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What We Can Learn from Amazon for Clinical Decision Support Systems.

Sidra Abid¹, Karim Keshavjee¹, Arsalan Karim¹

¹InfoClin Inc, Toronto, ON.

Studies in Health Technology and Informatics

|February 11, 2017

Summary

Healthcare can improve patient care and operational efficiency by adopting advanced analytics and decision-support tools, inspired by retail giants like Amazon. This research explores applying Amazon

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Area of Science:

Health Informatics
Data Science in Healthcare
Clinical Decision Support Systems

Background:

Healthcare lags behind retail and finance in adopting decision-support tools.
Amazon effectively uses predictive and prescriptive analytics to enhance customer experience and loyalty.
There is a need to bridge this gap by applying similar data-driven strategies in healthcare.

Purpose of the Study:

To explore Amazon's data science and big data practices for inspiration.
To identify elements of the Amazon website and practices applicable to healthcare.
To propose re-design strategies for clinical decision support systems in healthcare.

Main Methods:

Analysis of Amazon's website features and data science practices.
Identification of transferable concepts from e-commerce to healthcare.

Keywords:

A/B testing Amazon®algorithm clinical decision support system predictive analytics prescriptive analytics

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Development of potential clinical applications based on Amazon's models.

Main Results:

Specific Amazon website elements and data practices were identified.
Potential clinical applications were conceptualized for each identified element.
A framework for re-designing clinical decision support was proposed.

Conclusions:

Amazon's data-driven strategies offer valuable insights for improving healthcare decision support.
Applying these principles can enhance patient satisfaction, retention, and loyalty in healthcare settings.
Further research and development are needed to implement these enhanced clinical decision support systems.