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Improving Healthcare Outcomes Through AI-assisted Orchestration of Patient Data

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.

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Improving Healthcare Outcomes Through AI-assisted Orchestration of Patient Data

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  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 organization’s 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 Full Blog at:  • https://www.cigniti.com/resource/white-papers/ai-patient-data-orchestration-healthcare

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