Composer: Visual Cohort Analysis of Patient Outcomes

Composer screenshot


Objective. Visual cohort analysis utilizing electronic health record data has become an important tool in clinical assessment of patient outcomes. In this paper, we introduce Composer, a visual analysis tool for orthopedic surgeons to compare changes in physical functions of a patient cohort following various spinal procedures. The goal of our project is to help researchers analyze outcomes of procedures and facilitate informed decision-making about treatment options between patient and clinician.

Methods. In collaboration with Orthopedic surgeons and researchers, we defined domain-specific user requirements to inform the design. We developed the tool in an iterative process with our collaborators to develop and refine functionality. With Composer, analysts can dynamically define a patient cohort using demographic information, clinical parameters, and events in patient medical histories and then analyze patient-reported outcome scores for the cohort over time, as well as compare it to other cohorts. Using Composer's current iteration, we provide a usage scenario for use of the tool in a clinical setting.

Conclusion. We have developed a prototype cohort analysis tool to help clinicians assess patient treatment options by analyzing prior cases with similar characteristics. Though Composer was designed using patient data specific to Orthopedic research, we believe the tool is generalizable to other healthcare domains. A long term goal for Composer is to develop the application into a shared decision-making tool that allows translation of comparison and analysis from a clinician facing interface into visual representations to communicate treatment options to patients.


Jen Rogers, Nicholas Spina, Ashley Neese, Rachel Hess, Darrel Brodke, Alexander Lex
Composer: Visual Cohort Analysis of Patient Outcomes
Applied Clinical Informatics, 10(2): 278-285, doi:10.1055/s-0039-1687862, 2019.


  title = {Composer: Visual Cohort Analysis of Patient Outcomes},
  author = {Jen Rogers and Nicholas Spina and Ashley Neese and Rachel Hess and Darrel Brodke and Alexander Lex},
  journal = {Applied Clinical Informatics},
  publisher = {American Medical Informatics Association},
  doi = {10.1055/s-0039-1687862},
  volume = {10},
  number = {2},
  pages = {278-285},
  year = {2019}


This is the journal version of a previously published workshop paper.


We thank Sahar Mehrpour for initial data analysis and implementation. This project is funded by the Orthopaedic Research Center and NSF IIS 1751238.