Nuance is a technology pioneer with market leadership in conversational AI and ambient intelligence, and a full-service partner of 77 percent of U.S. hospitals and trusted by over 500,000 physicians daily. Microsoft provides trusted and secure cloud and AI capabilities with the goal to empower people and organizations to address the complex challenges facing the healthcare industry today. With a long-term commitment to leveraging cloud and AI technologies to enhance patient engagement and outcomes, reduce clinician burnout, improve clinical quality and safety, and enhance financial performance, Nuance and Microsoft are leaders in the future-focused healthcare ecosystem and well-equipped to ensure The AI Collaborative members are at the front-end of education and learning on the evolution of AI in healthcare. "The key to successful healthcare innovation using AI is understanding at a deep level the problems that you're trying to solve and focusing on the outcomes you want to achieve," said Peter Durlach, Chief Strategy Officer of Nuance. "With the combined engineering, market and domain expertise of Nuance and Microsoft, The AI Collaborative can bring together multiple technical, business and clinical stakeholders to prioritize deployment of solutions for clinician burnout, patient engagement and health system financial stability, while accelerating innovation in precision medicine, drug discovery, clinical decision support and other promising use cases across the entire healthcare ecosystem."
Over the last two years, staffing shortages in healthcare have impacted many hospital and healthcare facility business operations, especially during emergency events like COVID-19. A resourceful approach is overdue, and artificial intelligence might have a part in ensuring the continuity of patient care and security by using various AI tools. For the past two years, the whole world is facing a harsh time due to Covid-19 and most of its effect comes on the healthcare industry. Doctors and healthcare frontlines are working never-ending shifts because the no. of patients is rising day by day which makes them also think about their career once in their lifetime. Many highly skilled healthcare professionals, who tend to be older, are choosing to retire rather than face the Covid-19 associated risks of working in a hospital.
It's 10 a.m. on a Monday, and Aman, one of the developers of a new artificial intelligence tool, is excited about the technology launching that day. Leaders of Duke University Hospital's intensive care unit had asked Aman and his colleagues to develop an AI tool to help prevent overcrowding in their unit. Research had shown that patients coming to the hospital with a particular type of heart attack did not require hospitalization in the ICU, and its leaders hoped that an AI tool would help emergency room clinicians identify these patients and refer them to noncritical care. This would both improve quality of care for patients and reduce unnecessary costs. Aman and his team of cardiologists, data scientists, computer scientists, and project managers had developed an AI tool that made it easy for clinicians to identify these patients.
Algorithms have always had some trouble getting things right--hence the fact that ads often follow you around the internet for something you've already purchased. But since COVID upended our lives, more of these algorithms have misfired, harming millions of Americans and widening existing financial and health disparities facing marginalized groups. At times, this was because we humans weren't using the algorithms correctly. More often it was because COVID changed life in a way that made the algorithms malfunction. Take, for instance, an algorithm used by dozens of hospitals in the U.S. to identify patients with sepsis--a life-threatening consequence of infection.
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.
Long Covid, with its constellation of symptoms, is proving a challenging moving target for researchers trying to conduct large studies of the syndrome. As they take aim, they're debating how to responsibly use growing piles of real-world data -- drawing from the full experiences of long Covid patients, not just their participation in stewarded clinical trials. "People have to really think carefully about what does this mean," said Zack Strasser, an internist at Massachusetts General Hospital who has used existing patient records to study the characteristics of long Covid. Is this not some artifact that's just happening because of the people that we're looking at within the electronic health record? One of the largest sources of real-world data on long Covid is a first-of-its-kind centralized federal database of electronic health records called the National Covid Cohort Collaborative, or N3C.
University of Pittsburgh School of Medicine data scientists and UPMC neurotrauma surgeons have created a prognostic model that uses automated brain scans and machine learning to inform outcomes in patients with severe traumatic brain injuries (TBI). Their findings are published in the journal Radiology, in a paper titled, "Outcome Prediction in Patients with Severe Traumatic Brain Injury Using Deep Learning from Head CT Scans." The researchers demonstrated that their advanced machine-learning algorithm can analyze brain scans and relevant clinical data from TBI patients to quickly and accurately predict survival and recovery six months after the injury. "Every day, in hospitals across the United States, care is withdrawn from patients who would have otherwise returned to independent living," said co-senior author David Okonkwo, MD, PhD, professor of neurological surgery at Pitt and UPMC. "The majority of people who survive a critical period in an acute care setting make a meaningful recovery--which further underscores the need to identify patients who are more likely to recover."
Oversight of AI is the board's job, regardless of the subject matter complexity. One of the most consequential challenges confronting corporate governance in the near term will be its ability to exercise informed oversight over the application of artificial intelligence ("AI") within its organization. It will be a challenge that will arise regardless of the industry sector in which the company operates, and regardless of how it applies AI in that operation. The essence of the challenge is the rapidly emerging conflict between the perceived societal and commercial benefits arising from AI implementation, and the perceived societal and institutional risks arising from its use. The need to address the challenge is urgent; the competing interests of benefit and risk are hurtling at each other at hypersonic speed.
We are excited to bring Transform 2022 back in-person July 19 and virtually July 20 - 28. Join AI and data leaders for insightful talks and exciting networking opportunities. Unstructured data is by its very nature difficult to wrangle. It is one of the hardest sources of data to manage, said Amy Brown, founder and CEO of B2B software-as-a-service (SaaS) startup Authenticx. "AI allows organization of this really messy data source," Brown said. Still, she said, "it takes a commitment and a desire to use that data source."
Background: Composition of tissue types within a wound is a useful indicator of its healing progression. Tissue composition is clinically used in wound healing tools (eg, Bates-Jensen Wound Assessment Tool) to assess risk and recommend treatment. However, wound tissue identification and the estimation of their relative composition is highly subjective. Consequently, incorrect assessments could be reported, leading to downstream impacts including inappropriate dressing selection, failure to identify wounds at risk of not healing, or failure to make appropriate referrals to specialists. Objective: This study aimed to measure inter- and intrarater variability in manual tissue segmentation and quantification among a cohort of wound care clinicians and determine if an objective assessment of tissue types (ie, size and amount) can be achieved using deep neural networks. Methods: A data set of 58 anonymized wound images of various types of chronic wounds from Swift Medical’s Wound Database was used to conduct the inter- and intrarater agreement study. The data set was split into 3 subsets with 50% overlap between subsets to measure intrarater agreement. In this study, 4 different tissue types (epithelial, granulation, slough, and eschar) within the wound bed were independently labeled by the 5 wound clinicians at 1-week intervals using a browser-based image annotation tool. In addition, 2 deep convolutional neural network architectures were developed for wound segmentation and tissue segmentation and were used in sequence in the workflow. These models were trained using 465,187 and 17,000 image-label pairs, respectively. This is the largest and most diverse reported data set used for training deep learning models for wound and wound tissue segmentation. The resulting models offer robust performance in diverse imaging conditions, are unbiased toward skin tones, and could execute in near real time on mobile devices. Results: A poor to moderate interrater agreement in identifying tissue types in chronic wound images was reported. A very poor Krippendorff α value of .014 for interrater variability when identifying epithelization was observed, whereas granulation was most consistently identified by the clinicians. The intrarater intraclass correlation (3,1), however, indicates that raters were relatively consistent when labeling the same image multiple times over a period. Our deep learning models achieved a mean intersection over union of 0.8644 and 0.7192 for wound and tissue segmentation, respectively. A cohort of wound clinicians, by consensus, rated 91% (53/58) of the tissue segmentation results to be between fair and good in terms of tissue identification and segmentation quality. Conclusions: The interrater agreement study validates that clinicians exhibit considerable variability when identifying and visually estimating wound tissue proportion. The proposed deep learning technique provides objective tissue identification and measurements to assist clinicians in documenting the wound more accurately and could have a significant impact on wound care when deployed at scale.