Artificial intelligence is being deployed in many different areas. Within higher education, it is used for college admissions and financial aid decisions. Health researchers employ it to scan the scientific literature for chemical compounds that may generate new medical treatments. E-commerce sites deploy algorithms to make product recommendations for consumers based on their areas of interest.1 But one of the most important growth areas lies in finance and operations. Both public and private sector organizations have large budgets to manage and it is important to operate efficiently and effectively. Accusations of budget inefficiencies or wasteful spending decrease public confidence and make it important to figure out how to manage resources in fair ways. To help with budgetary oversight, AI is being used for financial management and fraud detection. Advanced algorithms can spot abnormalities and outliers that can be referred to human investigators to determine if fraud actually has taken place. It is a way to use technology to improve budget audits, personnel performance, and organizational activities. Yet is it crucial to overcome several problems that plague public sector innovation: procurement obstacles, insufficiently trained workers, data limitations, a lack of technical standards, cultural barriers to organizational change, and making sure anti-fraud applications adhere to responsible AI principles.
The World Health Organization issued its first global report on artificial intelligence in late June, highlighting concerns of algorithmic bias in health care applications of AI. It accompanies a growing number of news stories exposing AI's shortfalls. AI has come of age through the alchemy of cheap parallel (cloud) computing combined with the availability of big data and better algorithms. Problems that seemed unconquerable a few years ago are being solved, at times with startling gains -- think instant language translation capabilities, self-driving cars, and human-like robots. AI's arrival to health care, however, has been markedly slower.
AWS just announced the General Availability of Amazon HealthLake, a HIPAA-eligible service for healthcare providers, health insurance companies, and pharmaceutical companies to securely store, transform, query, analyze, and share health data in the cloud at petabyte scale. We believe that the combination of the innovation trends in healthcare (such as reimbursement models around data-driven evidence), standardization around interoperability (such as federal and global incentives and mandates in adopting the Fast Healthcare Interoperability Resources standard, or FHIR), and the advancement of scientific methods (such as with deep learning) enable our healthcare and life sciences (HCLS) customers to improve clinical and research efforts. Over the past decade, we've witnessed a digital transformation with healthcare organizations capturing huge volumes of patient information in electronic medical records (EMRs) every day, making the medical record a source of big data containing information regarding sociodemographics, medical conditions, genetics, and treatments. Making sense of all this data provides the biggest opportunity to transform care by tailoring disease treatment and prevention to individuals and populations. This so-called precision medicine takes into account the individual variability in genes, environment, and lifestyle for each individual.
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.
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.
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.
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.
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.
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.