Based on the professional literature available, there are some inconvenient truths about Covid-19 that are not always considered in the chorus of confusion that exists today. Here we summarize what is known, what has already happened, and what is to be expected based on the analysis of the data and the epidemiological models.

Background

An analysis of the first 425 laboratory-identified cases of a novel coronavirus infected pneumonia (Covid-19) is presented by Qun Li, et.al.¹. The first cases were identified at Wuhan hospitals as a "pneumonia of unknown etiology" when the patients met the following criteria: fever in excess of 100.4°F, radiographic evidence of pneumonia, low or normal white-cell count or low lymphocyte count, and no symptomatic improvement after antimicrobial treatment for 3 to 5 days according to standard clinical guidelines. On Jan. 7, 2020, the outbreak was confirmed as a new coronavirus infection².

‘That escalated quickly!” is a common trope used in popular culture to describe when a situation gets out of hand before you’ve even had a chance to think about it. We don’t often use this trope in medicine, but I can think of nothing better to describe what has been going on in the United States with the coronavirus outbreak.

I am a physician scientist who practices infectious disease medicine and runs a research laboratory that specializes in viruses. I spend much of my time directing a clinical microbiology laboratory for a large academic medical center. If you’ve ever had a doctor tell you that they are going to test you for a virus, it’s teams like mine that develop and run that test.

When I first heard about the coronavirus outbreak in China, I had no idea I would soon be on the front lines of dealing with this outbreak.

Imagine you’re a fossil hunter. You spend months in the heat of Arizona digging up bones only to find that what you’ve uncovered is from a previously discovered dinosaur.

That’s how the search for antibiotics has panned out recently. The relatively few antibiotic hunters out there keep finding the same types of antibiotics.

With the rapid rise in drug resistance in many pathogens, new antibiotics are desperately needed. It may be only a matter of time before a wound or scratch becomes life-threatening. Yet few new antibiotics have entered the market of late, and even these are just minor variants of old antibiotics.

Although the prospects look bleak, the recent revolution in artificial intelligence (AI) offers new hope. In a study published in February 2020 in the journal Cell, scientists from MIT and Harvard used a type of AI called deep learning to discover new antibiotics.

Biopharmaceuticals, also known as biological drugs or biologics, are manufactured from living organisms, or contain living organisms that have been genetically engineered to prevent or treat diseases. Biologics are chemically and structurally complex, and often highly heterogeneous; therefore, controlling and maintaining quality remains a challenge. The potential for new therapeutics to cure and treat previously untreatable diseases is enormous, but there is still a long way to go before they can be manufactured at the required scale, with predictive control of quality, and at a lower cost. NIST’s Vijay Srinivasan and Sheng Lin-Gibson discuss their recent paper on some of the challenges and solutions associated with manufacturing these life-saving drugs.

Given the complexities of healthcare, do basic statistics used to rank hospitals really work well? A study co-authored by MIT economists indicates that some fundamental metrics do, in fact, provide real insight about hospital quality.

“The results suggest a substantial improvement in health if you go to a hospital where the quality scores are higher,” says Joseph Doyle, an MIT economist and co-author of a new paper detailing the study’s results.

The study was designed to work around a difficult problem in evaluating hospital quality: Some high-performing hospitals may receive an above-average number of very sick patients. Accepting those difficult cases could, on the surface, worsen the aggregate outcomes of a given hospital’s patients and make such hospitals seem less effective than they are.

However, the scholars found a way to study equivalent pools of patients, thus allowing them to judge the hospitals in level terms. Overall, the study shows, when patient sickness levels are accounted for, hospitals that score well on quality measures have 30-day readmission rates that are 15 percent lower than a set of lesser-rated hospitals, and 30-day mortality rates that are 17 percent lower.

After a patient has a heart attack or stroke, doctors often use risk models to help guide their treatment. These models can calculate a patient’s risk of dying based on factors such as the patient’s age, symptoms, and other characteristics.

While these models are useful in most cases, they do not make accurate predictions for many patients, which can lead doctors to choose ineffective or unnecessarily risky treatments for some patients.

“Every risk model is evaluated on some dataset of patients, and even if it has high accuracy, it is never 100-percent accurate in practice,” says Collin Stultz, a professor of electrical engineering and computer science at MIT and a cardiologist at Massachusetts General Hospital. “There are going to be some patients for which the model will get the wrong answer, and that can be disastrous.”

Stultz and his colleagues from MIT, IBM Research, and the University of Massachusetts Medical School have now developed a method that allows them to determine whether a particular model’s results can be trusted for a given patient. This could help guide doctors to choose better treatments for those patients, the researchers say.

Biopharmaceutical manufacturing uses living cells to produce therapies that treat diseases like cancer, diabetes, and autoimmune disorders. Manufacturing medicine using biology presents different challenges from the traditional chemical manufacturing processes that stamp out identical pressed pills.

Biomanufacturing processes are hard to control, and the products are difficult to define as “identical” from batch to batch. Despite these challenges, biopharmaceuticals are critical to public health because the advantages are significantly greater. Scientific understanding of diseases and the success of biologically manufactured therapies to treat them has increased dramatically. But it can take a decade from design to full production of a biopharmaceutical—not fast enough to meet the needs of all the patients, or to beat competition from overseas.

Legislative support is growing for the reimbursement of care delivery via telemedicine. The Centers for Medicare and Medicaid Services (CMS) and the Office of Inspector General have recently made final and proposed rule changes to stimulate greater use and access for telemedicine delivery. These changes mean that for healthcare providers all around the United States, telemedicine will become a greater strategic focus.

Three major areas of telemedicine affected are remote patient monitoring (RPM) services, chronic care management (CCM), and opioid use disorder (OUD) treatment. Here we highlight the most significant changes that will impact providers in 2020 and beyond.

Remote patient monitoring (RPM)

Until recently, this contributing technology for telemedicine has been hampered by murky details within existing law. It is now, however, the area of the industry experiencing the most significant changes in recent rulemaking.

As usual with Quality Digest’s diverse audience, this year’s top stories covered a wide range of topics applicable to quality professionals. From hardware to software, from standards to risk management, from China trade to FDA regulations. It’s always fun to see what readers gravitate to, and this year was no different.

Below are five articles that garnered a lot of interest from our readers. As you can see, the topics are quite diverse.

Improve Risk Management and Quality Across the Value Chain by Increasing Visibility
by Kelly Kuchinski

Eric, a 40-something married father of three, runs a successful startup. Given his demanding career, he and his wife decided she would be a stay-at-home mum. Eric believed the attention he devoted to his family was adequate, and that he had fully harmonized his work as CEO and life as a family man.

On a recent family trip, Eric continued working as much as he could, as he always did. While taking a conference call, he dropped his phone and, without hesitation, leapt to catch it at the risk of hurting himself. Seeing this, his 13-year-old son blurted out, “I don’t know if you would have jumped after me like that.” Only then did Eric realize that his son didn't think he prioritized their family. Eric had been oblivious that his family felt neglected; he had been unaware or was in denial.