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The Data Dilemma and Its Impact on AI in Healthcare and Life Sciences

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There is no greater challenge for healthcare and life science organizations than ensuring that their digital transformation along with better data management will improve patient outcomes, increase operational efficiency and productivity, and better financial results. The drivers of healthcare and life science's transition from data rich to data driven are not new and include the race to manage cost and improve quality. Some new drivers include the growth of at risk contracting for providers, the threat of care delivery disruption by the retail industry and the impact of drug discovery in the challenge to balance speed to market with costs. Health and life science industries are data rich. IDC estimates that on average, approximately 270 GB of healthcare and life science data will be created for every person in the world in 2020. Transformation of data into insights creates the value for health and life science organizations coupled with organizations establishing a data driven culture.


Who Will Pay for AI?

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In 2020, the largest U.S. health care payer, the Centers for Medicare & Medicaid Services (CMS), established payment for artificial intelligence (AI) through two different systems in the Medicare Physician Fee Schedule (MPFS) and the Inpatient Prospective Payment System (IPPS). Within the MPFS, a new Current Procedural Terminology code was valued for an AI tool for diagnosis of diabetic retinopathy, IDx-RX. In the IPPS, Medicare established a New Technology Add-on Payment for Viz.ai software, an AI algorithm that facilitates diagnosis and treatment of large-vessel occlusion strokes. This article describes reimbursement in these two payment systems and proposes future payment pathways for AI.


How AI Can Decrease Drug-related Health Equity Challenges - insideBIGDATA

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Surveyor Health Corporation launched the SurveyorAI platform, a patented technology that drives efficiency and affordability for population medication management using drug knowledge from FDB (First Databank). The company also released the results of a Stanford-guided two-year study and peer reviewed research in the Journal of Managed Care & Specialty Pharmacy. The study validates the platform's ability to enhance remote medication management while lowering costs, reducing utilization, and improving health. The results of the study are based on a pilot conducted with IEHP, Inland Empire Health Plan, one of the top 10 largest Medicaid health plans and the largest not-for-profit Medicare-Medicaid plan in the country. "Adverse drug reactions result in 275,689 deaths and cost more than $528 billion per year*," said Erick Von Schweber, co-founder of Surveyor Health Corporation.


AI Healthcare Company Offers Software as a Medical Device

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It's probably no surprise that money is pouring into life sciences and healthcare startups during the biggest medical crisis in a century. CB Insights reported that global healthcare funding hit a new record $31.6 billion in this first quarter of 2021. It's also no shock that the two biggest trends – artificial intelligence and telehealth – also reaped record amounts of private cash. AI healthcare startups raised nearly $2.5 billion, while telehealth companies did even better by netting $4.2 billion in equity funding. That's the third consecutive quarter to hit record highs in both sectors dating back to Q3'20.


'Care bots' are on the rise and replacing human caregivers

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If you Google "care bots", you'll see an army of robot butlers and nurses, taking vital signs in hospitals, handing red roses to patients, serving juice to the elderly. For the most part these are just sci-fi fantasies. The care bots that already exist come in a different guise. These care bots look less like robots and more like invisible pieces of code, webcams and algorithms. They can control who gets what test at the doctor's office or how many care hours are received by a person on Medicaid. Increasingly, human caregivers work through and alongside automated systems that set forth recommendations, manage and surveil their labor, and allocate resources.


Explainable Health Risk Predictor with Transformer-based Medicare Claim Encoder

arXiv.org Artificial Intelligence

In 2019, The Centers for Medicare and Medicaid Services (CMS) launched an Artificial Intelligence (AI) Health Outcomes Challenge seeking solutions to predict risk in value-based care for incorporation into CMS Innovation Center payment and service delivery models. Recently, modern language models have played key roles in a number of health related tasks. This paper presents, to the best of our knowledge, the first application of these models to patient readmission prediction. To facilitate this, we create a dataset of 1.2 million medical history samples derived from the Limited Dataset (LDS) issued by CMS. Moreover, we propose a comprehensive modeling solution centered on a deep learning framework for this data. To demonstrate the framework, we train an attention-based Transformer to learn Medicare semantics in support of performing downstream prediction tasks thereby achieving 0.91 AUC and 0.91 recall on readmission classification. We also introduce a novel data pre-processing pipeline and discuss pertinent deployment considerations surrounding model explainability and bias.


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.


New study examines mortality costs of air pollution in US

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A team of University of Illinois researchers estimated the mortality costs associated with air pollution in the U.S. by developing and applying a novel machine learning-based method to estimate the life-years lost and cost associated with air pollution exposure. Scholars from the Gies College of Business at Illinois studied the causal effects of acute fine particulate matter exposure on mortality, health care use and medical costs among older Americans through Medicare data and a unique way of measuring air pollution via changes in local wind direction. The researchers--Tatyana Deryugina, Nolan Miller, David Molitor and Julian Reif--calculated that the reduction in particulate matter experienced between 1999-2013 resulted in elderly mortality reductions worth $24 billion annually by the end of that period. Garth Heutel of Georgia State University and the National Bureau of Economic Research was a co-author of the paper. "Our goal with this paper was to quantify the costs of air pollution on mortality in a particularly vulnerable population: the elderly," said Deryugina, a professor of finance who studies the health effects and distributional impact of air pollution.


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.