“It’s fair to say that, in Italy, we are doing 10 years of digital health evolution in 10 days.”
Our “man-on-the-street” in Italy (well, man-sheltered-in-place in Italy) Roberto Ascione, CEO of Healthware, reports in on the Covid-19 outbreak and what’s happening with digital health startups, health system partners, and hospitals as Italians continue battling at the forefront of the coronavirus outbreak.
A few weeks ahead of the U.S., there are many things to learn about Covid-19 testing, treatment, outcomes, and timing from the experience in Italy, including some foresight on how pathways for telehealth and digital health continue to evolve as conditions become more serious and the outbreak progresses. (For all you Gretzky fans, this is “skating to where the puck will be” kind of stuff…)
Some navigational guidance on this chat which took place March 26, 2020:
Update on Italian Covid-19 outbreak from health industry insider
10:25 minute mark: Digital Health startup case study, Paginemediche, self-triage chatbot data from 70K Italians, data sharing with Italian government & WHO, telehealth model flipping to give overwhelmed physicians opportunity to triage and “invite” patients based on needs
19:10 mark: How to work with Italian digital health startups to advance Covid-19 work
Today on Health in 2 Point 00, we have a viewer question! For our friends who are wondering what will happen to all the IPOs that were supposed to happen this year, I weigh in on how this crisis will impact IPOs and startup funding. On Episode 114, Jess asks me about the stimulus package granting $117 billion to hospitals and for my thoughts on all the startups coming up with ways to address COVID-19. A few startups that come to mind include Conversa with its virtual care conversation, Coronavirus Health Chats, Biofourmis which is looking for ways to track infected people earlier through its AI-powered arm sensor, and Surveyor Health leveraging its data analytics platform as well. For more on this, check out covid19healthtech.com where my colleagues at Catalyst have put together a resource hub for health tech solutions. —Matthew Holt
This piece is part of the series “The Health Data Goldilocks Dilemma: Sharing? Privacy? Both?” which explores whether it’s possible to advance interoperability while maintaining privacy. Check out other pieces in the series here.
If you live in one of the jurisdictions that have imposed stay-at-home requirements, you’re probably making your essential excursions — grocery store, pharmacy, even walks — with a wary eye towards anyone you come across. Do they have COVID-19? Have they been in contact with anyone who has? Are they keeping at least the recommended six feet away from you? In short, who is putting you at risk?
Well, of course, this
being the 21st century, we’re turning to our smartphones to help us try to
answer these questions. What this may lead to remains to be seen.
We long ago seemed to
shrug off the fact that our smartphones and our apps know where we are and
where we have been. No one should be surprised that location is of
importance to tracking the spread of COVID-19. No one should be surprised
that it is already being used. We may end up being surprised at how it
will be used.
This episode of “The THCB Gang” is up here as a video (you could also see it live at 1PT/4ET every Thursday) and it’s also preserved as a weekly podcast and available on our Itunes & Spotify channels a day or so later. Each week 4-6 semi-regular guests drawn from THCB authors and other assorted old friends of mine will shoot the shit about health care business, politics, practice, and tech. It should be fun but serious and informative!
This week, joining me was Michael Millenson (@MLMillenson), Grace Cordovano (@GraceCordovano), Vince Kuraitis (@VinceKuraitis), Brian Klepper (@bklepper1) Ian Morrison (@seccurve) & Anish Koka (@anish_koka). A fun and argumentative discussion about where the COVID-19 crisis is right now and what it’s going to mean both now and in the near future — Matthew Holt
Occasionally, you get handed a question you know little about, but it’s clear you need to know more. Like most of us these days, I was chatting with my colleagues about the novel coronavirus. It goes by several names: SARS-CoV-2, 2019-nCoV or COVID-19 but I’ll just call it COVID. Declared a pandemic on March 12, 2020 by the World Health Organization (WHO), COVID is diagnosed by laboratory test – PCR. The early PCR test used in Wuhan was apparently low sensitivity (30-60%), lengthy to run (days), and in short supply. As CT scanning was relatively available, it became an importantdiagnostic tool for suspected COVID cases in Wuhan.
The prospect of scanning thousands of contagious patients was daunting, with many radiologists arguing back and forth about its appropriateness. As the pandemic has evolved, we now have better and faster PCR tests and most radiologists do not believe that CT scanning has a role for diagnosis of COVID, but rather should be reserved for its complications. Part of the reason is the concern of transmission of COVID to other patients or healthcare workers via the radiology department.
But then someone asked: “After you have scanned a patient for COVID, how long will the room be down?” And nobody really could answer – I certainly couldn’t. A recent white paper put forth by radiology leaders suggested anywhere from 30 minutes to three hours. A general review of infection control information for the radiologist and radiologic technologist can be found in Radiographics.
So, let’s go down the rabbit hole of infection control in the radiology department. While I’m a radiologist, and will speak about radiology-specific concerns, the fundamental rationale behind it is applicable to other ancillary treatment rooms in the hospital or outpatient arena, provided the appropriate specifics about THAT environment is obtained from references held by the CDC.
Jamey Edwards, CEO of one of the larger in-hospital B2B telehealth startups in the US, Cloudbreak Health, is already seeing changes in the way hospitals are using his company’s telemedicine services in the wake of COVID-19.
From a noted rise in the rate of infectious disease consults, to “quarantine rooms” where telemedicine equipment is cleverly deployed to practice “clinical distancing” to minimize risk to front-line healthcare workers (and also preserve PPE), Jamey talks about what he’s seeing among hospital clinicians and what they seem to need most right now from telehealth providers amid the COVID-19 outbreak.
With changes to licensing regulations, HIPAA policies, and reimbursement changing the very infrastructure around telehealth, will we finally see virtual care become a true part of the healthcare system at-scale?
“One of the hardest things to do in our healthcare system is match cost to acuity,” says Jamey. “I’m not going to say we’ve overvalued the in-person encounter, but we certainly have been very hesitant to step away from it.”
“The fact of the matter is that that’s a bias. And so it’s up to us to look at these biases and say, ‘Well, no. What is the right way to do this?’”
I got asked the other day to comment for Wired on the role of AI in Covid-19 detection, in particular for use with CT scanning. Since I didn’t know exactly what resources they had on the ground in China, I could only make some generic vaguely negative statements. I thought it would be worthwhile to expand on those ideas here, so I am writing two blog posts on the topic, on CT scanning for Covid-19, and on using AI on those CT scans.
As background, the pro-AI argument goes like this:
CT screening detects 97% of Covid-19, viral PCR only detects 70%!
A radiologist takes 5-10 minutes to read a CT chest scan. AI can do it in a second or two.
If you use CT for screening, there will be so many studies that radiologists will be overwhelmed.
In this first post, I will explain why CT, with or without AI, is not worthwhile for Covid-19 screening and diagnosis, and why that 97% sensitivity report is unfounded and unbelievable.
Next post, I will address the use of AI for this task specifically.
Since the World Health Organization (WHO) officially declared COVID-19 a pandemic on March 11, 2020, we have been changing our daily lives to protect the highest-risk populations: older adults and people with chronic medical conditions. We are asked to follow sensible guidelines like social distancing and thorough hand-washing. Although one may have a gut-reaction to put their own safety at the forefront during these times of crisis, it is essential that we are taking the necessary steps to protect populations with additional vulnerabilities – rural tribal communities.
With the announcement that COVID-19 reached the Confederated Tribes of Umatilla Indian Confederation on March 9, 2020, it was evident the virus would not stay confined to urban and metropolitan centers like some previously predicted. The experience in China with COVID-19 clearly reflects the vulnerability of rural communities because many people travel routinely from urban to rural. Experts who conducted an epidemiological study in Hubei province, the initial epicenter of the COVID-19 pandemic, noted in their report: “…most public medical resources are concentrated in cities but are relatively scarce in rural areas. Therefore, prevention and treatment of 2019-nCoV in rural areas will be more challenging if new phases of the epidemic emerge.”
By VASANTH VENUGOPAL MD and VIDUR MAHAJAN MBBS, MBA
What can Artificial
Intelligence (AI) do?
simply put, do two things – one, it can do what humans can do. These are tasks
like looking at CCTV cameras, detecting faces of people, or in this case, read
CT scans and identify ‘findings’ of pneumonia that radiologists can otherwise
also find – just that this happens automatically and fast. Two, AI can do
things that humans can’t do – like telling you the exact time it would take you
to go from point A to point B (i.e. Google maps), or like in this case,
diagnose COVID-19 pneumonia on a CT scan.
on CT scans?
an infection of the lungs, is a killer disease. According to WHO statistics from
2015, Community Acquired Pneumonia (CAP) is the deadliest communicable disease
and third leading cause of mortality worldwide leading to 3.2 million deaths
be classified in many ways, including the type of infectious agent (etiology),
source of infection and pattern of lung involvement. From an etiological classification
perspective, the most common causative agents of pneumonia are bacteria
(typical like Pneumococcus, H.Influenza and atypical like Legionella,
Mycoplasma), viral (Influenza, Respiratory Syncytial Virus, Parainfluenza, and
adenoviruses) and fungi (Histoplasma & Pneumocystis Carinii).