Contractor - Medical Data Abstractor
3 days ago
About Triomics:Triomics is building the modern technology stack for oncology trial sites and investigators that unifies the workflows of clinical care and clinical research, moving the healthcare industry closer to the vision of Clinical Research as a Care Option. Our platform, which is based on our proprietary oncology-focused large language model (OncoLLMTM) co-developed with several leading cancer centers, eliminates the operational inefficiencies in patient recruitment, data curation, and other laborious tasks involved in clinical research, thus enabling the generation of high-quality data and speeding up the clinical trials.Role OverviewAs part of the Oncology Clinical team, the Medical Data Abstractor supports oncology data dictionary development, data curation/abstraction, SOP adherence, and clinical trial study builds. The role navigates electronic medical records (EMRs) and other clinical data sources to review and abstract information for consistency, completeness, and compliance with study protocols and applicable regulations.Contract Terms & Work Conditions- Type: Contract (full-time hours)- Location: On-site only- Schedule: Monday–Friday, standard business hours- Compliance: Must adhere to site access, confidentiality, and data privacy/security requirements (e.g., HIPAA-equivalent and company SOPs)Key Responsibilities- Screen patient cases for eligibility across multiple oncology clinical trials, applying trial-specific inclusion/exclusion criteria.- Interpret diagnostic, pathology, imaging, and other clinical reports to extract accurate, trial-relevant information per SOPs.- Abstract and categorize clinical data (e.g., adverse events, hospitalizations, medications) and enter it into designated electronic systems with high accuracy.- Serve as an oncology subject-matter expert across solid and hematologic malignancies; maintain up-to-date knowledge of cancer trial designs and requirements.- Apply critical thinking to understand complex clinical concepts and make sound, well-documented abstraction decisions.- Build and maintain data libraries; coordinate with cross-functional teams to address project-specific needs.- Review project documentation to align with goals, timelines, and abstraction workflows; onboard quickly to new projects using approved resources.- Share medical and procedural knowledge with peers; provide guidance to Data Abstractors as needed.- Respond promptly to queries from Leads/Operations and collaborate to resolve data issues.- Perform additional tasks as assigned, including preliminary eligibility assessments and second-level/QC reviews of medical records.Qualifications- 2+ years of experience in clinical data abstraction or patient treatment–related data work.- Strong clinical knowledge of chronic disease states and medical terminology.- Oncology knowledge across the care continuum (diagnosis through follow-up), with deep familiarity with both solid and liquid tumors.- Experience reviewing medical data for clinical research, especially oncology trials.EducationB.Sc (Nursing), BDS, BAMS, BHMS, MDS, BPharm, or MPharm.Skills & Abilities- Excellent written and verbal communication; able to collaborate with internal and external stakeholders in a fast-paced environment.- Sound judgment in abstracting medical data, with the ability to work independently and to escalate or seek input when appropriate.- High adaptability to changing assignments, project requirements, training schedules, and occasionally suboptimal tools or databases.- Proficient with Microsoft Office (Word, Excel, Outlook, PowerPoint); able to create, maintain, and share professional documents and reports.
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Senior Medical Data Abstractor
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Bangalore Division, India Triomics Full timeAbout Triomics: Triomics is building the modern technology stack for oncology trial sites and investigators that unifies the workflows of clinical care and clinical research, moving the healthcare industry closer to the vision of Clinical Research as a Care Option. Our platform, which is based on our proprietary oncology-focused large language model...
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