Data Management Adjusted Productivity Model within the Sarah Cannon Transplant and Cellular Therapy Network (SCTCTN)- Process and Benefits
Year: 2025
Type: article
Abstract: Background/Introduction The Sarah Cannon Transplant and Cellular Therapy Network (SCTCTN) is comprised of 6 transplant and cellular therapy programs. There are 20 Data Analysts (DAs) who support the network. The analysts report directly to the Manager of Data Analytics and a Director of Clinical Support Services. In 2022, the network consolidated all of the DAs from the program level to the SCTCTN. Following this, leadership began tracking DA productivity. In September 2022, the SCTCTN began tracking productivity of DAs with the goal of understanding data abstraction efforts, removing abstraction barriers to improve productivity, and further inform staffing needs. Objectives Develop a productivity model that factors in the average work effort (in hours) to complete CIBMTR forms. In addition, a non-productive time model was developed to account for work unrelated to form completion. Methods Consulting with the experienced DAs, each form was allocated a work effort (in hours). Autologous, allogeneic and CAR T-cell forms were distinguished when defining the work effort. (Figure 1). Next, a weighted score per form type was defined. From here, each DA was given a productivity value that factored in forms completed and the weighted score of the form. This allowed for the ability to determine time spent completing each form with the understanding that some forms are more complex and take additional time to complete. Subsequently, a non-productive time model was developed. This was defined as time spent outside of form completion i.e. internal research projects, audits , CDVR, CTA, and program specific requests, as well as time away from the office (PTO, FMLA, etc.). Lastly, the weighted score/form type/DA and the number of productive weeks/DA were utilized to issue an overall weighted score. From here, categories were developed to determine overall performance: “Excellent”, “High”, “Average”, and “Low” (Figure 3). Results The weighted scores showed an actual representation of productivity of each data analyst compared to the non-weighted scores. Furthermore, the individual adjustments per data analyst accurately reflected not only the focus of the work effort related to form completion but also factors non-productive time. Conclusion Factoring in the varied work effort of each form type helps to better gauge the work performed by data analysts. It mitigates concerns related to volume of forms without considering form difficulty, it encourages complex form completion, and it assists with projection of staffing needs.
Authors: Hector Zambrano, Zachary Williams, Jennifer Culver, Sarah Hatley, Tonya Cox, Betsy Blunk
Institutions: HCA Healthcare, Sarah Cannon
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: hybrid
Estimated APC paid: $3,650
OpenAlex ID: W4407977096