Areas of Focus

Health Implementation

Health Implementation will devise the overall process by which promising precision health discoveries can be integrated into Michigan Medicine patient care and health systems throughout the state of Michigan, as well as informing improvements in health care nationwide. Leaders and providers throughout the health system are key partners in the success of this initiative.


Karandeep Singh, MD, MMSc
Associate Director

Karandeep Singh, MD, MMSc, is an Assistant Professor of Learning Health Sciences, Internal Medicine, Urology, and Information at the University of Michigan. He is a nephrologist with a background in biomedical informatics who uses machine learning methods to model electronic health record and registry data in support of a learning health system. He directs the Machine Learning for Learning Health Systems lab, which focuses on using machine learning and biomedical informatics methods to understand and improve health at scale. His research spans multiple clinical domains, including nephrology, urology, emergency medicine, obstetrics, and ophthalmology. He chairs the Michigan Medicine Clinical Intelligence Committee, which focuses on implementation of machine learning models across the health system. He teaches a graduate-level health data science course. He completed his internal medicine residency at UCLA Medical Center, where he served as chief resident, and a nephrology fellowship in the combined Brigham and Women’s Hospital/Massachusetts General Hospital program in Boston. He completed his medical education at the University of Michigan Medical School and holds a master’s degree in medical sciences in Biomedical Informatics from Harvard Medical School. He is board certified in internal medicine, nephrology, and clinical informatics.

Paul J. Grant, MD, FACP, SFHM
Assistant Director

Paul Grant is a hospitalist in the Division of Hospital Medicine at Michigan Medicine, and an Associate Professor of Medicine at the University of Michigan Medical School. He is Director of the Consultative and Perioperative Medicine Program and is active in medical student and residency education. In 2011, Grant was appointed Associate Chief Medical Information Officer for Michigan Medicine. He joined Precision Health in 2019. Grant has published several papers in the fields of venous thromboembolism and perioperative medicine in addition to authoring many book chapters. He is also the co-editor of a comprehensive perioperative medicine textbook. Grant’s professional organization memberships include senior fellowship in the Society of Hospital Medicine (SHM), fellowship in the American College of Physicians (ACP), and member of the Society for Perioperative Assessment and Quality Improvement (SPAQI).


Health Implementation Goals:

  • Identify barriers to the evaluation of algorithms, AI, clinical therapeutics, novel diagnostics, and process redesign interventions in IRB-approved patient populations at Michigan Medicine
  • Identify and correct data source limitations that prevent implementation science
  • Design an open-source decision support and visualization framework that allows integration of electronic health records, social determinants of health, digital phenotype, and genetics with novel algorithms to provide clinical decision support at the point of care
  • Enable novel algorithms and AI to be exposed and tested in micro-randomization trials of patients and providers at Michigan Medicine
  • Implement the decision-support system throughout various Michigan Medicine clinical settings, enabling campus researchers to work with patients and providers with a range of scientific needs
  • Enable at-scale implementation science of precision health data streams at Michigan Medicine clinical sites
  • Partner with statewide Collaborative Quality Initiatives (CQIs) led by U-M faculty to disseminate advances in clinical guidelines, decision support tools, clinical therapeutics, and novel diagnostics
  • Work with payers and policymakers to identify and overcome financial and regulatory barriers to implementation of reproducible, generalizable evidence
  • Enhance U-M’s reputation in implementation science and precision health implementation via peer-reviewed publications and enhanced competitiveness for sponsored research