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Delivering Great Customer Service in the Healthcare Industry

Help Scout

Perhaps there’s specific terminology to learn or a heavier focus on one type of metric over another. Take healthcare, for example: Outside of differing cultural norms, there are also laws pertaining to things like storing information or interacting with customers. Why customer service is important in the healthcare industry.

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Making Businesses Better: Introducing New VirtualPBX Case Studies

VirtualPBX

Whether it’s a flooring manufacturer, a financial services firm, or a digital healthcare solutions company, a reliable and feature-rich communication system is vital to streamline operations and boost customer satisfaction. In the healthcare industry, reliability and connectivity are paramount.

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Automatically generate impressions from findings in radiology reports using generative AI on AWS

AWS Machine Learning

For a quantitative analysis of the generated impression, we use ROUGE (Recall-Oriented Understudy for Gisting Evaluation), the most commonly used metric for evaluating summarization. This metric compares an automatically produced summary against a reference or a set of references (human-produced) summary or translation.

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Build a news recommender application with Amazon Personalize

AWS Machine Learning

However, delivering truly personalized recommendations presents several key challenges: Capturing diverse user interests – News can span many topics and even within specific topics, readers can have varied interests. For more information about these metrics, see Evaluating a solution version with metrics.

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Understanding Patient Feedback: A guide for Healthcare Professionals

Feedback

Healthcare providers often learn how they’re doing directly from their patients in person. Like other online review sites, there are several healthcare provider review sites. They can also provide expanded information about their practice, healthcare philosophy, credentials, and more. What Are Patient Feedback Websites?

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Revolutionizing large language model training with Arcee and AWS Trainium

AWS Machine Learning

The increasing number of trained tokens correlated with enhanced model performance, as measured by the perplexity metric. This is particularly evident from the direct comparisons of time-to-train metrics between Trainium and traditional GPU setups, where Trainium’s efficiency and cost-effectiveness shine.

APIs 108
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How to evaluate the quality of the synthetic data – measuring from the perspective of fidelity, utility, and privacy

AWS Machine Learning

For highly regulated sectors such as Financial Services, Healthcare, Life Sciences, Automotive, Robotics, and Manufacturing, the problem is even greater. The metrics that translate each one of these dimensions for the end-users are somewhat flexible. We look at each of these metrics in more detail in the following sections.

Metrics 78