Clarify Health has launched the real-world evidence-based cloud software, Clarify Trials, designed to aid life sciences companies to enhance the recruitment of clinical trial subjects from diverse and underrepresented communities.
The software offers more than 400 social determinants of health (SDoH) insights to help research sites as well as investigators to enrol patients from diverse populations into the trials.
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It also helps in evaluating the referral patterns of doctors to facilitate coordinated outreach within underserved people.
Furthermore, it allows life sciences companies and clinical research organisations to lower health inequalities without unwarranted delays in the trial, budget overruns, or change in protocol, while they navigate the 2020 guidance of the Food and Drug Administration to boost trial diversity.
The analytics of the software is based on a dataset that comprises SDoH data at the patient level for a variety of aspects, including race, gender, level of income, ethnicity, access to transportation, stability in housing, education level and health literacy.
More than 300 million patient lives are covered by these data, which are connected to government and commercial claims, prescription, clinical and dispensing information.
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By GlobalDataLife sciences firms could assess disparities in health within different patient groups and enrol people who best represent the future patient population of their treatment.
Clarify Health CEO Jean Drouin said: “Greater clinical trial diversity has an important role to play in tackling undeniable disparities in healthcare.
“Clarify’s patient journey analytics deliver socio-behavioural insights to ensure the people most in need are included in clinical research, accurately diagnosed and prescribed the right therapy.”
The cloud-based business applications of the company leverage the Clarify Atlas Platform that charts more than 300 million patient journeys to provide over 18 billion artificial intelligence-driven predictions and surface insights quickly and accurately.
