As more health systems recognize the value of care management programs, they may find that traditional utlization-based or morbidity-based algorithms are insufficient in helping them identify the patients most likely to benefit from care management.   In an exclusive article for For the Record, principal data scientist Michael Simon, PhD explains:

  • The value of care management programs in supporting individual physicians
  • Challenges with traditional algorithms for identifying patients for care management
  • How predictive analytics can offer an alternative approach to identifying the patients most likely to respond to interventions
  • Approaches to developing those predictive analytics
  • The importance of a physician’s clinical judgment in implementing any algorithm for patient selection

To read the full article, please visit For the Record.  

Michael Simon, PhD

Michael A. Simon, Ph.D., is Principal Data Scientist at Arcadia Healthcare Solutions, with experience in data quality, process efficiency, and experimental design. Since joining Arcadia in 2011, Michael has focused on demonstrating and enhancing the value of client data through exploratory data analysis and advanced statistical methodologies, including through Arcadia’s Data Quality Initiative and the development of Arcadia’s Launchpad platform. Michael is a former National Science Foundation AAAS Science and Technology Policy Fellow, where he coordinated the redesign of a critical granting process and developed and implemented an international prize competition in coordination with the Bill & Melinda Gates Foundation benefiting smallholder farmers in developing countries. Michael received his doctoral degree from Tufts University in Neurophysiology and Biomechanics, and received his Bachelors degrees from Rice University in Electrical Engineering and Economics.