Friday, October 30, 2015

Use Cases for Data Science at Academic Health Science Centers

Like many academic health science centers, my institution is undergoing a planning process to determine our strategy for data science. I have expressed my concern about the (lack of?) differences between data science and biomedical and health informatics, but the former term seems to be carrying the day. I consider it a personal mission to ensure that the long learned history of biomedical and health informatics is not lost in our rush to embrace this seemingly new data science.

One of my major contributions to our process has been to delineate a set of use cases for data science in academic health science centers. These institutions are distinct from organizations that are predominantly devoted to healthcare delivery, and tend to have small or non-existent research and education missions, and general universities, which may not have healthcare delivery activities integrated with their research and educational missions.

I have broken down my use cases into the three general missions that most academic health science centers have. I only present these at a high level, and there is obviously a much greater depth of detail that could be described for each. But these are the big-picture use cases that in my view drive data science in academic health science centers.

Use cases for the clinical mission of academic health science centers include:
  • Clinical decision support – improve clinical practice via predictive analytics and other uses of patient data, including precision medicine as it works its way into clinical practice
  • Quality measurement and improvement – use data to measure and improve quality of care delivered, especially as healthcare shifts to new value-based models of care
  • Business intelligence – apply data to improve business and financial operations of healthcare delivery
  • Patient engagement – patients upload and interact with data related to their care
  • Public health surveillance – use data for early detection and intervention in public health threats (natural and manmade)
Research is also critically important for academic health science centers, and here are some broad use cases, with many important variations on these themes:
  • Prospective studies – improve data capture and analysis for clinical trials and related studies
  • Retrospective studies – enhance ability to use data already collected
  • Basic science research – studies in the "omics," imaging, and other areas that lead to health-related applications
  • "Third science" research – advancing the science of healthcare delivery, the third science of healthcare (after basic and clinical sciences)
  • Data science and informatics research – advance the theory and practice of data science and biomedical and health informatics
Education is also a vital mission for academic health science centers, not only to train users and managers of data but also professionals and researchers who implement and advance the science:
  • Training for data users and managers, clinicians, and others – allowing those who implement programs applying data science to be more savvy in doing so
  • Education for data science and informatics professionals – master's-level education as well as the new clinical informatics fellowships for physicians
  • Advanced education for data science and informatics researchers – doctoral-level education to advance the science of this work
Data science is indeed unique in academic health science centers. These use cases demonstrate how it spans across all of their missions. The success of initiatives such as ours are likely to depend upon the integration all of three.

1 comment:

  1. I completely agree with your points. I am currently working on a systematic review concerning the designs of EHRS and clinical workflow as a clinical informatics grad student at UW-Seattle. I hope that the future faculty at the upcoming medical college at UIUC ( considers your perspective.