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Making Clinical Trial Decisions Easier
Service Design, User Research, Strategy



Project Duration

2.5+ years

Role & Activities

Designer, Product Manager, Program Lead

How might we improve current supply chain system and data management to support decision making so that product allocation for clinical trials is more transparent and tension-free?

OpenBiome is a nonprofit biotech organization catalyzing research in the microbiome with poop-derived treatments. Healthy microbiome bacteria are harvested from a donor’s poop and packaged as a treatment to patients with Clostridium difficile infection and other gut-related diseases.

An acquisition of the core laboratory function created silos in the poop supply chain. Most departments, including Product Operations, were now part of Finch Therapeutics while Clinical Research and Commercial Product remained a part of OpenBiome.

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With all internal users, we took advantage of frequent process observations and co-designing workshops.

We spent 4 months on research—observing and talking with laboratory technicians, stakeholders, and experts in biotech-specific information technology systems, quality assurance and control, and regulated supply chain environments. We took this time to watch 3-hour long processes from prep to clean up, ask why at almost every step, and observe unspoken team and workflow dynamics.

Co-design workshops were critical to success. In such a fast-paced organization, these workshops were dual purpose: maintain user and stakeholder interest and buy in as well as hear directly from teammates what they were thinking about their processes, frustrations, and goals. Throughout the project, we continued to use similar full-team sessions to review, troubleshoot, and plan for new features.

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We mapped... everything!

We mapped everything as we listened, asked questions, and observed. We made maps of process flows, information flows, data flows, physical movement within buildings and across the city of Boston, interactions between stakeholders, and interconnectivity of goals and workstreams. We paid extra attention to cross-functional details to ensure we understood how these junctures played out in real life.

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Insight #1: Most of the company was in the dark about inventory and donor information.

There was a lot of pressure to have “accurate” numbers when the actual need was a general trend to inform decision making.

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We implemented a dashboard tool that allowed for customized dashboards for each team or initiative. We created a meeting called “State of the Poonion” as a change management effort. 


By shifting the fidelity and frequency of data access, we were able to relieve pressure on our only data analyst and empower our teams to make data-informed decisions on a daily basis!

Insight #2: We found that many observed issues came out a lack of protocol and skip-level decision making. 

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We defined interactions between the OpenBiome Clinical Research Team and the Finch teams, created protocols, and developed and executed an implementation plan to take the current state of clinical research fulfillment into a stable future.


With robust structured protocols to guide interactions between clinical research and manufacturing teams, the backlog of clinical trials was cleared out within 2 months and team dynamics drastically improved!

Insight #3: A delicate balance of digital and paper systems was required for effective change management.

We delivered a FDA-compliant electronic medical record and inventory management system. To future-proof all the improvements made so far, we implemented a digital system that keeps data accurate. 



  • Increased patient safety

  • Enabled the organization’s pursuit of FDA approval by increasing reliability and decreasing friction to data-driven decision making

  • Real-time data dashboards as a trustworthy tool for planning

  • Integrated a structured way to engage with scaling and maturing business functions

Lessons learned: We were most effective when we designed with our teams rather than for them.

Many of our stakeholders and users prioritized more time-sensitive work. With such a long project, it was difficult to maintain consistent engagement and buy-in. We were most effective when we designed with our teams rather than for them. In practice that meant more rounds of iteration, lobbying their managers allocate working hours towards this project, and partnering with champions on each team to bridge the gap between the old and new processes. 

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