discovery
We identified a set of users from the lab to understand their end-to-end workflow during a sample transfer process. This helped us visualize their lab environment, the setup they use for sample transfer, the tools, common practices, and the challenges they face during this process.
What we learned
Decoding user interviews, two critical insights emerged that shaped every design decision that followed.
The user's mental model
Lab technicians always organize their physical workspace with source containers on the left and target containers on the right — transferring samples left to right. Any digital solution that breaks this spatial model would fight their muscle memory and increase errors.
User needs and challenges
The interviews revealed that the biggest pain point wasn't the pipetting itself — it was the cognitive overhead of tracking which sample goes where. Users relied on handwritten notes, printed spreadsheets, and physical markers to keep track, creating multiple points of failure.
I keep a printed sheet next to me and cross off each sample after I pipette it. If I get interrupted, I sometimes lose track and have to start verifying from the beginning.
Before vs. After
- ✗ Handwritten notes & printed spreadsheets
- ✗ No real-time tracking of transfer progress
- ✗ High risk of pipetting into wrong wells
- ✗ Interrupted workflows require re-verification
- ✓ On-screen visual guidance for each step
- ✓ Real-time position tracking & status
- ✓ System-recommended target positions
- ✓ Supports multiple pipetting methods
Concept explorations
Multiple user flows and interaction models were explored to determine the most efficient way to guide users through sample transfers. The flows addressed different scenarios — single transfers, batch transfers, and error recovery paths.
Design explorations
The first step was to get the base layout right. Based on the user research, we understand that the users set up manual pipetting by placing the source containers on the left and target containers on the right. The transfer happens from left to right. We wanted to replicate the same experience in the application.
Basic layout
Establishing the core left-to-right mental model — source containers on the left, target on the right.
Adding structure
Introducing sample data, container previews, and a dedicated visualizer panel.
Detailed wireframe
Full-fidelity layout with sample list, 96-well plate grid, and transfer confirmation flow.
Design details
Getting the details right was critical for a tool that lab technicians would use daily under time pressure. Three key areas required deep design attention.
Target containers
We were dealing with two types of containers — a 96-well plate and an 8-tube strip. For each container type, we needed to clearly indicate position status: empty, suggested, filled, and unavailable for pipetting.
Pipetting orientation
Users use different pipetting methods — multi-channel dispensing (8 or 12 samples at once) and single-channel. The 8-channel pipettes place samples vertically, while 12-channel pipettes place them horizontally. The design allows users to choose their method, and the system adapts the recommended target positions accordingly.
Sample visualizer
The sample visualizer is the core of the transfer experience — a real-time visual representation of both source and target containers, highlighting the active transfer step and guiding the technician through each pipetting action with clear visual cues.
Final design
The final design consolidated all learnings from research, concept explorations, and usability testing into a cohesive sample transfer experience that visually guides technicians through each step of the pipetting process.
Validation & Testing
A formative usability test was conducted with 3 lab technicians to gauge their mental model against the design. This allowed us to validate how we translated user interview learnings into the interface. Technicians performed actual pipetting tasks while following the on-screen guidance in a simulated lab environment.
The testing surfaced actionable feedback that directly shaped the final design — including adding sample-level comments for noting input volumes, concentration, and buffer details, which became one of the most-used features in the shipped product.
Measurable impact
We compared task completion times between the manual sample transfer process (spreadsheets + physical tracking) and the visually guided approach across all 3 test users. The results exceeded our expectations.
Key findings
- Visual guidance eliminated errors — Zero mis-transfers during testing, compared to the error-prone manual process where interruptions could derail an entire batch
- Spatial model validated — The left-to-right source-target layout matched the users' physical workspace mental model exactly, requiring zero learning curve
- Step-by-step approach preferred — Users strongly preferred the guided sequential transfer over seeing all transfers at once, reporting significantly lower cognitive load
- Pipetting orientation clarity — Clear indication of row vs. column pipetting direction eliminated confusion about transfer sequences
- Comments feature was a surprise hit — The sample-level comments added from testing feedback became critical for tracking concentration and buffer details