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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).
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.
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.
Healthcare is one of the most complex products our economy produces. Over the next 50 years, global health megatrends will change dramatically & we are headed to face increased risks of exposure to new, emerging and re-emerging diseases, new pandemics with surging globalisation, all putting a huge pressure on the healthcare system. Massive variations in health status, lack of access to quality health care, poor health outcomes and increasing cost of care are huge concerns globally. The Freaking future of healthcare pushes us to achieve a more intuitive, responsive, empathetic, cost effective and safer health systems. Only possible when the entire ecosystem & the stakeholders raise the collective expectations of how the system performs today.
This blog post was written by Dr. Maryam S. Jaffer, Director Data and Statistics, Emirates Health Services; Dr. Bashar Balish, Senior Director, Cerner; and Michel Ghorayeb, UAE Managing Director, SAS. The future of health care has never been more exciting. Artificial intelligence (AI) and data analytics have captured center stage for any business planning on surviving and thriving. Given the pace of technological development, AI is transforming the future on an unprecedented scale. And that includes the future of health care.
We examine the ability of a genetic algorithm to learn a predictive model that can estimate the likelihood that a physical therapist will receive annual Medicare payments above or below the industry median based on the physical therapist's practice parameters. We compare the performance of a canonical genetic algorithm and a self adaptive genetic algorithm with the performance of traditional logistic regression. Results show that both genetic algorithm approaches are competitive with logistic regression with the canonical genetic algorithm consistently outperforming logistic regression.
In this paper, we propose to use machine learning to automate Medicare fraud detection. By cross checking Medicare payment database and provider exclusion database, we build datasets with millions of service providers, including a handful of convicted fraudulent service providers. One essential challenge is that the dataset created is extremely imbalanced, making it extremely difficult to learn accurate classifiers for fraud detection. To tackle the challenge, we first use feature engineering to design effective features, by taking the difference between each service provider and its group cohort into consideration. At the instance level, we also use a synthetic instance generation approach to generate positive samples to alleviate the data imbalance challenge.
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.
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.