Study Design & Start Up
Optimize your study design, accelerate startup, and improve in every way.
Feasibility
Custom feasibility solution for a large pharmaceutical company.
The Challenge
A large pharmaceutical company was facing long feasibility cycle times for their high priority principal investigators (PIs) and sites across their portfolio. With global studies across eight therapeutic areas and multiple CROs, analytics were extremely tedious and time-consuming — using multiple Excels, Sharepoints and emails. Data remained in silos, delaying feasibility and startup times.
How Saama Helped
Saama built a data repository that allowed the sponsor to centralize all their data — internally, as well as from CROs — in a single location. Then Saama built an analytics tool that allowed the customer to quickly identify, qualify and select countries, sites and investigators, as well as conduct ad hoc analyses.
Impact
Reduced site selection cycle times by 6 weeks.
Trial Diversity
Measure the diversity of your trials in real-time.
The Challenge
A large pharmaceutical company wanted to measure the diversity of their trials — including race, ethnicity, gender and age — to ensure the diversity of their trials aligned with that of the patient population.
How Saama Helped
Saama was able to create a epidemiologic library that allowed the customer to pull diversity data — such as race, ethnicity, age, sex, and disease prevalence information — for their studies. Saama also enabled them to link that data to patient data in their ongoing trials, so they could compare recruitment figures against epidemiological information in real-time to meet their diversity goals.
Impact
Customer able to measure the diversity of their clinical trials and track progress in real-time.
Literature Search and Analytics
Accelerate the study design process.
The Challenge
A biotechnology company was looking for a way to search literature data from in-house and public sources — including ct.gov — to support the design of future studies. Additionally, relevant clinical entities needed to be extracted from the literature sources, which is typically a time-consuming and manual process.
How Saama Helped
Saama built an AI-powered literature search engine, which automated the literature search, review, and clinical entity extraction process. Users were able to see ranked lists of relevant publications — based on their search parameters — and extract medical entities such as patient population, intervention, outcomes, inclusion/exclusion criteria, diagnosis, treatment and more.
Impact
Automated the literature search and clinical entity extraction process.
