Developing a cohesive analysis ready dataset and cohort building grammar

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Motivation

Research teams working with real-world healthcare data face a fundamental challenge: every analysis starts with the time-consuming task of building cohorts and preparing datasets. Without standardized tools, data scientists spend countless hours writing custom code to apply inclusion/exclusion criteria, derive patient features, and create analysis-ready datasets. This leads to inconsistent methods across teams, duplicated effort, and longer time-to-insights.

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Solution

Plinth develops cohesive cohort building frameworks that transform how teams work with real-world data. Our solutions feature intuitive, low-code grammar that enables both novice and expert users to efficiently build analysis-ready cohorts. By creating standardized "verbs" like include_*(),add_*(), and derive_*(), we ensure consistent methodology while automating complex data processing tasks. Our frameworks handle everything from patient attrition tracking to biomarker summarization, with built-in transparency and scientific rigor.

Impact

Plinth's cohort building solutions deliver dramatic efficiency gains: 80% reduction in time to answer analytical requests, zero lines of code required for basic cohort generation, and over 100 hours of development time saved. Teams can now focus on discovering insights rather than wrestling with data preparation. By standardizing cohort building processes across organizations, our frameworks ensure reproducible, scientifically valid analyses while enabling rapid scaling to new use cases and datasets.

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