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ClinTech | Greenville, SC
Avallano ClinTech Increased Patient Enrollment by 23% leveraging data
Systems of Record: MongoDB, Snowflake, QuickBooks Online, AWS Pinpoint, ClinicalTrials.gov, MedLinePlus
Targets/Destinations: Snowflake, SFTP, MongoDB
Avallano was establishing a cloud-first platform in the ClinTech space and had data in transactional and operational applications, including third party datasets. We did not want to build and maintain our own data integration connectors due to the high employee cost and the technical debt required to maintain over time.
The need for a dashboard that integrated these datasets on a daily basis along with providing access to our external customers was paramount since it’s part of Avallano’s value-add to its customers.
The Solution & Outcome
By selecting DataLakeHouse.io as the center of their data integration strategy, Avallano was able to integrate data from many sources into Snowflake’s Data Cloud in one day. From here, dashboards were created for internal and external customers for operational and clinical trial purposes. This saved us over 100 hours of development time and costs.
“Even as a non-technical user, I can easily set up connections from various source systems to bring all of the data I need into Snowflake on a daily basis. This prevents us from having to hire additional employees with the skills required to build and maintain data pipelines. The dashboards offer visualizations that make the data understandable and, more importantly, actionable.”
Co-Founder – COO, Avallano
Participant Identification and Recruitment
By leveraging DataLakeHouse.io, Avallano is able to use their platform to identity and recruit participants for clinical trials by adhering to several technology-based principals:
Enhanced Patient Selection Precision: Technology can significantly improve the process of patient selection for clinical trials by allowing researchers to use advanced algorithms and data analytics to identify individuals who are most likely to benefit from the treatment being tested. This precision can lead to more successful trials with higher response rates, as patients who are a better match for the treatment are enrolled.
Faster Recruitment: Technology can streamline the patient recruitment process by automating the identification and screening of potential participants. By leveraging electronic health records, patient databases, and AI-driven algorithms, clinical trial sponsors can identify and recruit eligible patients more quickly, reducing the time and cost associated with trial recruitment.
Diverse and Representative Trial Populations: Technology can help ensure that clinical trials include a more diverse and representative group of patients. By analyzing a broader range of patient data and using tools to identify potential participants from underrepresented demographic groups, clinical trials can have results that are more applicable to the general population, leading to improved healthcare outcomes for a wider range of patients.
Analytics drives efficiencies in identifying clinical trial participants
Analytics helps to reduce the costs associated with recruiting participants for clinical trials. By targeting and identifying potential participants more efficiently, organizations can save up to 20% on recruitment expenses.
Data analytics also leads to faster recruitment and shorter trial timelines. Clinical trials that leverage analytics typically achieve enrollment goals 30% faster, allowing for quicker data collection and analysis, potentially speeding up the drug development process.
“I’ve witnessed firsthand the transformative power of analytics in improving the participant recruitment for clinical trials. By harnessing the insights derived from data, we’re not only reducing the time and costs associated with recruiting participants, but we’re also accelerating our mission to bring life-changing therapies to those in need. Analytics isn’t just a tool; it’s our compass guiding us towards more efficient and impactful research.”
Paul Della Maggiora,
Rather than rely on traditional methods that are expensive and time consuming to identify and recruit participants to clinical trials, using data and analytics to streamline this process significantly reduces those time and costs.