Kat Jercich

By using real-world patient information, the agency hopes to research population health trends and medication efficacy.

Billed by the software giant as its first industry-specific cloud product, the newly-launched platform is also aimed at enhancing patient engagement, improving provider communication and boosting analytics.

Researchers from New York-based Mount Sinai Health System have combined artificial intelligence, imaging and clinical data to rapidly detect COVID-19 in patients. In a study published this week in Nature Medicine, researchers used AI algorithms in conjunction with chest CT scans and patient history to quickly diagnose patients who were positive for COVID-19 and improve the detection of patients who presented with normal CT scans. "We were able to show that the AI model was as accurate as an experienced radiologist in diagnosing the disease, and even better in some cases where there was no clear sign of lung disease on CT," said Dr. Zahi Fayad, director of the BioMedical Engineering and Imaging Institute at the Icahn School of Medicine at Mount Sinai, in a statement. WHY IT MATTERS Because the symptoms of COVID-19 are non-specific, it can be difficult to diagnose. Meanwhile, the SARS-CoV-2 virus-specific reverse transcriptase polymerase chain reaction (RT-PCR) test commonly used to identify COVID-positive patients can take up to two days to complete – and clinicians face the possibility of false negatives. RT-PCR test kits are also in short supply throughout many parts of the country. This, researchers say, reiterates the need for other ways to quickly and accurately diagnose patients with COVID-19. Researchers relied on CT scans of more than 900 patients that had been admitted to 18 medical centers in 13 Chinese provinces. They included 419 confirmed COVID-19-positive cases and 486 COVID-19-negative scans. The team also had access to patients' clinical information, including blood test results, age, sex and symptoms.  Using patient data, Mount Sinai researchers developed an AI algorithm to produce separate probabilities of COVID-19 positivity based on CT images, clinical information and the two combined.  "In a test set of 279 patients, the AI system achieved an area under the curve of 0.92 and had equal sensitivity as compared to a senior thoracic radiologist," researchers wrote. In addition, the algorithm correctly identified 17 of 25 patients whose RT-PCR results had tested positive for COVID-19 but who presented with normal CT scans; for comparison, radiologists had classified all the patients as COVID-negative.  Although clinicians in the United States do not frequently use CT scans to diagnose COVID-19, researchers say imaging can play a vital role in conserving hospital resources and treating patients quickly.  "The high sensitivity of our AI model can provide a 'second opinion' to physicians in cases where CT is either negative (in the early course of infection) or shows nonspecific findings, which can be common," said Fayad. "It's something that should be considered on a wider scale, especially in the United States, where currently we have more spare capacity for CT scanning than in labs for genetic tests," Fayad continued. THE LARGER TREND Researchers have increasingly relied on AI to diagnose and treat patients with the novel coronavirus. In March, cognitive computing platform vendor behold.ai announced it had developed an AI-based algorithm to flag chest X-rays from COVID-19. Calling its platform "instant triage," behold.ai predicted it could help speed COVID-19 diagnosis.  "As we evaluate further positive cases from across the world, our results will be further validated," said behold.ai Chief Medical Officer Dr. Tom Naunton Morgan.  "This will increase the utility of our instant triage and potentially help reduce the burden on healthcare systems as more and more cases of pneumonia present and require rapid diagnosis," Morgan said. Other technology vendors have adapted existing tuberculosis-detecting AI technology to help indicate COVID-affected lung tissue in chest X-rays. ON THE RECORD Mount Sinai researchers say their next steps will be to further develop the model to forecast patient outcomes and to share their results with other healthcare facilities. "This study is important because it shows that an artificial intelligence algorithm can be trained to help with early identification of COVID-19, and this can be used in the clinical setting to triage or prioritize the evaluation of sick patients early in their admission to the emergency room," said Dr. Matthew Levin, director of the Mount Sinai Health System's clinical data science team.  "This is an early proof [of] concept that we can apply to our own patient data to further develop algorithms that are more specific to our region and diverse populations," said Levin. "This toolkit can easily be deployed worldwide to other hospitals, either online or integrated into their own systems," said Fayad.   Kat Jercich is senior editor of Healthcare IT News. Twitter: @kjercich Healthcare IT News is a HIMSS Media publication.

When it comes to cybersecurity issues, many in the healthcare industry likely recognize the importance of protecting patient medical data.  However, as Fairview Health Offices Chief Information Security Officer Judy Hatchett and Proofpoint managing director of health practice Ryan Witt pointed out in a recent HIMSS20 Digital presentation, cybersecurity is also about protecting patients themselves. "'Do no harm' is a principle that I know … providers hold dear," said Witt in his talk with Hatchett, Why Cybersecurity Is a Core Component of Patient Safety. "Patient safety is a component of that." Witt, a HIMSS Cybersecurity, Privacy & Security Committee member, explained that security and data breaches can lead to service outages at healthcare facilities, which in turn can compromise patient health in a real way. When a facility has "downtime as a result of a cyberattack, almost by definition you are doing your patients harm," Witt said. According to a 2019 American Medical Association-Accenture Medical Cybersecurity Survey, 36% of health institutions were unable to provide care for at least five hours as a result of cyberattacks. "Any sort of cybercriminal activity that drives downtime, that interrupts your system ... is potentially impacting patient care," Witt said.  Hatchett and Witt said that the majority of cybercrime occurred using phishing – with bad actors often impersonating trusted contacts like the Centers for Disease Control and Prevention, the World Health Organization, and others.  This tactic is especially notable amid the coronavirus crisis, they said, as message recipients are more likely to be looking for reliable information from health organizations. "Any time of email compromise is always going to be the number one threat vector," said Hatchett. However, she said, it's also vital to be conscious of the ways a system is protecting connected medical devices, both for the sake of patients who rely on those devices and for the security of the system itself. Hatchett and Witt also warned about employees' habits of posting too much information about their professional role on LinkedIn or other social networking sites, as it may make them a target for criminals. This is especially true for those who hold more frequently attacked positions, such as nurses, pharmacists and researchers. "Who doesn't want to brag about what they do on LinkedIn?" Hatchett said. "But there is some risk in doing that. … Put some thought into how much you're putting out there." .jumbotron{ background-image: url("/sites/hitn/files/u2556/HIMSSDigitalJumbo.jpg"); background-size: cover; color: white; } .jumbotron h2{ color: white; } HIMSS20 Digital Experience the education, innovation and collaboration of the HIMSS Global Health Conference & Exhibition… virtually. Kat Jercich is senior editor of Healthcare IT News. Twitter: @kjercich Healthcare IT News is a HIMSS Media publication.

The VA St. Louis Healthcare System is partnering with telecom vendor Uniper Care in a pilot program to give veterans access to social support to address loneliness and depression.

Current patient deferral of nonurgent services could lead to a "care debt" down the line, say Duke researchers. To meet those needs, health systems should be building out a robust telemedicine infrastructure.

The legislation would forbid companies from using health information for "discriminatory, unrelated or intrusive purposes."

Boston-area South Shore Health System raised HCAHPS scores for its OB/GYN department with the help of a mobile app providing consistent information to expectant patients.

Leaders from Geisinger, Dartmouth-Hitchcock and other health systems say "transformation" is needed to help providers and their patients weather this storm.

The New York-based company claims its platform can be used to more accurately monitor efficacy of immunotherapy.