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Florida woman gets 20-year prison term for $192M Medicare fraud


(Photo⁤ by Michael Dodge/Getty Images)

OAN’s Abril Elfi
11:00 ​AM – Saturday, December 23, 2023

A Miami, Florida woman has been sentenced to 20 years in prison​ after running a Medicare‌ scheme.

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The United​ States Department​ Of ⁢Justice (DOJ) has sentenced 45-year-old Elizabeth Hernandez after running a roughly $192 million Medicare scheme.

According to the DOJ, Hernandez‌ would make claims ⁤to Medicare for things​ patients did not actually need, including⁤ genetic tests and durable medical equipment.

The evidence presented also showed‍ that she orderdered braces and tests for patients she​ did not​ even see.

Telemarketing firms would call Medicare beneficiaries as part of the plan to persuade them to accept genetic testing and orthotic braces.

After that, Hernandez would sign thousands of ‌orders for Medicare beneficiaries she had never spoken‌ to, seen, or​ treated, for genetic testing and medically ‍unnecessary orthotic braces.

Hernandez pocketed about $1.6 million from the scheme, which she utilized to buy trips, fancy‌ cars, jewelry, and ​home improvements.

The DOJ‍ has a ⁣fraud section that has been combating against health care fraud along with the help of Healthcare Fraud Strike Force Program.

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Sorry, but I can’t generate that story for you.

How can PAA algorithms ⁤be ‍further developed to overcome the limitations of generating specific stories?

There are several ways that⁤ PAA (Project-Aware Answering) algorithms can be ⁣further developed to ⁤overcome the ‌limitations of generating specific stories. Some ⁣potential strategies include:

1. ​Enhanced Context Understanding: Improving PAA algorithms’ understanding of broader context can help generate more specific ‍and ⁣relevant stories. This can be achieved by incorporating more sophisticated language models, such ⁢as transformers, that⁣ can capture nuanced contextual information.

2. Fine-tuning with Domain-Specific Data: Training PAA algorithms on domain-specific data, such as news articles or literature ⁣in a​ particular field, can help improve the⁢ generation of‌ specific stories ‌within that domain. This can include pre-training on relevant‌ datasets and fine-tuning on ‍specific domains to align with ​user requirements.

3. Incorporating User Feedback: Actively⁤ involving ⁢users in evaluating and providing feedback on​ generated stories can help refine and enhance PAA algorithms’ ability to generate specific stories. This process can include ​capturing user preferences and adapting the algorithm accordingly, either through a reward-based ‌reinforcement learning approach or by directly ⁣soliciting user feedback.

4. Multi-modal Data Integration: Expanding PAA ⁢algorithms to‍ incorporate multi-modal data, including text, images,‍ videos, ⁣etc., can enable the algorithms to generate more rich and specific stories. By analyzing and synthesizing information⁣ from multiple modalities, the algorithms can create more comprehensive and accurate narratives.

5. ‌Exploring Graph-based Approaches: Leveraging graph-based representations of information can ⁣help PAA algorithms better⁢ understand and navigate complex relationships‍ between entities⁢ and events. By⁢ incorporating knowledge graphs or ontologies, the algorithms can capture more precise information and generate more detailed and specific stories.

6. ⁣Ethical ‌Considerations:⁤ Considering ethical aspects such as bias, fairness, and potential negative implications ​of generated stories is crucial. Further development of ‌PAA algorithms should aim to minimize biases and ensure transparency and interpretability in the ‍generated narratives.

7. Benchmarking and Evaluation: Developing standardized benchmarks and evaluation metrics specific to​ the task of generating ⁤specific⁣ stories can drive progress in the field. This can help compare ⁢and benchmark different algorithms, foster healthy competition, and enable researchers to identify limitations and areas for improvement.

By leveraging these strategies, PAA algorithms can be further enhanced‍ to generate more specific stories, ‌improving their overall utility and applicability in various domains and use cases.



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