  A consequence of the fragmented and siloed healthcare landscape is that
patient care (and data) is split along multitude of different facilities and
computer systems and enabling interoperability between these systems is hard.
The lack interoperability not only hinders continuity of care and burdens
providers, but also hinders effective application of Machine Learning (ML)
algorithms. Thus, most current ML algorithms, designed to understand patient
care and facilitate clinical decision-support, are trained on limited datasets.
This approach is analogous to the Newtonian paradigm of Reductionism in which a
system is broken down into elementary components and a description of the whole
is formed by understanding those components individually. A key limitation of
the reductionist approach is that it ignores the component-component
interactions and dynamics within the system which are often of prime
significance in understanding the overall behaviour of complex adaptive systems
(CAS). Healthcare is a CAS.
  Though the application of ML on health data have shown incremental
improvements for clinical decision support, ML has a much a broader potential
to restructure care delivery as a whole and maximize care value. However, this
ML potential remains largely untapped: primarily due to functional limitations
of Electronic Health Records (EHR) and the inability to see the healthcare
system as a whole. This viewpoint (i) articulates the healthcare as a complex
system which has a biological and an organizational perspective, (ii) motivates
with examples, the need of a system's approach when addressing healthcare
challenges via ML and, (iii) emphasizes to unleash EHR functionality - while
duly respecting all ethical and legal concerns - to reap full benefits of ML.
