Amazon’s app-based employee healthcare service could be first step toward retailer becoming a disruptive force in healthcare; federal VA develops its own mHealth apps
More consumers are using smartphone applications (apps) to manage different aspects of their healthcare. That fact should put clinical laboratories and anatomic pathology groups on the alert, because a passive “wait and see” strategy for making relevant services and lab test information available via mobile apps could cause patients to choose other labs that do offer such services.
Patient use of apps to manage healthcare is an important trend. In January, Dark Daily covered online retail giant Amazon’s move to position itself as a leader in smartphone app-based healthcare with its launch of Amazon Care, a virtual medical clinic and homecare services program. At that time, the program was being piloted for Seattle-based employees and their families only. Since then, it has been expanded to include eligible Amazon employees throughout Washington State.
Mobile health (mHealth) apps are giving healthcare providers rapid access to patient information. And healthcare consumers are increasingly turning to their mobile devices for 24/7 access to medical records, clinical laboratory test results, management of chronic conditions, and quick appointment scheduling and prescription refills.
Thus, hearing ‘There’s an app for that’ has become part of patients’ expectations for access to quality, affordable healthcare.
For clinical laboratory managers, this steady shift toward mHealth-based care means accommodating patients who want to use mobile apps to access lab test results and on-demand lab data to monitor their health or gain advice from providers about symptoms and health issues.
Amazon, VA, and EMS Develop Their Own mHealth Apps
The Amazon Care app can be freely downloaded from Apple’s App Store and Google Play. With it, eligible employees and family members can:
Communicate with an advice nurse;
Launch an in-app video visit with a doctor or nurse practitioner for advice, diagnoses, treatment, or referrals;
Request a mobile care nurse for in-home or in-office visits;
Receive prescriptions through courier delivery.
The combination telehealth, in-person care program, mobile medical service includes dispatching nurses to homes or workplaces who can provide “physical assessments, vaccines or common [clinical laboratory] tests.”
“Amazon is a company that is experimenting a lot with a variety of opportunities in healthcare,” Glen Tullman (above), Executive Chairman of Livongo, a healthcare company specializing in treating diabetes, and an Amazon partner company, told CNBC. “It’s one to watch.” (Photo copyright: CNBC.)
However, the US federal Department of Veterans Affairs (VA) also is becoming a major player in the mHealth space with the development of its own mobile app—VA Launchpad—which serves as a portal to a range of medical services.
Veterans can access five categories of apps that allow them to manage their health, communicate with their healthcare team, share health information, and use mental health and personal improvement tools.
“The VA was an early adopter of digital health tools and remains a leader within US healthcare in leveraging technology to enhance patient engagement,” Neil C. Evans, MD (above), Chief Officer in the VA Office of Connected Care, told Healthcare IT News. “These digital tools are allowing veterans to more actively understand their health data, to better communicate with VA clinical teams, and to engage more productively as they navigate their individual health journeys,” Evans added. (Photo copyright: Department of Veterans’ Affairs.)
mHealthIntelligence reported that mobile health tools also are enabling first responders to improve emergency patient care. At King’s Daughters Medical Center in Brookhaven, Miss., emergency medical technicians (EMTs) are using a group of mHealth apps from DrFirst called Backline to gain real-time access to patients’ HIPAA-compliant medication histories, share clinical data, and gain critical information about patients prior to arriving on the scene.
Using Backline, EMTs can scan the barcode on a patient’s driver’s license to access six months’ worth of medication history.
“In the past, we could only get information from [patients] who are awake or are willing to give us that information,” Lee Robbins, Director of Emergency Medical Services at King’s Daughters Medical Center in Brookhaven, Miss., told mHealthIntelligence. “Knowing this information gives us a much better chance at a good outcome.”
Smartphone App Detects Opioid Overdose
The opioid crisis remains one of the US’ greatest health challenges. The federal Centers for Disease Control and Prevention (CDC) reported 47,600 opioid-related deaths in 2017, and the problem has only gotten worse since then.
To curtail these tragic deaths, University of Washington (UW) researchers developed a smartphone app called Second Chance, that they believe can save lives by quickly diagnosing when an opioid overdose has occurred.
The app uses sonar to monitor an opioid user’s breathing rate and, according to a UW press release, can detect overdose-related symptoms about 90% of the time from up to three feet away. The app then contacts the user’s healthcare provider or emergency services.
The UW researchers are applying for US Food and Drug Administration (FDA) clearance. They published their findings in the journal Science Translational Medicine.
While Demand for mHealth Apps Grows, Concern over Privacy and Security also Increases
According to mobile data and analytics company App Annie, global downloads of medical apps grew to more than 400 million in 2018, up 15% from two years earlier.
“As with mobile banking, consumers are showing they trust mobile apps with their most sensitive information and are willing to leverage them to replace tasks traditionally fulfilled in-person, such as going into a bank branch or, in the case of medical apps, to a doctor’s office,” App Annie’s website states.
However, the proliferation of mHealth apps has raised privacy and safety concerns as well. While the FDA does regulate some mobile health software functions, it does not ensure an mHealth app’s accuracy or reliability.
Fierce Healthcarereported that federal lawmakers are worried veterans who use the VA’s 47 mHealth apps could find their sensitive healthcare information shared or sold by third-party companies. In fiscal year 2018, veterans participated in more than one million video telehealth visits, a VA press release reported.
US Rep. Susie Lee, D-Nevada, Chairperson of the House Veterans’ Affairs Subcommittee on Technology Modernization, told Fierce Healthcare, “As we assess the data landscape at the VA and the larger health IT space, we need to look at where protections exist or don’t exist and whether we need more guardrails.”
What does all this mean for clinical laboratories? Well, lab managers will want to keep an eye on the growing demand from consumers who want direct access to laboratory test data and appointment scheduling through mHealth apps. And, also be aware of HIPAA regulations concerning the sharing of that information.
Working from tissue slides similar to those used by surgical pathologists, the algorithm accurately detects prostate cancer with an impressive 98% sensitivity
It could be that a new milestone has been reached on the road to using artificial intelligence (AI) to help anatomic pathologists diagnose cancer and other diseases. A research collaboration between a major American university and an Israeli company recently published a study about the ability of an AI algorithm to correctly diagnose prostate cancer.
The scientists trained the Galen Prostate AI to recognize prostate cancer by having it examine images from over a million parts of stained tissue slides taken from patient biopsies. Expert pathologists labeled each image to teach the algorithm how to distinguish between healthy and abnormal tissue. The AI was then tested on 1,600 different tissue slide images that had been collected from 100 patients seen at UPMC who were suspected of having prostate cancer.
“Humans are good at recognizing anomalies, but they have their own biases or past experience,” said Rajiv Dhir, MD, Chief Pathologist and Vice Chair of Pathology at UPMC Shadyside Hospital, Professor of Biomedical Informatics at University of Pittsburgh, and senior author of the study, in a UPMC news release. “Machines are detached from the whole story. There’s definitely an element of standardizing care.”
The image above is “of prostate cancer (represented by the heatmap) detected by the Ibex Galen Prostate [AI] solution on a biopsy that was previously diagnosed as benign by the pathologist,” stated an Ibex news release announcing the UPMC study. (Photo copyright: Ibex.)
UPMC Algorithm Goes Beyond Cancer Detection, Exceeds Human Pathologists
The researchers also noted that this is the first algorithm to extend beyond cancer detection. It reported high performance for tumor grading, sizing, and invasion of surrounding nerves—clinically important features of pathology reports.
“Algorithms like this are especially useful in lesions that are atypical,” Dhir said. “A nonspecialized person may not be able to make the correct assessment. That’s a major advantage of this kind of system.”
The algorithm also flagged six slides as potentially containing abnormal tissue that were not flagged by human pathologists. However, the researchers pointed out that this difference does not mean the AI is better than humans at detecting prostate cancer. It is probable, for example, that the pathologists simply saw enough evidence of malignancy elsewhere in the patients’ samples to recommend treatment.
Other Studies Where AI Detected Prostate Cancer
The UPMC researchers are not the first to use AI to detect prostate cancer. In February, The Lancet Oncology published a study from researchers at Radboud University Medical Center (RUMC) in the Netherlands who developed a deep learning AI system that could determine the aggressiveness of prostate cancer in certain patients.
For that research, the RUMC scientists collected 6,000 biopsies from more than 1,200 men. They then showed the biopsy images along with the original pathology reports to their AI system. Using deep learning, the AI was able to detect and grade prostate cancer according to the Gleason Grading System (aka, Gleason Score), which is used to rate prostate cancer and choose appropriate treatment options. The Gleason Score ranges from one to five and most cancers obtain a score of three or higher.
“Systems such as ours can be used in different ways. First, it can be used to screen biopsies and to filter out the easy (benign) cases. This could reduce the workload for pathologists,” said Wouter Bulten, a PhD candidate at Radboud who worked on the study, in an interview with HemOnc Today. “Second, the system can be used as a second opinion after the pathologist’s initial read. The system can flag a case if its opinion differs from that of the pathologist. It also can give feedback during the first read, showing the pathologist where to look. In this case, the pathologist needs only to confirm the opinion of the AI system.”
Can Today’s AI Outperform Human Pathologists?
In their research, the Radboud team discovered that their AI system was able to achieve pathologist-level performance and, in some cases, even performed better than human pathologists. However, they do not foresee AI replacing the need for pathologists, but rather emerging as another method to use in cancer detection and treatment.
“We see our system as an additional tool that the pathologist can use. Although our system performs very well, it still makes mistakes,” stated Bulten. “These mistakes are often different from those a human would make. We believe that when you merge the expertise of the pathologist with the second opinion of an AI system, you get the best of both worlds.”
According to the American Cancer Society, prostate cancer is the second most common cancer among men in the US, after skin cancer. The organization estimates there will be approximately 191,930 new cases of prostate cancer diagnosed and about 33,330 deaths from the disease in the US in 2020.
Though the UPMC study focused only on prostate cancer, the scientists believe their algorithm can be trained to detect other types of cancer as well. AI in clinical diagnostics is clearly progressing, however more studies will be required. Nevertheless, if AI can truly become a useful tool for anatomic pathologists to detect cancer earlier, we may see a welcomed reduction in cancer deaths.
Clinical laboratories involved in genetic testing may find this welcomed news, after a pair of studies conducted in 2019 raised concerns about CRISPR base editing. The researchers of those studies observed that it “causes a high number of unpredictable mutations in mouse embryos and rice,” Chemical and Engineering News (C&EN) reported, adding, “Other groups have raised concerns about off-target mutations caused when the traditional CRISPR-Cas9 form of gene editing cuts DNA at a location that it wasn’t supposed to touch. The results of the new studies are surprising, however, because scientists have lauded base editors as one of the most precise forms of gene editing yet.”
Nevertheless, UC Berkeley’s latest breakthrough is expected to drive development of new and more accurate CRISPR-Cas genome-editing tools, which consist of base editors as well as nucleases, transposases, recombinases, and prime editors.
Understanding CRISPR Base Editors At a ‘Deeper Level’
Harvard University Chemistry and Chemical Biology Professor David Liu, PhD, who co-authored the study, explained the significance of this latest discovery.
“While base editors are now widely used to introduce precise changes in organisms ranging from bacteria to plants to primates, no one has previously observed the three-dimensional molecular structure of a base editor,” he said in a UC Berkeley news release. “This collaborative project reveals the beautiful molecular structure of a state-of-the-art highly-active base editor—ABE8e—caught in the act of engaging a target DNA site.”
UC Berkeley Professor Jennifer Doudna, PhD (above), who served as senior author of the study, says scientists may now have the information necessary to refine base editors and improve their precision and genome-targeting ability. “This structure helps us understand base editors at a much deeper level,” she said in the UC Berkeley statement. “Now that we can see what we’re working with, we can develop informed strategies to improve the system.” (Photo copyright: UC Berkeley.)
Jennifer Doudna, PhD, UC Berkeley Professor, Howard Hughes Medical Institute Investigator, and senior author of the study, has been a leading figure in the development of CRISPR-Cas9 gene editing. In 2012, Doudna and Emmanuelle Charpentier, PhD, Founding, Scientific and Managing Director at Max Planck Unit for the Science of Pathogens in Berlin, led a team of researchers who “determined how a bacterial immune system known as CRISPR-Cas9 is able to cut DNA, and then engineered CRISPR-Cas9 to be used as a powerful gene editing technology.” In a 2017 news release, UC Berkeley noted that the work has been described as the “scientific breakthrough of the century.”
Viewing the Base Editor’s 3D Shape
CRISPR-Cas9 gene editing allows scientists to permanently edit the genetic information of any organism, including human cells, and has been used in agriculture as well as medicine. A base editor is a tool that manipulates a gene by binding to DNA and replacing one nucleotide with another.
According to the recent UC Berkeley news release, the research team used a “high-powered imaging technique called cryo-electron microscopy” to reveal the base editor’s 3D shape.
Genetic Engineering and Biotechnology News notes that, “The high-resolution structure is of ABE8e bound to DNA, in which the target adenine is replaced with an analog designed to trap the catalytic conformation. The structure, together with kinetic data comparing ABE8e to earlier ABEs [adenine base editors], explains how ABE8e edits DNA bases and could inform future base-editor design.”
The graphic above, taken from the UC Berkeley news release, shows the “3D structure of a base editor, comprised of the Cas9 protein (white and gray), which binds to a DNA target (teal and blue helix) complementary to the RNA guide (purple), and the deaminase proteins (red and pink), which switch out one nucleotide for another.” (Image and caption copyright: UC Berkeley.)
Knowing the Cas9 fusion protein’s 3D structure may help eliminate unintended off-target effects on RNA, extending beyond the targeted DNA. However, until now, scientists have been hampered by their inability to understand the base editor’s structure.
“If you really want to design truly specific fusion protein, you have to find a way to make the catalytic domain more a part of Cas9, so that it would sense when Cas9 is on the correct target and only then get activated, instead of being active all the time,” study co-first author Audrone Lapinaite, PhD, said in the news release. At the time of the study, Lapinaite was a postdoctoral fellow at UC Berkeley. She is now an assistant professor at Arizona State University.
“As a structural biologist, I really want to look at a molecule and think about ways to rationally improve it. This structure and accompanying biochemistry really give us that power,” added UC Berkeley postdoctoral fellow Gavin Knott, PhD, another study co-author. “We can now make rational predications for how this system will behave in a cell, because we can see it and predict how it’s going to break or predict ways to make it better.”
Clinical laboratory leaders and pathologists will want to monitor these new advances in CRISPR technology. Each breakthrough has the power to fuel development of cost-effective, rapid point-of-care diagnostics.
Though only in the pilot study phase, results correlate with earlier studies where both dogs and humans were able to “smell” specific diseases in people
Man’s best friend has risked life and limb to save humans for centuries. Now, researchers in Germany have discovered that pooches may be useful in the fight against COVID-19 as well, along with the added benefit that such testing would be non-invasive. In fact, some people believe disease-sniffing dogs may give clinical laboratory testing a run for its money.
Further, even if this approach were not warranted as a clinical diagnostic procedure, trained dogs could be deployed at airports, train stations, sporting events, concerts, and other public places to identify individuals who may be positive for SARS-CoV-2, the coronavirus that causes the COVID-19 illness. Such an approach would make it feasible to “screen” large numbers of people as they are on the move. Those individuals could then undergo a more precise medical laboratory test as confirmation of infections.
After only one week of training, the dogs were able to accurately detect the presence of the infection 94% of the time.
According to a live interview, which featured Holger Volk, PhD, Department Chair and Clinical Director of the Small Animal Clinic at the University of Veterinary Medicine Hannover and Maren von Köckritz-Blickwede, PhD, Professor of Biochemistry of Infections and Head of Scientific Administration and Biosafety at the Research Center for Emerging Infections and Zoonoses at TiHo, “The samples were automatically distributed at random and neither the dog handlers involved nor the researchers on site knew which samples were positive and which were used for control purposes. The dogs were able to distinguish between samples from infected (positive) and non-infected (negative) individuals with an average sensitivity of 83% and a specificity of 96%. Sensitivity refers to the detection of positive samples. The specificity designates the detection of negative control samples.
In their published study, the authors wrote, “Within randomized and automated 1,012 sample presentations, dogs achieved an overall average detection rate of 94% with 157 correct indications of positive, 792 correct rejections of negative, 33 false positive and 30 false negative indications.” They concluded, “These preliminary findings indicate that trained detection dogs can identify respiratory secretion samples from hospitalized and clinically diseased SARS-CoV-2 infected individuals by discriminating between samples from SARS-CoV-2 infected patients and negative controls. This data may form the basis for the reliable screening method of SARS-CoV-2 infected people.”
In the live interview, Dr. Köckritz-Blickwede said, “We think that this works because the metabolic processes in the body of a diseased patient are completely changed,” adding, “We think that the dogs are able to detect a specific smell of the metabolic changes that occur in those patients.”
“People have not really realized the potential the dog could have to detect disease from lung-diseased patients,” said Holger Volk, PhD (above with his dog Jo), Department Chair and Clinical Director of the Small Animal Clinic at the University of Veterinary Medicine Hannover and one of the authors of the paper, in a live interview. (Photo copyright: University of Veterinary Medicine Hannover.)
Using Dogs as Part of Clinical Laboratory Testing
The American Kennel Club (AKC) estimates that a dog’s sense of smell is 10,000 to 100,000 times greater than that of humans. This gives dog’s the ability to detect diseases in early stages of development.
“The next steps will be that we try to differentiate between sputum samples from COVID patients versus other diseases, like, for example from influenza patients,” said Köckritz-Blickwede. “That will be quite important to be able to differentiate that in the future.”
“This method could be employed in public areas such as airports, sport events, borders or other mass gatherings as an addition to laboratory testing, helping to prevent further spreading of the virus or outbreaks,” the live interview description states.
During a pandemic, employers might be able to use dogs to screen employees as they arrive for work. Dogs also could be used as an alternative or in addition to clinical laboratory testing to help prevent the spread of COVID-19. But more work must be done.
“What has to be crystal clear is that this is just a pilot study,” said Volk. “So, there is a lot of potential to take this further to really make it possible to use these dogs in the field.”
An article on the VCA Hospitals website, titled, “How Dogs Use Smell to Perceive the World,” states that dogs devote much of their brain power to the interpretation of smells and they have more than 100 million sensory receptor sites located in their nasal cavity.
By contrast, humans have only six million sensory receptor sites in their nasal cavity. The area of a dog’s brain that is dedicated to the analysis of odors is about 40 times larger than the comparable part of a human brain and dogs are capable of detecting odors thousands of times better than humans.
The article also further explains how dog’s olfactory glands are very unique when compared to other animals and humans. “Unlike humans, dogs have an additional olfactory tool that increases their ability to smell. Jacobson’s organ is a special part of the dog’s olfactory apparatus located inside the nasal cavity and opening into the roof of the mouth behind the upper incisors. This amazing organ serves as a secondary olfactory system designed specifically for chemical communication.
“The nerves from Jacobsen’s organ lead directly to the brain and are different from the other nerves in the nose in that they do not respond to ordinary smells. In fact, these nerve cells respond to a range of substances that often have no odor at all. In other words, they work to detect “undetectable” odors.”
VCA Hospitals is a chain of veterinary hospitals with more than 1,000 facilities located in 46 states and five Canadian provinces.
Could dogs help prevent hospital-acquired infections? It is an interesting question, and one that has been asked before. In “C. diff-sniffing Beagle Dog Could Lead to Better Infection Control Outcomes in Hospitals and Nursing Homes,” January 2013, Dark Daily reported on a beagle named Cliff (above), which could sniff out Clostridium difficile (C. diff), a potentially deadly bacteria. In a study conducted by researchers at Vrije University Medical Center (VUMC) in Amsterdam, Cliff detected C. diff in both stool samples and the air surrounding infected patients in hospitals. In one test, Cliff correctly identified 50 out of 50 stool samples that were C. diff positive. He correctly identified 47 of 50 negative samples. That’s a sensitivity rate of 100% and a specificity rate of 94%. (Photo copyright: ABC News.)
Dogs are amazing, that’s for sure. But for canines to become widely used to detect infections there would have to be a way to validate each dog’s ability to detect diseases, so that the diagnostics would be consistent across all the dogs being used.
So, while there appears to be potential for utilizing a dog’s uncanny sense of smell to detect disease—including COVID-19—more research is needed before development of clinical testing can take place. And, perhaps, a set of canine billing codes.
The KCL researchers’ new models for predicting which patients will need hospitalization and breathing support may be useful for pathologists and clinical laboratory scientists
One more window into understanding the SARS-CoV-2 coronavirus may have just opened. A British study identified six distinct “clusters” of symptoms that the research scientists believe may help predict which patients diagnosed with COVID-19 will require hospitalization and respiratory support. If further research confirms these early findings, pathologists and medical laboratory managers may gain new tools to diagnose infections faster and more accurately.
Launched in March in the United Kingdom and extended to the United States and Sweden, the app has attracted more than four million users who track their health and potential COVID symptoms on a daily basis.
Increased Accuracy in Predicting COVID-19 Hospitalizations
KCL researchers identified six distinct “types” of COVID-19, each distinguished by a particular cluster of symptoms. They include headaches, muscle pains, fatigue, diarrhea, confusion, loss of appetite, shortness of breath, and more. The researchers also found that COVID-19 disease progression and outcome also vary significantly between people, ranging from mild flu-like symptoms or a simple rash to severe or fatal conditions.
Using app data logged by 1,600 users in March and April, the researchers developed an algorithm that combined information on age, gender, body mass index (BMI), and pre-existing conditions with recorded symptoms from the onset of the illness through the first five days. The researchers then tested the algorithm using a second independent dataset of 1,000 users, logged in May.
In a news release, the KCL researchers identified the six clusters of symptoms as:
Flu-like with No Fever: Headache, loss of smell, muscle pains, cough, sore throat, chest pain, no fever.
Flu-like with Fever: Headache, loss of smell, cough, sore throat, hoarseness, fever, loss of appetite.
Gastrointestinal: Headache, loss of smell, loss of appetite, diarrhea, sore throat, chest pain, no cough.
Severe Level One, Fatigue: Headache, loss of smell, cough, fever, hoarseness, chest pain, fatigue.
Severe Level Two, Confusion: Headache, loss of smell, loss of appetite, cough, fever, hoarseness, sore throat, chest pain, fatigue, confusion, muscle pain.
Severe Level Three, Abdominal and Respiratory: Headache, loss of smell, loss of appetite, cough, fever, hoarseness, sore throat, chest pain, fatigue, confusion, muscle pain, shortness of breath, diarrhea, abdominal pain.
Using the data, the researchers were able to more accurately predict—78.8% versus 69.5%—which of the six symptom clusters placed patients at higher risk of requiring hospitalization and breathing support (ventilation or additional oxygen) than with prediction models based on personal characteristics alone. For example, nearly 50% of the patients in cluster six (Severe Level Three, Abdominal and Respiratory) ended up in the hospital, compared with 16% of those in cluster one (Flu-like with No Fever).
“These findings have important implications for care and monitoring of people who are most vulnerable to severe COVID-19,” Claire Steves, MD, PhD (above left), Clinical Senior Lecturer at King’s College London, said in the KCL news release. “If you can predict who these people are at day five, you have time to give them support and early interventions, such as monitoring blood oxygen and sugar levels, and ensuring they are properly hydrated—simple care that could be given at home, preventing hospitalizations and saving lives.” (Photo copyright: King’s College London.)
According to the Zoe website, the ongoing research is led by:
Prof. Tim Spector, FMedSci, Professor of Genetic Epidemiology at King’s College London and Director of TwinsUK, an adult registry of twins in the United Kingdom;
Andrew Chan, MD, Professor of Medicine at Harvard Medical School, Professor of Immunology and Infectious Diseases, Harvard T.H. Chan School of Public Health and Chief of the Clinical and Translational Epidemiology Unit, CTEU Massachusetts General Hospital; and
Encouraging Everyone to Use the COVID-Symptom Study App
The study points out that—broadly speaking—people with cluster four, five, or six COVID-19 symptoms tended to be older and frailer and were more likely to be overweight and have pre-existing conditions, such as diabetes or lung disease, than those with cluster one, two, or three symptoms.
“Our study illustrates the importance of monitoring symptoms over time to make our predictions about individual risk and outcomes more sophisticated and accurate,” said lead researcher Carole Sudre, PhD (above), a Research Fellow at King’s College London and the study’s lead researcher, in the KCL news release. “This approach is helping us to understand the unfolding story of this disease in each patient so they can get the best care.” (Photo copyright: University College London.)
Tim Spector, FMedSci, Head of the Department of Twin Research and Genetic Epidemiology, and Professor of Genetic Epidemiology at King’s College London, encourages everyone to download the COVID Symptom Study app and help increase the data available to researchers.
“Data is our most powerful tool in the fight against COVID-19,” Spector said in the KCL news release. “We urge everyone to get in the habit of using the app daily to log their health over the coming months, helping us to stay ahead of any local hotspots or a second wave of infections.”
As the body of knowledge surrounding COVID-19 grows, clinical laboratory professionals would be well advised to remain informed on further research regarding not only the potential for COVID-19 variants to exist, but also the evolving guidance on infection prevention and testing.
