These are the main findings of a new report from Deloitte Consulting on future business models that will reshape the form and function of hospitals around the world. Researchers surveyed health, technology, and customer experience workers in mid-January, just before the spread and severity of the coronavirus was apparent, and then interviewed a handful of experts in April. The resulting feedback showed that people expect hospital changes to accelerate. "The timeline is significantly shortened in terms of when people thought these changes would take place," says Kulleni Gebreyes, a principal at Deloitte and a coauthor of the report. Here are a few ways experts think hospitals and the hospital experience will change in a post-pandemic world.
BATAVIA -- The first robotically assisted knee replacement surgery at Rochester Regional Health United Memorial Medical Center (UMMC) has been performed and there are more coming up soon.The first procedure was done Friday by Dr. Matthew Landfried, chief of orthopedic surgery for UMMC and a Genesee Orthopaedics surgeon. It was one of two such procedures he did that day with the help of a Robotic Surgical Assistant (ROSA) Knee System."The That hopefully lets people know this hospital (UMMC) is on par with the city hospitals. Our capability in orthopedics are equal to the city hospitals. The fact that the system chose me to start it is sending a message that hospitals out here are capable of the same types of procedures … as the city hospitals. People don't have to travel to get that kind of quality. I was honored to be picked to do the first one in the (RRH) system."ROSA
Discussions about the application of artificial intelligence (AI) in healthcare often span multiple areas, most commonly about making more accurate diagnoses, identifying at-risk populations, and better understanding how individual patients will respond to medicines and treatment protocols. To date, there has been relatively little discussion about practical applications of AI to improve medication management across the care continuum, an area this article will address. What's the first thing that comes to mind when someone mentions prescription drugs in the United States? In poll after poll, the high and rising costs of medications are American voters' top healthcare-related issue. This concern is well founded.
Careline Health Group, a healthcare organization that provides Hospice and Physician Service care for families and patients who face serious or terminal illness, has implemented Muse Healthcare's machine learning and predictive modeling tools to meet the needs of their patients. The Muse technology evaluates and models every clinical assessment, medication, vital sign and other relevant data to perform a risk stratification of these patients. The tool then highlights the patients with the most critical needs and visually alerts the agency to perform additional care. It also makes accurate changes to the care plans based on the condition and location of the patient (LTC, SNF or in home). According to Careline Health Group's Chief Executive Officer, Joe Mead, data from Muse provides meaningful insights for their patients.
A new whitepaper coauthored by researchers at the Vector Institute for Artificial Intelligence examines the ethics of AI in surgery, making the case that surgery and AI carry similar expectations but diverge with respect to ethical understanding. Surgeons are faced with moral and ethical dilemmas as a matter of course, the paper points out, whereas ethical frameworks in AI have arguably only begun to take shape. In surgery, AI applications are largely confined to machines performing tasks controlled entirely by surgeons. AI might also be used in a clinical decision support system, and in these circumstances, the burden of responsibility falls on the human designers of the machine or AI system, the coauthors argue. Privacy is a foremost ethical concern. AI learns to make predictions from large data sets -- specifically patient data, in the case of surgical systems -- and it's often described as being at odds with privacy-preserving practices.
To answer medical questions that can be applied to a wide patient population, machine learning models rely on large, diverse datasets from a variety of institutions. However, health systems and hospitals are often resistant to sharing patient data, due to legal, privacy, and cultural challenges. An emerging technique called federated learning is a solution to this dilemma, according to a study published Tuesday in the journal Scientific Reports, led by senior author Spyridon Bakas, Ph.D., an instructor of Radiology and Pathology & Laboratory Medicine in the Perelman School of Medicine at the University of Pennsylvania. Federated learning--an approach first implemented by Google for keyboards' autocorrect functionality--trains an algorithm across multiple decentralized devices or servers holding local data samples, without exchanging them. While the approach could potentially be used to answer many different medical questions, Penn Medicine researchers have shown that federated learning is successful specifically in the context of brain imaging, by being able to analyze magnetic resonance imaging (MRI) scans of brain tumor patients and distinguish healthy brain tissue from cancerous regions.
As the virus continues to spread quickly in many parts of the world, the demand for touchless and contactless services have grown and have become increasingly popular. One point of caution many people find them selves at is in hospitals or other healthcare facilities as they are filled with at-risk people or even coronavirus patients. It is perfectly understandable to feel a sense of caution in any place filled with people, especially healthcare facilities in this day in age. The recent events that have unfolded have prompted some healthcare centers to launch remote doctor appointments for much of the general public, especially the elderly. As the pandemic continues to exploit vulnerabilities in healthcare systems around the world, do these contactless practices paint the future of a new normal?
Wikipedia defines artificial intelligence in healthcare as the use of complex algorithms and software to emulate human cognition in the analysis, interpretation and comprehension of complicated medical and healthcare data. This "emulation" is done in less time and at a fraction of the cost. Artificial intelligence in healthcare was valued at about $600 million in 2014 and is projected to reach $150 billion by 2026. Reinventing and reinvigorating healthcare through the use of artificial intelligence is happening predominantly through assisting in better diagnosis, better processes, drug development and robot-assisted surgery. In 2015 misdiagnosing illness and medical error accounted for 10% of all U.S. deaths.
MCG Health, part of the Hearst Health network, announces it has successfully piloted its new machine learning solution, Indicia for Effective Focus, and it is now available for licensing. MCG is partnering with five major hospital systems for continued development and enhancement of this solution: Avera McKennan Hospital, Baptist Health System, Erlanger Health System, Franciscan Alliance, and IU Health. Indicia for Effective Focus prioritizes utilization management worklists based on the probability of appropriate patient placement, as well as the potential negative financial impact due to untimely decision making. The platform leverages MCG's extensive clinical evidence base, real-time data from the EHR (electronic health record), and machine learning technology to guide the case prioritization. Crissa Mulkey, Director of Utilization Management at IU Health said, "Indicia for Effective Focus shows an intuitive understanding of UM users, as well as how UM, or utilization review, is performed. It is a refreshing change."
Since Symphony Care Network's introduction to DataRobot in 2016, the Illinois-based transitional care and assisted living provider has used the automated machine learning platform for a number of applications, including one that uses data on falls to predict readmission cases. After seeing research indicating that if certain patients fall, they are likely to fall again, Taylor and his team built a model with DataRobot that can help predict if a patient is expected to fall. The model incorporates data on the type of medication a patient is on and if the drug makes a patient more susceptible to falls, such as by making them dizzy. It also takes in data on patients' conditions and whether a patient has fallen before, among other data. The DataRobot-built model can mine patient data and identify patients who are likely to fall.