Improving Healthcare Outcomes Through AI-assisted Orchestration of Patient Data

1

Improving Healthcare Outcomes Through AI-assisted
Orchestration of Patient Data
2
Improving Healthcare Outcomes Through AI-assisted
Orchestration of Patient Data


Abstract The COVID-19 outbreak had been a
moment of truth for global healthcare
establishments. In a world where the existing
healthcare infrastructure is often stretched to
their limits to cope with the exponential
inflation in non-communicable and lifestyle
disease burden, the onset of the pandemic left
obvious discords in its wake. Nevertheless, the
situation has also allowed the healthcare
decision-makers to introspect on setting the
imperative of facilitating operational ease and
accentuating healthcare experience within
compressed response windows. The industry
discourse around embedding clinical workflows
with Artificial Intelligence (AI) engines to
assist manual operations, is not new. However,
the recent developments have brought the EHR
systems, constituting the single source of truth
for medical practice management landscapes, into
the priority focus.  This whitepaper seeks to
hold up a case for aligning AI and data-driven
constructs with EHR workflows, investigate the
typical complexities posed by such transitions
and the role of comprehensive testing and quality
assurance (QA) inputs in drawing a simplified
AI-backed EHR adoption roadmap. 
3
Improving Healthcare Outcomes Through AI-assisted
Orchestration of Patient Data

• Accentuating Clinical Impact with a Robust Test
  QA Framework
• While the factors mentioned above can have
  evident negative projections on both the speed
  and quality of healthcare, they should not deter
  HCOs from inducting the AI/ML advantage into
  their EHR operations. For this, both healthcare
  IT vendors involved in the production of new
  generation enterprise applications and clinical
  establishments which operate them are
  increasingly turning to a new testing and QA
  outsourcing model.
• Validate the stability of the AI engines and the
  applications powered by them across the EHR
  landscape.
• Validate the relevance of the ML models
• Verify that the service coverage of the EHR stack
  is in line with the organizations mission
  statement.
• Validate that all the performance benchmarks and
  the quality of human-machine interaction are up
  to the satisfaction of the stakeholders in the
  loop. 
• Read More
• https//www.cigniti.com/resource/white-papers/ai-p
  atient-data-orchestration-healthcare