  Health informatics can inform decisions that practitioners, patients,
policymakers, and researchers need to make about health and disease. Health
informatics is built upon patient health data leading to the need to codify
patient health information. Such standardization is required to compute
population statistics (such as prevalence, incidence, etc.) that are common
metrics used in fields such as epidemiology. Reliable decision-making about
health and disease rests on our ability to organize, analyze, and assess data
repositories that contain patient health data.
  While standards exist to structure and analyze patient data across patient
data sources such as health information exchanges, clinical data repositories,
and health data marketplaces, analogous best practices for rigorously defining
patient populations in health informatics contexts do not exist. Codifying best
practices for developing disease definitions could support the effective
development of clinical guidelines, inform algorithms used in clinical decision
support systems, and additional patient guidelines.
  In this paper, we present a workflow for the development of phenotype
definitions. This workflow presents a series of recommendations for defining
health and disease. Various examples within this paper are presented to
demonstrate this workflow in health informatics contexts.
