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Artificial intelligence in Health Insurance - Current Applications and Trends

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Health insurance is a critical component of the healthcare industry with private health insurance expenditures alone estimated at $1.1 billion in 2016, according to the latest data available from the Centers for Medicare and Medicaid Services. This figure represents 34 percent of the 2016 National Health Expenditure at $3.3 trillion. In this article, we will look at four AI applications that are tackling problems of underutilization and fraud in the insurance industry. Some applications below claim that they are using artificial intelligence to help improve health insurance cost efficiency, while reducing waste of money on underutilized or preventable care. Other applications claim to detect fraudulent claims.


Is Clover Health Stock a Buy?

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The company sells Medicare Advantage plans, focusing on customer experience and leveraging machine learning and artificial intelligence to …


Now Streaming: Government Data

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The concept of data streaming is not new. But one of the most critical emerging uses for streaming data is in the public sector, where government agencies are eyeing its game-changing capability to advance everything from battlefield decision-making to constituent experience. IDC predicts that the collective sum of the world's data will grow 33%, to 175 zettabytes, by 2025. For context, at today's average internet connection speeds, 175 zettabytes would take 1.8 billion years for one person to download. Streaming has only further accelerated the velocity of data growth.


Hitting the Books: AI doctors and the dangers tiered medical care

Engadget

Healthcare is a human right, however, nobody said all coverage is created equal. Artificial intelligence and machine learning systems are already making impressive inroads into the myriad fields of medicine -- from IBM's Watson: Hospital Edition and Amazon's AI-generated medical records to machine-formulated medications and AI-enabled diagnoses. But in the excerpt below from Frank Pasquale's New Laws of Robotics we can see how the promise of faster, cheaper, and more efficient medical diagnoses generated by AI/ML systems can also serve as a double-edged sword, potentially cutting off access to cutting-edge, high quality care provided by human doctors. Excerpted from New Laws of Robotics: Defending Human Expertise in the Age of AI by Frank Pasquale, published by The Belknap Press of Harvard University Press. We might once have categorized a melanoma simply as a type of skin cancer.


AI-Powered Text From This Program Could Fool the Government

WIRED

In October 2019, Idaho proposed changing its Medicaid program. The state needed approval from the federal government, which solicited public feedback via Medicaid.gov. But half came not from concerned citizens or even internet trolls. They were generated by artificial intelligence. And a study found that people could not distinguish the real comments from the fake ones.


Sparse encoding for more-interpretable feature-selecting representations in probabilistic matrix factorization

arXiv.org Machine Learning

Dimensionality reduction methods for count data are critical to a wide range of applications in medical informatics and other fields where model interpretability is paramount. For such data, hierarchical Poisson matrix factorization (HPF) and other sparse probabilistic non-negative matrix factorization (NMF) methods are considered to be interpretable generative models. They consist of sparse transformations for decoding their learned representations into predictions. However, sparsity in representation decoding does not necessarily imply sparsity in the encoding of representations from the original data features. HPF is often incorrectly interpreted in the literature as if it possesses encoder sparsity. The distinction between decoder sparsity and encoder sparsity is subtle but important. Due to the lack of encoder sparsity, HPF does not possess the column-clustering property of classical NMF -- the factor loading matrix does not sufficiently define how each factor is formed from the original features. We address this deficiency by self-consistently enforcing encoder sparsity, using a generalized additive model (GAM), thereby allowing one to relate each representation coordinate to a subset of the original data features. In doing so, the method also gains the ability to perform feature selection. We demonstrate our method on simulated data and give an example of how encoder sparsity is of practical use in a concrete application of representing inpatient comorbidities in Medicare patients.


UVA Artificial Intelligence Project Among 7 Finalists for $1 Million Prize

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A UVA Health data science team is one of seven finalists in a national competition to improve healthcare with the help of artificial intelligence. UVA's proposal was selected as a finalist from among more than 300 applicants in the first-ever Centers for Medicare & Medicaid Services (CMS) Artificial Intelligence Health Outcomes Challenge. UVA's project predicts which patients are at risk for adverse outcomes and then suggests a personalized plan to ensure appropriate healthcare delivery and avoid unnecessary hospitalizations. CMS selected the seven finalists after reviewing the accuracy of their artificial intelligence models and evaluating how well healthcare providers could use visual displays created by each project team to improve outcomes and patient care. Each team of finalists received $60,000 and will compete for a grand prize of up to $1 million.


UVA artificial intelligence project among finalists for national challenge

#artificialintelligence

The proposal was chosen as part of the first Centers for Medicare and Medicaid Services Artificial Intelligence Health Outcomes Challenge. It predicts the outcomes for patients and suggests a personalized plan for their health care delivery to avoid unnecessary trips to the hospital.


Humanizing AI: How to Close the Trust Gap in Healthcare - InformationWeek

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Physician turnover in the United States, due to burnout and related factors, was conservatively estimated to cost the US healthcare system some $4.6 billion annually, according to a 2019 Annals of Internal Medicine study. The results reflect a familiar dynamic, where too many doctors are crushed in paperwork, which takes time away from being with patients. Just five months after this study was publicized, Harvard Business Review published "How AI in the Exam Room Could Reduce Physician Burnout," examining multiple artificial intelligence initiatives that may streamline providers' administrative tasks, thus reducing burnout. Still, barriers to trust in AI solutions remain, highlighted by 2020 KPMG International survey findings that note only 35% of leaders have a high degree of trust in data analytics powered by AI within their own organizations. This lack of confidence even in their own AI-driven solutions underscores the significant trust gap that exists between decision-makers and technology in the current digital era.


Seven Finalists Vie for AI Health Outcomes Challenge

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The Centers for Medicare & Medicaid Services (CMS) announced seven finalists who will advance to the final round of the Artificial Intelligence Health Outcomes Challenge. This multi-stage competition launched last year with more than 300 entities proposing AI solutions for predicting patient health outcomes aimed at revolutionizing healthcare for potential use by the CMS Center for Medicare and Medicaid Innovation. In this last stage of the competition, the seven finalists will further develop algorithms that demonstrate how AI tools can be used to predict unplanned hospital and skilled nursing facility admissions and adverse events, and also will develop predictive algorithms for a standard target to be selected by CMS. CMS will announce the Grand Prize winner (who will receive up to $1 million in prize money) and Runner-Up (who will receive up to $230,000 in prize money) by the end of April 2021.