Clinical Data Coordinator (Open Rank, Entry to Senior)

University of Colorado•Aurora, CO

60d•$48,446 - $72,498•Onsite

About The Position

Clinical Data Coordinators perform key roles in gathering clinical data and ensuring high degrees of comprehensiveness, accuracy, and fidelity. They establish and maintain patient databases crucial for successful academic clinical research and basic science pursuits involving patient samples and information. Additionally, they have intimate knowledge of data transfer mechanisms and usage, thus can inform strategies to streamline these processes through computational and artificial intelligence-based approaches. This Clinical Data Coordinator position is unique in that it is part of a pilot program for a campus-wide initiative to better harness clinical data. The position is funded for one (1) year as a proof-of-principle, with the potential for prolonged extension if successful. The position is funded in collaboration with a bio-tech company (Refined Science) embedded on the University of Colorado campus, with the opportunity to help develop translational tools applicable to both academic and industry data projects. The coordinator will work closely with our clinical trials unit and the clinical data team at Refined Science to further develop skills applicable to all manner of clinical data collection and analysis. If successful at the one (1)-year mark, there is significant opportunity for this data analyst to lead and shape the expansion of data efforts across the school of medicine and improve the collaborative clinical data efforts with Refined Science.

Requirements

Bachelor's degree in any field.
A combination of education and related technical/paraprofessional experience may be substituted for the bachelor's degree on a year for year basis.
Knowledge and understanding of federal regulations and Good Clinical Practice (GCP).
Ability to communicate effectively, both in writing and orally.
Ability to establish and maintain effective working relationships with employees at all levels throughout the institution.
Familiarity with artificial intelligence and application in healthcare data.
Familiarity with malignant hematologic diseases, classifications, and outcome metrics.
Knowledge of basic human anatomy, physiology, and medical terminology.
Organized with great attention to detail and a commitment to accuracy.
A willingness to learn and ask questions.
Possess a high level of initiative and self-motivation.
Ability to maintain flexibility in a changing environment.
Ability to manage time well and prioritize.
Ability to handle problems in a solutions-oriented manner.

Nice To Haves

Prior experience in a healthcare or research setting with oncology or blood cancers.
Prior experience with biostatistics.
Prior independent programming experience with R, particularly analysis with common healthcare outcome metrics such as kaplan-meier curves, hazard ratios, demographic statistic tables, and others.
Experience using REDCap databasing software and API functionality.
Prior experience with AI functionality as applied to healthcare databasing questions and challenges.
Two (2) or more years of data abstraction experience including knowledge of basic medical terminology and the use of electronic medical records.
Some programming knowledge or experience.

Responsibilities

Efficient abstraction of pathology, treatment and other clinical data from medical records that is thorough and accurate.
Utilization of critical thinking skills with a high attention to detail while performing abstraction.
Identify potential problems affecting the abstraction process for improvement and quality control.
Perform quality review on work completed by other data abstractors.
Review data quality dashboards and make corrections.
Address data quality questions from the multidisciplinary team.
Work with the multidisciplinary team to support automated abstraction activities.
Work to develop new procedures for data abstraction involving computational approaches and artificial intelligence.
Work with clinicians for clarification when needed.
Assist in the development of data definitions and abstraction procedures.
Practice a high level of integrity and honesty in maintaining confidentiality.
Adhere to research regulatory standards.