Data Analytics

The Department of Surgery offers centralized resources to help faculty acquire data and perform analytics for a variety of needs and projects, including:

  1. Quality (NSQIP, VQI, STS, billing data, Cerner/medical record)

  2. Financial (all billing data + hospital and professional fee rates/costs)

  3. Patient population (number of patients that have XX diagnosis)

  4. Statistical analyses for research papers/abstracts

  5. Other

Faculty have used these data pulls and associated analytics and biostatistician work for publications, clinical trials start up and implementation, research proposals, and other endeavors. A major advantage of working with our internal team is that they are aware of the compliance and HIPAA requirements that must be observed when acquiring data within our regulatory environment.

Data Requests and Deadlines

Data analytic services are available to all faculty that need support. Analytics requests will be acknowledged by a team member within 48 hours. Small projects of four-to-eight hours will be done in the order they are received and completed in less than two weeks. Completion estimates for requests requiring more time to execute will need to be determined when compared to the status of other requests in the queue at the time of submission.

To make a data request, please complete this form.

For specific cardiothoracic surgery data requests, please complete this form.

Project Examples

Four-to-eight hours

  • Descriptive statistics or very simple univariate analysis for an abstract submission

  • Compiling information from the data warehouse regarding the number of patients that meet certain conditions, such as a certain diagnosis or who underwent a certain procedure

  • A well-defined, limited query from NSQIP, STS, or other registry (e.g., patients with pneumonia who underwent CEA surgery from NSQIP)

  • Statistical consultation on an analysis being conducted (e.g., a fellow is performing the analysis but wants advice on what test to use or how to interpret certain results)

More time intensive

  • Statistical analysis for a research paper

  • Complex queries of clinical data from the data warehouse on a population of patients

  • Setting up a RedCap or other type of database for research work