In the rapidly evolving field of healthcare management, the analysis of medical claims data has become an essential component for improving the quality and equity of healthcare services. The nature of care delivery in the United states is heavily influenced by its fragmentation--care is often spread across multiple disconnected providers (e.g., primary-care physicians, specialists). Settings with greater care fragmentation have been shown to inhibit effective communication and coordination between care team members, thus contributing to higher costs and lower quality of treatment [13,33,21,1,7]. Despite the well-understood impacts of fragmentation, there are still few quantitative tools that can capture the mechanisms of care delivery networks at scale [14]. Standard analyses of local infrastructure features, often executed using tabular data, are limited in their ability to distill complex dynamics between physicians.

Consumer ultrasound is a rapidly growing field with immense potential. Hand-held devices that utilize ultrasound technology are becoming increasingly available and affordable, allowing people to take control of their health care in new and empowering ways. Medical professionals are taking notice of the potential of consumer ultrasound. A recent study found that 41 percent of surveyed physicians believe that hand-held ultrasound will be an important part of medical care in the future. The study also found that nearly half of physicians believe that consumer ultrasound will help to improve patient safety.

From the NEJM Catalyst event AI and Machine Learning for Health Care Delivery, sponsored by Advisory Board, March 24, 2022. In the special artificial intelligence theme issue of NEJM Catalyst Innovations in Care Delivery, "Preventing Delayed and Missed Care by Applying Artificial Intelligence to Trigger Radiology Imaging Follow-up" explores a Northwestern Medicine initiative that uses recurrent neural networks and natural language processing to examine radiology reports for findings necessitating follow-up. Speaking at the NEJM Catalyst "AI and Machine Learning for Health Care Delivery" event, senior author Mozziyar Etemadi, MD, PhD, describes the In Depth article. Most people outside of health care associate radiology with images from X-rays, CT scans, and MRIs. But to doctors who are not radiologists, what comes to mind are large blocks of text from radiology reports, which can be a lot to parse through, Etemadi says.

From the NEJM Catalyst event AI and Machine Learning for Health Care Delivery, sponsored by Advisory Board, March 24, 2022. In the special artificial intelligence theme issue of NEJM Catalyst Innovations in Care Delivery, "Using AI to Empower Collaborative Team Workflows: Two Implementations for Advance Care Planning and Care Escalation" compares AI implementations for improving the rate of advanced care planning and earlier prediction of clinical deterioration. Speaking at the NEJM Catalyst "AI and Machine Learning for Health Care Delivery" event, first author Ron C. Li, MD, describes the care escalation intervention and key takeaways from the case study. "Our work starts with a foundational premise: that we need to change how we think about AI in health care," says Li. Instead of starting with a machine learning model and then deciding how to deploy it, Li says that health care should start with a problem and think about AI not as the solution, but as a capability that enables a broader set of solutions. AI will not replace humans in health care, but empower them.

From the NEJM Catalyst event AI and Machine Learning for Health Care Delivery, sponsored by Advisory Board, March 24, 2022. In the special artificial intelligence theme issue of NEJM Catalyst Innovations in Care Delivery, "How Health Systems Decide to Use Artificial Intelligence for Clinical Decision Support" explores how health systems decide which AI products to use. Speaking at the NEJM Catalyst "AI and Machine Learning for Health Care Delivery" event, senior author Christina Silcox, PhD, shares best practices for choosing health care AI tools. Potential for AI in the health space is enormous, from population health to individual health, health system administration, and biomedical innovation. Silcox and fellow researchers at the Duke-Margolis Center for Health Policy focused on how health systems choose which specific population and individual health tools to use.

Health care information technology company Netsmart has purchased Remarkable Health, a Chandler, Arizona-based provider of artificial intelligence (AI) technology and software solutions for organizations focused on behavioral health and individuals with intellectual and developmental disabilities (I/DD). Terms of deal, which was announced Thursday, were not disclosed. Remarkable Health's products include CT One -- a management platform for behavioral health claims and records -- and Bells, a notetaking documentation solution for behavioral health clinical staff. Remarkable Health's products will complement that of Netsmart's CareFabric platform, an operating system that includes resources such as electronic health records and management tools, and which are utilized by providers like those in behavioral health, addiction treatment and autism care. Remarkable Health claims that the Bells platform helps reduce time spent on clinical documentation by over 50%, enabling organizations to serve six more clients per month.

While the pandemic is still raging, the chaos of the past 18 months has calmed a bit, and the dust is starting to settle. Now the time has come for healthcare CIOs and other health IT leaders to look forward and plan their IT investments – shaped, in no small part, by the lessons of the recent past. According to new research from HIMSS Media, the average overall 2021 IT budget is nearly $13 million, with 15% on average being allocated to IT security. While that may be a lot of money, there are many technological areas yearning for more investment. Today, Healthcare IT News launches a new feature article series, Health IT Investment: The Next Five Years.

The adoption of AI in health care is being driven by an exponential growth of health data, the broad availability of computational power, and foundational advances in machine learning techniques. AI has already demonstrated the potential to create value by reducing costs, expanding access, and improving quality. But in order for AI to realize its transformative potential at scale, its proponents need business models optimized to best capture that value. AI changes the rules of business and, as ever, there are some unique considerations in health care. In order to understand these, we studied AI across 15 sets of use cases. These span five domains of health care (patient engagement, care delivery, population health, R&D, and administration) and cover three types of functions (measure, decide, and execute).

As the era of COVID-19 persists, healthcare systems are facing a daunting range of challenges. Healthcare faculties thrive for a sustainable emergency management framework to look beyond COVID-19 and future contingency. The scientific perception of COVID-19 has been ceaselessly expanding since its outbreak. Worldwide contamination and disease progression rates, successful treatment, and speculation over potential antidote are continually fluctuating. The combination of unpredictable patterns and inadequate insight means that preparing for and executing both an instant and viable long-term pandemic response is baffling--yet undoubtedly critical for the care of patients, employees, and organizational survival. Researchers and provider organizations have increasingly embraced artificial intelligence (AI) and machine learning (ML) tools to reduce and track the spread of COVID-19 and to improve their surveillance efforts.

Researchers and provider organisations have increasingly embraced artificial intelligence (AI) and machine learning (ML) tools to reduce and track the spread of COVID-19 and to improve their surveillance efforts. Big data analytics systems have helped health experts to stay ahead of the pandemic from predicting patient outcomes to anticipating future hotspots, resulting in more efficient care delivery. However, the level of pandemic preparation by healthcare organisations is only as good as the data available to them. Although the industry is well aware of the data issues, the COVID-19 pandemic has brought a host of unique challenges to the forefront of care delivery. Nature of the SARS-CoV-2 has led to significant gaps in COVID-19 data with inconsistencies in information, leaving officials uncertain of the effectiveness of public health interventions.