Pittsburgh Supercomputing Enables Transparent Medicare Outcome AI


Medical applications of AI are replete with promise, but stymied by opacity: with lives on the line, concerns over AI models' often-inscrutable reasoning – and as a result, possible biases embedded in those models – largely prevent scaled applications of AI for medical treatment, no matter how promising the underlying research. Recently, researchers from Mederrata Research (a nonprofit aiming to use data-driven techniques to preempt medical errors), Sound Prediction (a digital health informatics company aiming to create transparent AI models) and the NIH leveraged supercomputing at the Pittsburgh Supercomputing Center (PSC) to design a method for recreating the benefits of AI models in medicine with more explicability. The root of the team's approach is multilevel modeling (MLM, not to be confused with multilevel marketing). Through MLM, groups of similar cases are bundled and differential equations are used to identify a limited set of controlling factors for each case, allowing for easier – and more consistent – identification of the model's reasoning compared to post-hoc analyses of more opaque models. The researchers designed and applied the AI toward predicting – and explaining – readmission and death among Medicare patients following a hospital visit, training the model on three years of data (2009-2011) and testing it on a fourth (2012).

Fulltime D openings in Seattle, United States on August 29, 2022


Starting with our very first store on Ocean Avenue in San Francisco, opened almost 50 years ago by Doris and Don Fisher. The thread that's run through those five decades is the phenomenal people that make up our brand – our employees and our customers. People who are rooted in the Legacy that makes Gap what it is, but who are also focused on the future. People who want to leave the world better than they found it. We've built our brand on staying true to our roots while always being out in front of what's next. If you want to be part of an iconic American brand, and help lead the way for where we're headed, we'd love to have you join us About the Role* In this role, you will support the store leadership team by performing functional tasks as assigned. You will act as a mentor and role model to employees to support service behaviors and the execution of tasks in specific areas of expertise. You will focus on leading processes and/or areas of the business, executing tasks and maintaining productivity to ensure goals are met. Through collaboration with your leadership team, your goal is to teach and coach your team and drive behaviors to deliver a best-in-class customer experience What You'll Do* Serve as a role model to achieve priorities in store, with the customer as the primary focus Support the store leadership team to collaborate effectively with employees and ensure work tasks are completed in a timely and efficient manner Build and share expertise in an assigned specialized functional area Support completion or work processes before or after the store closes as needed inclusive of opening and/or closing the store Listen and ask questions to solicit feedback to understand needs and provide service Handle unique or complex customer interactions.Who You Are* Provides clear and direct communication of expectations and gives feedback Ability to utilize technology effectively and engage with customers and your team to meet goals Able to effectively lead and inspire others through coaching and mentoring Demonstrate interest and initiative towards continuous improvement and growth Research process or transaction flow to identify root cause of errors. One of the most competitive Paid Time Off plans in the industry.* Employees can take up to five "on the clock" hours each month to volunteer at a charity of their choice.

Geisinger Taps Cohere Health to Streamline Prior Authorizations


GHP provides coverage to more than half a million members throughout Pennsylvania and boasts a network of more than 30,000 primary and specialty care physicians. GHP will utilize Cohere's AI-driven and machine learning technology to accelerate patient access to high-quality care while streamlining prior authorization. Cohere's digital platform creates episode-specific care paths based on the member's utilization history and individual care needs, in addition to historic results for a specific patient population. Fully transparent, evidence-based suggestions prompt providers to select high-value care options before requests are submitted, reducing peer-to-peer clinical reviews and denials. Initial clinical improvement programs will focus on increasing patient access to conservative and home-based treatments.

Algorithms in Medicine: Where They Help … and Where They Don't


Walter Bradley Center director Robert J. Marks continued his podcast discussion with anesthesiologist Richard Hurley in "Good and bad algorithms in the practice of medicine" (May 19, 2022). An algorithm is "a procedure for solving a mathematical problem (as of finding the greatest common divisor) in a finite number of steps that frequently involves repetition of an operation." Algorithms, Dr. Marks points out, can either sharpen or derail services, depending on their content. Before we get started: Note: Robert J. Marks, a Distinguished Professor of Computer and Electrical Engineering, Engineering at Baylor University, has a new book, coming out Non-Computable You (June, 2022), on the need for realism in another area as well -- the capabilities of artificial intelligence. This portion begins at 01:59 min.

Machine Learning May Help Predict Opioid Overdoses in Medicaid Patients


A new study shows that a machine-learning model using state Medicaid data may accurately predict opioid overdose in beneficiaries.

AI in health and medicine - Nature Medicine


Artificial intelligence (AI) is poised to broadly reshape medicine, potentially improving the experiences of both clinicians and patients. We discuss key findings from a 2-year weekly effort to track and share key developments in medical AI. We cover prospective studies and advances in medical image analysis, which have reduced the gap between research and deployment. We also address several promising avenues for novel medical AI research, including non-image data sources, unconventional problem formulations and human–AI collaboration. Finally, we consider serious technical and ethical challenges in issues spanning from data scarcity to racial bias. As these challenges are addressed, AI’s potential may be realized, making healthcare more accurate, efficient and accessible for patients worldwide. AI has the potential to reshape medicine and make healthcare more accurate, efficient and accessible; this Review discusses recent progress, opportunities and challenges toward achieving this goal.

Using Machine Learning in the Evolving Landscape of Real-World Data


According to the Food and Drug Administration (FDA), the term real-world data (RWD) refers to routinely collected data relating to patient health status and the delivery of healthcare services, and real-world evidence (RWE) is the clinical evidence regarding the usage and potential benefits or risks of a medical product derived from the analysis of RWD. Both RWD and RWE have increasingly attracted attention in the healthcare industry for years now, and rightly so, considering that the healthcare analytics market is expected to expand at a compound annual growth rate of 28.9% between now and 2026. There's no doubt that within this massive data trove, there exist countless insights that could streamline care delivery, help physicians diagnose disease faster, and improve treatment strategies – if only we could identify them. This data revolution we are experiencing in the healthcare industry necessitates the appropriate tools and approaches to work with higher dimensional data sources to truly harvest the insights buried in RWD. Machine learning, an area of artificial intelligence (AI) consisting of a collection of methodologies that focus on algorithmically learning efficient representations of data and extracting insights from data, offers promise and has consistently been gaining traction within the industry in the context of RWD.

Convolutional generative adversarial imputation networks for spatio-temporal missing data in storm surge simulations Artificial Intelligence

Imputation of missing data is a task that plays a vital role in a number of engineering and science applications. Often such missing data arise in experimental observations from limitations of sensors or post-processing transformation errors. Other times they arise from numerical and algorithmic constraints in computer simulations. One such instance and the application emphasis of this paper are numerical simulations of storm surge. The simulation data corresponds to time-series surge predictions over a number of save points within the geographic domain of interest, creating a spatio-temporal imputation problem where the surge points are heavily correlated spatially and temporally, and the missing values regions are structurally distributed at random. Very recently, machine learning techniques such as neural network methods have been developed and employed for missing data imputation tasks. Generative Adversarial Nets (GANs) and GAN-based techniques have particularly attracted attention as unsupervised machine learning methods. In this study, the Generative Adversarial Imputation Nets (GAIN) performance is improved by applying convolutional neural networks instead of fully connected layers to better capture the correlation of data and promote learning from the adjacent surge points. Another adjustment to the method needed specifically for the studied data is to consider the coordinates of the points as additional features to provide the model more information through the convolutional layers. We name our proposed method as Convolutional Generative Adversarial Imputation Nets (Conv-GAIN). The proposed method's performance by considering the improvements and adaptations required for the storm surge data is assessed and compared to the original GAIN and a few other techniques. The results show that Conv-GAIN has better performance than the alternative methods on the studied data.

TD Pilot will let people with disabilities control iPads with their eyes


There's plenty new in iPadOS 15, but it also features an under-sung accessibility upgrade: support for third-party eye-tracking devices. That'll allow people with disabilities to use iPad apps and speech generation software simply through eye movements -- no touchscreen interaction required. Tobii Dynavox, the assistive tech division of the eye-tracking company Tobii, worked with Apple for years to help make that happen. And now, the firm is ready to announce TD Pilot, a device that aims to bring the iPad experience to the estimated 50 million people globally who need communication assistance. The TD Pilot is basically a super-powered frame for Apple's tablets: It can fit in something as big as the iPad Pro 12.9-inch, and it also packs in large speakers, an extended battery and a wheelchair mount.

Measuring Outcomes in Healthcare Economics using Artificial Intelligence: with Application to Resource Management Artificial Intelligence

The quality of service in healthcare is constantly challenged by outlier events such as pandemics (i.e. Covid-19) and natural disasters (such as hurricanes and earthquakes). In most cases, such events lead to critical uncertainties in decision making, as well as in multiple medical and economic aspects at a hospital. External (geographic) or internal factors (medical and managerial), lead to shifts in planning and budgeting, but most importantly, reduces confidence in conventional processes. In some cases, support from other hospitals proves necessary, which exacerbates the planning aspect. This manuscript presents three data-driven methods that provide data-driven indicators to help healthcare managers organize their economics and identify the most optimum plan for resources allocation and sharing. Conventional decision-making methods fall short in recommending validated policies for managers. Using reinforcement learning, genetic algorithms, traveling salesman, and clustering, we experimented with different healthcare variables and presented tools and outcomes that could be applied at health institutes. Experiments are performed; the results are recorded, evaluated, and presented.