Lead Quality Clinical Data Abstractor

POSITION SUMMARY:

The Lead Quality Clinical Data Abstractor will support the oversight of all daily operational data management and quality activities for all data abstracted by the program’s internal and external abstraction teams. This role includes supporting the design and establishment of quality requirements and workflows for the abstraction department’s quality framework, monitoring and controlling the quality of the program’s abstracted data, and hosting recurring department training and audits. This position is also responsible for communicating frequent updates and plans on high impact, high visibility data products.

PRIMARY RESPONSIBILITIES:

● Responsible for performing abstraction for ad hoc and ongoing internal and external data and abstraction requests for clinical information. Abstraction responsibilities include manually reading, reviewing, and abstracting clinical data from medical records.

● Track patients and data abstracted, along with meeting program productivity requirements.

● Serve as an integral part of the quality team by collaborating with the Lead Clinical Data Quality Manager to be the first escalation point of medical and abstraction questions raised by internal and external abstraction teams, and cross-functional team of IT, engineering, data scientist, medical, and scientific staff as it relates to clinical data and abstraction activity.

● Contribute to the development of the department’s abstraction/clinical data QA and QA governance framework.

● Support the development and maintenance of all abstraction QA and QC artifacts such as workflows, policies and procedures.

● Conduct quality auditing, validation, and tracking, while identifying data quality issues using quality assessment reports.

● Provide feedback that can be utilized by the Clinical Data Abstraction team to improve data quality issues.

● Support the application of conceptual and independent methods for analyzing and evaluating data, including transforming data trends or data patterns into insights to inform long-term business and department decisions.

● Immediately communicate minor and major quality issues, including those that will impact program and abstraction projects.

● Provide statistical reports on patient data or trends as a result of abstraction efforts (strong data analysis skills) and data product.

● Support the engineering team to identify data issues and ensure clinical data are modeled appropriately for end user querying and utilization.

● Support the creation and design of data standardization and harmonization specifications.

● Build and maintain ongoing strong working relationships with stakeholders, with a focus on building effective communication channels.

● Other duties as assigned.

QUALIFICATIONS:

● MS or higher in health sciences, bioinformatics, or related field.

● Currently U.S. certified Physician Assistant, Nurse Practitioner, or Nurse (MS level of above) (only candidates with such certifications will be considered).

● 5+ years of clinical data abstraction experience. Requires direct experience performing medical record abstraction across multiple platforms (paper records, different electronic medical record systems, EDC, registries, etc.).

● Must be familiar with various cancer indications and the treatment and care of various cancers, rare diseases, and women’s health.

● Must be familiar with data harmonization, and standardization.

● Must be familiar with standard clinical data abstraction models, data abstraction, and various clinical data ontologies such as ICD-9-CM & SNOMED CT.

KNOWLEDGE, SKILLS, AND ABILITIES:

● Ability to work independently, with little to no direction, and as part of a team.

● Strong organizational and communication skills.

● Strong research and clinical skills.

● In-depth attention to detail and a fast learner.

● Experience working with startup companies and respond to shifting

priorities and changes.

● Ability to interact with various levels of staff and external customers.

● Possess a high level of initiative and self-motivation.

● Strong computer and Google Suite skills.

● Experience working with multiple monitors.

● Strong familiarity with oncology data codification and various clinical data ontologies such as ICD-9-CM and SNOMED CT.

● Strong familiarity with data abstraction tools, EDCs, and registries and working with system change requests, and providing design input in the design of such databases.

● Medical terminology experience.

● Experience with multiple electronic medical record systems such as Allscripts, OncoEMR, Cerner, Epic, NextGen, Meditech, Cerner, etc.

● Exper