Without the cost of employee health benefits expected to rise 5.5% in 2018, cost containment is a top priority for health plan sponsors. However, simply shifting costs to employees through higher deductible and copays may not be a viable  option since affordability of medical coverage is a growing concern. One way to reduce expenses without further shifting costs to plan participants is to use patient health data to design extremely targeted-high-value medical benefit programs.  

Radiology departments face the same challenges as the rest of the healthcare market; they must transition to an environment driven by outcomes and value. Yet, this challenge is compounded for imaging departments, since they’re often perceived as a cost center instead of a profit center. One factor that hampers their ability to change, is that their tools—mainly the PACS—were designed to help them read more studies faster. And while maintaining a high reading volume remains important, it’s now accompanied by additional priorities including quality metrics, improved outcomes, patient satisfaction, and reduced costs.  To achieve these goals, radiologists need to streamline their workflows through advanced, modern tools. 

This paper describes both best practices and examples of ways in which workflow can deliver tangible value in these four areas. 

Radiology departments face the same challenges as the rest of the healthcare market; they must transition to an environment driven by outcomes and value. Yet, this challenge is compounded for imaging departments, since they’re often perceived as a cost center instead of a profit center. One factor that hampers their ability to change, is that their tools—mainly the PACS—were designed to help them read more studies faster. And while maintaining a high reading volume remains important, it’s now accompanied by additional priorities including quality metrics, improved outcomes, patient satisfaction, and reduced costs.  To achieve these goals, radiologists need to streamline their workflows through advanced, modern tools. 

This paper describes both best practices and examples of ways in which workflow can deliver tangible value in these four areas. 

There is nothing artificial about AI's impact of healthcare. It is driving improvements across the care continuum, in areas like cardiology, radiology and cancer treatment. Now, AI is poised to tackle the revenue cycle, and its potential to reimagine performance is nothing short of groundbreaking. 

Washington, DC – March 23, 2020 – Today, eHealth Initiative (eHI) sent a letter to Congress, signed by 21 leading digital health and health IT organizations, outlining support for certain provisions of the Coronavirus Aid, Relief, and Economic Security (CARES) Act, as well as additional steps to combat the COVID-19 pandemic.

“Digital health, telehealth, and remote monitoring technology can help providers effectively triage and treat COVID-19 patients,” says Jennifer Covich Bordenick, Chief Executive Officer of eHI. “There is an urgent need to remove regulations and help providers scale this technology quickly.”

The letter outlines support for telehealth provisions, including increased funding, as well as provisions related to the sharing of patient health information. It asks Congress to take additional steps to support telehealth, remote patient monitoring, expand broadband for rural areas, advance patient matching, fund artificial intelligence use and testing, and provide regulatory relief for providers and hospitals.

By aggregating and analyzing different types of data - such as claims and non-claims data - benefits managers can determine crucial correlations between preventive screenings, health outcomes, and healthcare costs. Thus, they can develop more targeted benefits packages that reduce costs while improving overall employee health and productivity. 

The cost of employee health benefits is on the rise. Employers and health plans are struggling to manage these increasing costs. This infographic helps explain the details of using data to design effective preventive screening programs. 

These charts show how fast COVID-19 is spreading — and what it takes to flatten the curve

As U.S. public officials, health-care workers and epidemiologists struggle to track the course of the coronavirus pandemic, they are being hampered by a dearth of data on exactly how far and how fast the virus is spreading.

Despite frequent updates by the news media, public health agencies and independent researchers tracking the outbreak, the available data represents only a portion of the total number of cases, many of which have gone unreported.

That lack of data in the U.S. is largely the result of delays in rolling out widespread testing in the early stages of the outbreak.

“Without knowing the extent and availability of testing, it is very hard to know what to make of the reported numbers,” said Yonatan Grad, a professor of Immunology and Infectious Diseases at the Harvard T.H. Chan School of Public Health. “For the U.S., when we see reports of numbers, they are best understood not as new cases but as identified cases where the true number of cases is unknown.”

But as testing becomes more widespread and the number of confirmed cases rises, a sharper picture is beginning to emerge of the pace of the spread of the virus.

To better track the speed of the pandemic’s spread, CNBC analyzed two months of data collected by researchers at Johns Hopkins University from multiple sources, including the World Health Organization, the U.S. Centers for Disease Control and Prevention, and various other national and local public health agencies around the world. The analysis looks at the pace of growth of new cases in U.S. states and in countries around the world beginning from time the outbreak began to accelerate. (To make that comparison, we adjusted each time series to start on the day each country or state began reporting more than 100 confirmed cases.)   

The full article from CNBC can be viewed at this link.  

Why we’re not overreacting to COVID-19, in one chart

The national mobilization against the coronavirus is now in full swing. Schools and workplaces nationwide have shuttered. The federal government has recommended that people not gather in groups of 10 or more. Social distancing and self-isolation are now becoming part of the fabric of daily American life.

This has all sparked a serious question among many people: Are we overreacting? It’s not just a question being asked by partiers and bar-goers — it has also been asked in the New York Times. A widely circulated article by Stanford’s John Ioannidis suggests that the stepped-up US response is a “fiasco in the making” that’s being made without enough data.

To someone who hasn’t been following the pandemic’s spread closely, the drastic measures indeed might seem like an overreaction. After all, around 25,000 cases and 300 deaths — as of March 21 — in a country of 330 million may not seem that bad. Is it really worth shutting down the economy, a measure that will of course have horrific costs of its own, for such a small toll?

But the numbers mask what’s really causing experts to worry: The coronavirus’s trajectory is putting us on a course of many, many more cases and many, many more deaths unless we do something drastic.

In other words, there’s a simple answer to the question: No, we’re not overreacting.

To explain why we’re not overreacting, we need to look to the experience of another country going through the coronavirus crisis: Italy. 

The full Vox article can be viewed at this link.  

Time for NIH to lead on data sharing

The U.S. National Institutes of Health (NIH), the largest global funder of biomedical research, is in the midst of digesting public comments toward finalizing a data sharing policy. Although the draft policy is generally supportive of data sharing (1), it needs strengthening if we are to collectively achieve a long-standing vision of open science built on the principles of findable, accessible, interoperable, and reusable (FAIR) (2) data sharing. Relying on investigators to voluntarily share data has not, thus far, led to widespread open science practices (3); thus, we suggest steps that NIH could take to lead on scientific data sharing, with an initial focus on clinical trial data sharing.

The full Science Magazine article can be downloaded below.