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Heres Why Bitcoin Price Just Spiked to $7.3K, Liquidating $90M – Cointelegraph

The Bitcoin (BTC) price spiked from $6,900 to $7,300 across major cryptocurrency exchanges, liquidating $90 million on BitMEX and Bitfinex alone.

While several traders anticipated the Bitcoin price to see a strong upsurge as it breaks out of the $6,900 resistance level, the abrupt upsurge to $7,300 caught many technical analysts off guard.

The Bitcoin price is often susceptible to large downside movements as seen on March 12, when the price dropped to $3,600 from $8,000 on a single day.

Bitcoin tends to see overextended price movements because of the concentration of its volume in the futures market. According to the data from Bitwise Asset Management, the verifiable 24-hour spot volume of Bitcoin is estimated to be around $1.5 billion.

In contrast, BitMEX alone has processed $2.9 billion in the last 24 hours, with Binance Futures, OKEx, Huobi, Bybit and Deribit also seeing high volumes across top cryptocurrencies including Bitcoin and Ethereum.

On futures exchanges, traders typically use leverage ranging from 1x to 125x, essentially trading with borrowed funds. Leveraged trades leave the market vulnerable to extreme price swings, as they lead long contracts and short contracts to be liquidated in short periods of time.

Prior to the drop, the majority of traders in the crypto market were longing Bitcoin. Across BitMEX, Binance Futures, and Bitfinex, around 60 percent of futures contracts were longs.

The steady increase in buying demand started to squeeze Bitcoin shorts, forcing traders to market buy and adjust their positions. The squeeze continued on past $7,000, bringing the price of BTC to $7,300 in quick succession.

BTC-USDT daily chart. Source: TradingView

Highly regarded traders like Flood previously said that the strong recovery of Bitcoin from the $5,900 support level would likely result in a new test of higher resistance levels.

Had the entire move been triggered by a cascade of short liquidations from low levels, it would make a solid case for a short-term bearish retest.

But, since late March, the gradual increase in the price of Bitcoin since low $6,000s has primarily been led by growing spot volume and demand.

In a report, major cryptocurrency exchange Coinbase said that retail investors increasingly bought Bitcoin following the crash to $3,600.

Our customers typically buy 60% more than they sell but during the crash this jumped to 67%, taking advantage of market troughs and representing strong demand for crypto assets even during extreme volatility, said Coinbase.

A spot market-driven rally that is supplemented with shorts liquidation presents a more optimistic short-term outlook than just a futures market-driven price spike.

The key to sustaining the newly found short-term momentum of Bitcoin would be a slow grind upwards following the initial rapid increase to $7,300.

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Heres Why Bitcoin Price Just Spiked to $7.3K, Liquidating $90M - Cointelegraph

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Crypto Traders See Bitcoin (But Not Ethereum) Surging To All-Time Highs In 2020Heres Why – Forbes

Bitcoin investors have nervously watched markets over recent weeks, with some senior figures in the bitcoin community warning confidence has "evaporated."

The bitcoin price has swung wildly along with traditional markets since the coronavirus COVID-19 began spreading out of China, dropping to lows of under $4,000 per bitcoin from almost $10,000 in early March.

Now, despite bitcoin dropping by some 30% over the last 30-day period, survey results show bitcoin investors were upbeat at the beginning of the year, with most expecting the bitcoin price to soar to over $20,000 per bitcoin in 2020.

Bitcoin investors are confident bitcoin, by far the most valuable cryptocurrency, will see a fresh ... [+] all-time high this year--though most don't expect ethereum, the second most valuable digital token, to do so.

The average 2020 bitcoin price target cited by traders and investors was $22,866 per bitcoin, a survey of U.S.-based bitcoin and crypto exchange Kraken users showed, up on bitcoin's all-time high of around $20,000.

The bitcoin price has fallen by some $2,000 since the survey was carried out, hit hard by the broader market sell-off sparked by the spreading coronavirus.

Bitcoin sunk to lows of under $4,000 earlier this month before bouncing back to trade over $6,000 and remains highly volatile.

The U.S. Federal Reserve and central banks around the world have moved to pump unprecedented levels of freshly-printed cash into the system in response to the coronavirus crisis, with some senior bitcoin and cryptocurrency figures arguing this could result in a surge of interest in crypto.

"As we get used to talk about trillions, a modest $2 trillion market cap of bitcoin will put one bitcoin at $100,000," the chief executive of the world's largest bitcoin and crypto exchange Binanace, Changpeng Zhao, said viaTwitter, adding it's "not such a hard to imagine number now, right?"

Ahead of the bitcoin price plunge earlier this month, many of Kraken's biggest users expected the ... [+] bitcoin price to hit all-time highs later this year.

However, the outlook for altcoins was less rosy with only slight more than half (54%) of respondents expecting a so-called alt season this year.

Traders didn't see ethereum, the world's second most valuable cryptocurrency returning to its all-time highs this year, with an average price target of $810down from ethereum's all-time highs of over $1,000 in late 2017.

Elsewhere, bitcoin and crypto market sentiment was mixed. A majority (44%) of respondents saw the market as bullish, though 34% were undecided and 22% felt it was in bear market territory.

Bitcoin and crypto traders also failed to find common ground as to what will push the market forward this year.

"Adoption" was cited by 19% of respondents, while bitcoins upcoming halving was named by 15%. Political "conflict," "fear of missing out," and economic "crisis" were also popular responses.

Most bitcoin and cryptocurrency investors believe we're now in a bull market though there's little ... [+] agreement, with many seeing the current market as bearish and others undecided.

The survey, carried out in late January, polled some 400 so-called VIP Kraken users, with 41% of respondents describing themselves as "investors," 40% as "traders," and 15% as "institutions"the remainder was made up of "payment processors," "crypto exchanges" and "miners."

Meanwhile, nearly 50% of respondents said they expect the U.S. Security and Exchange Commission (SEC) to approve a bitcoin exchange-traded (ETF) fund this year.

Earlier this year, the SEC rejected an ETF application from New York-based asset management firm Wilshire Phoenix and options exchange NYSE Arca that wanted to mix bitcoin and short-term Treasuries.

The SEC has rejected many applications for a bitcoin ETF over recent years, meaning this latest ruling didn't come as a surprise, though comments accompanying the ruling suggested the SEC might not green light a bitcoin ETF for the foreseeable future.

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Crypto Traders See Bitcoin (But Not Ethereum) Surging To All-Time Highs In 2020Heres Why - Forbes

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Bitcoin Closes Q1 With Historic Darth Maul Candle: Heres What it Means – newsBTC

Bitcoin price had one of its most volatile months yet, resulting in a massive, $6,000 long Darth Maul candle on the three-month price chart.

But what does this rare and explosive candlestick pattern typically indicate, and what could this mean for Bitcoin price in the future?

Last night at 8:00 PM Eastern, the monthly candle closed on Bitcoin price charts, and with it came the close of the first quarter of 2020 a quarter filled with economic turmoil, a pandemic, and much more.

The first-ever cryptocurrency ended 2019 with a bang, falling to $6,400 before beginning 2020 with a massive, over 60% rally, taking Bitcoin price to over the crucial psychological barrier at $10,000.

Related Reading | Bitcoin Price Action Triggers Devastatingly Accurate Sell Signal

However, the rally failed to incite retail FOMO, and days later, fears over the rapid proliferation of the coronavirus peaked, spilling into markets and causing record-breaking collapses in most major asset classes.

Bitcoin and the rest of cryptocurrencies were hit particularly hard, causing the leading cryptocurrency by market cap to plummet to a low of $3,800.

The over 60% fall shocked even the longest-term Bitcoin holders and wiped cryptocurrency exchange order books clean for days to follow.

In the first three months of 2020, Bitcoin price went from $6,800 to $10,500, back down to $3,800 before closing last nights monthly above $6,400 holding onto monthly support by a mere few dollars.

The resulting price action has left a nasty looking candle on three-month Bitcoin price charts, resembling a Darth Maul lightsaber from the popular sci-fi series Star Wars.

This type of candle features a tight red-bodied candle, with two enormous wicks on either end, forming the two-ended lightsaber the Star Wars character wields.

The damage these candles do is almost as sinister as the villain himself, and on smaller timeframes, are a sign that stop losses on either side of the trading range were taken out.

Stop runs or stop loss hunting is a common practice in trading and results in candlesticks with the devilish-looking appearance.

Oftentimes, these stop runs are designed to clear out stops ahead of the final, decisive move to a new range.

Related Reading | How the Dows Fractal of Doom Could Take Bitcoin to $1,000

Just a few weeks earlier, a Darth Maul candle hit Bitcoin on smaller timeframes, causing 1% spoke to clear out stops above, followed by a 3% drop lower just minutes later, wiping out the stops below before the final move down was made.

If the same type of price action was driving the devastating candle on the three-month price chart, its appearance could be a sign that stops on the highest timeframes have now been wiped out, and Bitcoin price could be finally ready for its next major move.

The question still remains, however, is that direction up or down?

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Bitcoin Closes Q1 With Historic Darth Maul Candle: Heres What it Means - newsBTC

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Could Coronavirus Kill the Bitcoin Mining Industry? – Finance Magnates

Depending on where you are in the world, you may be on week four, eight, or even thirteen of coronavirus quarantine; while video conferencing in pajama pants may be starting to feel a bit more normal, the world is keenly aware that the full effects and implications of the quarantine have not yet been felt, and will not be fully realized for monthsor even yearsto come.

Just as in much of the financial world, this is particularly true in the cryptocurrency sector. Every weekand, at times, every daythere is a new revelation of the effects of the spread of COVID-19 on various parts of the nascent industry.

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while massive fluctuations in crypto markets are perhaps the most visible part of these effects, there are many other consequences that are somewhat overlookednamely, the effects of coronavirus on the cryptocurrency mining industry.

This has been evidenced by major fluctuations in the hash rate of the Bitcoin network, which measures the amount of computing power that is being devoted to perform mining duties.

On the Bitcoin network, mining is the process by which transactions are confirmedcomputers are chosen by the network solve complex cryptographic equations, which results in transactions being added to the ledger. In exchange for their work, these computers are rewarded with Bitcoins.

The hash rate is an important indicator of the Bitcoin networks health: essentially, the greater the hash rate, the more secure the blockchain is; a higher hash rate means that it is more difficult for hackers to successfully alter the blockchain. A higher hash rate can also mean that transactions sent through the network are validated more quickly.

Therefore, a lower hash rate means slower transaction times: if we look at this from a short term perspective, it is inconvenient as creating the block now takes longer as a result, said Alex Batlin, ex-Blockchain Lead at BNY Mellon and current chief executive of custodial wallet specialist Trustology, to Finance Magnates. Suddenly, blocks that could be mined in 10 minutes now take 20-30 minutes, causing massive issues for the blockchain.

Additionally, some cryptocurrency analysts also believe that hash rate is an indicator of Bitcoins pricethat, though it may take several months, an increase in hash rate is an eventual indicator of an increase in price, and vice versa.

The spread of the coronavirus is already being blamed in part for a steep dive in the Bitcoin networks hash rate that took place throughout the month of March; the BTC hash rate reached its highest rate this year at 150 EH/s on March 5th before plummeting to 105.6 EH/s by March 15th, just 10 days latera 29 percent drop.

Then, on March 26th, the drop continued; i the hash rate dove as much as an additional 15.95 percent, resulting in a 45 percent decline since the peak in January. It has since shown some signs of recovery, but has not neared its higher levels earlier in the month.

The decline is, in part, being attributed to the spread of the coronavirus.

Case in point: a 10-K report that was was filed with the United States Securities and Exchange Commission late last month, Riot Blockchain, a cryptocurrency mining firm based in Castle Rock, Colorado, laid out several scenarios in which fallout from the coronaviruswhich has already begun to affect some of the companys operationscould seriously impair its business.

This is for several reasons: first, quarantined employees cannot perform all of the necessary duties to maintain business-as-usual: [] we have experienced and will experience disruptions to our business operations resulting from quarantines, self-isolations, or other movement and restrictions on the ability of our employees to perform their jobs, the report said.

The firm also pointed to possible issues with its supply chain: China has also limited the shipment of products in and out of its borders, which could negatively impact our ability to receive mining equipment from our China-based suppliers, he said.

Finally, Riot Blockchain said that because we have not been classified as an essential business in the jurisdictions that have decided that issue to date, there is a possibility that we may not be allowed to access our mine or offices.

All of this could result in shutdowns: if we are unable to effectively service our miners, our ability to mine bitcoin will be adversely affected as miners go offline, which would have an adverse effect on our business and the results of our operations.

Riot Blockchains 10-K report scenario is largely hypothetical, but there have been reports from other participants in the mining industry who have said that their operations have been impaired by the spread of the coronavirusthis is particularly true for miners in China, which is still home to the majority of Bitcoins hash power.

Indeed, in early February, PandaMiner, a mining firm based in China, told CoinDesk that quarantine controls had caused disruptions in business as usual: Abe Yang, the companys chief executive, said that not only us, [but] most miner makers have been affected by the outbreak since their factories are based in cities like Dongguan and Shenzhen in Guangdong province.

But quarantined workers and possible disruption in mining supply chains are not the only corona-related reasons that miners may be shutting off their equipment.

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Indeed, the price fallout from the Bitcoin network that has ensued as part of the widespread economic fallout over the last several weeks may have also caused a slowdown in mining: some of the decline in hash rate can be attributed to the possibility that larger mining rigs are also programmed to shut off once the price of Bitcoin passes through set lower limits, and return to full functionality once the price of Bitcoin recovers to a certain level.

However, while these programmatic price limits may have been responsible for some of the decline in hash rate throughout March, it doesnt seem as March 26ths hash rate drop was related to any price event on the Bitcoin networkthe price hovered around $6600 throughout the day, up from a monthly low of approximately $4200 the week prior.

However, the price movements in Bitcoin may have more dire implications for smaller- and medium-sized mining operations, who may have been forced to temporarily (or even permanently) close up shop.

Indeed, Ibrahim Alkurd, the chief executive of New Mine and Partner at Lavaliere Capital, told Finance Magnates that economies of scale play a big factor in mining farms.

The recent price drop in BTC caused smaller mining farms that have more expensive power and machine costs to unplug, he said. Although the bigger mining farms have seen smaller profits after the recent price crash, theyre still running profitably.

Alkurd added that the downward price movements that Bitcoin has experienced as a result of the coronavirus crisis are particularly grizzly because of the upcoming halvening or halving in May 2020, which will result in the mining reward for Bitcoin miners to be cut in half.

This will make it even harder for small farms to compete with the big players Alkurd said.

This could have other, more serious implications for the Bitcoin mining industry over the long term: I expect that we will see more centralisation of mining in the Bitcoin sphere with the May 2020 halving, he continued. The bar of entry to run a profitable mining farm is constantly being raised due to the reducing supply [of mining rewards] because of halving events.

Of course, there is quite a bit of evidence that hash power on the Bitcoin network is already highly centralized: earlier this year, blockchain research firm found that as of 27th January 2020, [five] mining entities controlled 49.9% of the hashrate of the bitcoin network.

Therefore, more centralization at this point could mean that the Bitcoin network is under greater security threats: Alex Batlin told Finance Magnates that in the long-term, lower hash rate increases the risk of a 51% attack on the network.

Its a point in time issue really at the moment, but the bigger issue is more systemic. BTC currently operates on a proof of work model [that requires] significant amounts of electricity and computational needs to operate, and is also very limited in the number of transactions it can process at the same time.

Other networks though are moving to a proof of stake model that doesnt require the massive computation, in terms of cost, power and people to operate. So, the question becomes: should BTC migrate to a proof of stake model like other networks, and will the halving of BTC perhaps force them to make the move? (Easier said than done, perhaps.)

On the other hand, Ibrahim Alkurd told Finance Magnates that although the decline in the price of Bitcoin meant that the profit margins for miners are smaller, it isnt anything out of the ordinary for this market and that theres no effect on the Bitcoin network in terms of security. This volatility in hash rate and price is completely normal for this market.

Experienced miners should factor these swings into their models and be ready to react to them, Alkurd explained, adding that Bitcoin actually performed better than the DOW and the S+P 500 by a considerable amount in Q1 2020.

While the long-term effects of coronavirus on the Bitcoin mining industry may be impossible to avoid, Alkurd says that there are some steps that mining companies may be able to take to abate the effects in the short-term.

For example, instead of in-person maintenance checks, utilize software to remotely monitor machines, Alkurd suggested, adding that adding cameras to mining farms can make remote monitoring easier.

If in-person checks are absolutely necessary, implement social distancing practices within the farms, he added. Companies can also reduce the number of staff in the [facility] at any one time. This can be done by altering the shifts of the workers.

Additionally, mining companies located in countries where the virus has not yet passed its peak can look at China and see what they did wrong and right, and learn from it.

Because of the way this virus has spread, we can use information from countries that have been impacted before us, so we can learn what to expect.

What are your thoughts on the possible short- and long-term effects of the coronavirus on the Bitcoin mining industry? Let us know in the comments below.

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Bitcoin Price Analysis: BTC Surges 9% And Will Set Up A Strong Rally IF We Can Close Above This Pattern – Coingape

Bitcoin increased by a whopping 9% over the past 24 hours of trading as the cryptocurrency surged to a high of around $7,250. It has since dropped lower but is battling to remain above the $6,800 level.

If Bitcoin can actually close above $6,800, this should set up a fantastic 2 weeks ahead as it would be breaking a consolidation pattern that has trapped the cryptocurrency since the market collapse.

Bitcoin Price Analysis

BTC/USD Daily CHART SHORT TERM

Taking a look at the daily chart above, we can see Bitcoin breaking above the triangle today as it reached a high above $7,000. We can see that it even broke slightly past strong resistance at $7,174 which is provided by a bearish .618 Fibonacci Retracement level.

After it reached this level, Bitcoin came back down and is now battling to remain above $6,800 so it can close above the triangle.

As the current candle is still yet to close, we can consider Bitcoin neutral at this moment in time. However, if it manages to close above $6,800 and break the upper boundary of the triangle, the market will turn bullish.

If the bulls can close above the triangle, the first level of resistance lies at $7,000. Above this, resistance is located at $7,174 (bearish .618 Fib Retracement), $7,270 (1.414 Fib Extension), and $7,500.

Beyond $7,500, resistance lies at $7,676 (1.618 Fib Extension) and $8,073 (bearish .786 Fib Retracement).

On the other hand, if the sellers push lower, support lies at $6,572 and $6,100 (.382 Fib Retracement). This is followed with support at the lower boundary of the triangle and then at $6,000 and $5,786 (.5 Fib Retracement).

Support: $6,542, $6,100, $6,000, $5,911, $5,786, $5,636, $5,600, $5,500, $5,467 $5,200, $5,000, $4,800, $4,672, $4,577, $4,139, $4,000, $3,912, $3,500, $3,436.

Resistance: $6,800, $7,000, $7,174, $7,200, $7,270, $7,500, $7,676, $8,000, $8,073, $8,250, $8,461, $8,672, $8,979, $9,000, $9,100.

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Bitcoin Price Analysis: BTC Surges 9% And Will Set Up A Strong Rally IF We Can Close Above This Pattern

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Bitcoin saw an epic 9% price increase over the past 24 hours of trading as the cryptocurrency finally breaks above $6,800.The cryptocurrency had traded higher to reach as high as $7,250 but quickly came back down.

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Yaz Sheikh

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Coin Gape

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Revolut brings Bitcoin to all of its 7 million users – Decrypt

UK challenger bank Revolut has expanded its cryptocurrency offering to all of its users as an alternative to government-backed fiat currencies. As governments around the world push stimulus packages, including the printing of new moneyknown as quantitative easingRevolut argued that Bitcoin offers an alternative, because it has a fixed supply.

During a recent presentation, our Head of Crypto, Edward Cooper, reminded us that cryptocurrencies came about as an alternative to real money during the times of quantitative easing and currency devaluation following the 2018 financial crisis. Given the recent economic upset, we are starting to see quantitative easing and currency devaluation happening again right now, Revolut stated in an email to its customers.

Revolut has made its crypto offering available to all. Image: Shutterstock.

"We had planned to make this official later this year, but in light of recent events, we've decided to give all Revolut customers the opportunity to explore different ways of diversifying, including through crypto, right now," it added.

All Revolut standard users will now be able to use the service to buy and sell Bitcoin and other cryptocurrencies, which they can keep within the app. It will make its Gold feature available to standard users in April too. The app has seven million users according to PYMNTS.

Last week, the US unveiled a $2 trillion stimulus package to quell economic woes. The Federal Reserve has been printing money at a rate of $1 million per secondthats Bitcoins entire market cap every 35 hours.

And it doesnt look like it will end any time soon. Last month, the President of the Federal Reserve Bank of Minneapolis, Neel Kashkari, said that the Federal Reserve has an infinite amount of cash.

"I think once the dust settles of liquidations, margin calls, and outright panic, Bitcoin will start to show its strength against unlimited fiat printing," Alan Silbert, managing director at INX, told Decrypt at the time.

Still, while Revolut's crypto expansion is well timed and designed to appeal to those in the crypto community, not everyone is so eager to pounce on the deal.

According to several Redditors, Revolut's feature involves multiple pitfalls.

"Sadly you cannot move your crypto out of the Revolut ecosystem and into your own wallet. Great news but defeats the purpose of being your own bank," wrote one Redditor.

Another added that a price premium on the service outweighed the benefits. "It costs 200 more than the spot price," they said.

But if theres one thing Revolut cant do, its print new Bitcoin at will.

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Stanford launches an accelerated test of AI to help with Covid-19 care – STAT

In the heart of Silicon Valley, Stanford clinicians and researchers are exploring whether artificial intelligence could help manage a potential surge of Covid-19 patients and identify patients who will need intensive care before their condition rapidly deteriorates.

The challenge is not to build the algorithm the Stanford team simply picked an off-the-shelf tool already on the market but rather to determine how to carefully integrate it into already-frenzied clinical operations.

The hardest part, the most important part of this work is not the model development. But its the workflow design, the change management, figuring out how do you develop that system the model enables, said Ron Li, a Stanford physician and clinical informaticist leading the effort. Li will present the work on Wednesday at a virtual conference hosted by Stanfords Institute for Human-Centered Artificial Intelligence.

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The effort is primed to be an accelerated test of whether hospitals can smoothly incorporate AI tools into their workflows. That process, typically slow and halting, is being sped up at hospitals all over the world in the face of the coronavirus pandemic.

The machine learning model Lis team is working with analyzes patients data and assigns them a score based on how sick they are and how likely they are to need escalated care. If the algorithm can be validated, Stanford plans to start using it to trigger clinical steps such as prompting a nurse to check in more frequently or order tests that would ultimately help physicians make decisions about a Covid-19 patients care.

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The model known as the Deterioration Index was built and is marketed by Epic, the big electronic health records vendor.Li and his team picked that particular algorithm out of convenience, because its already integrated into their EHR, Li said. Epic trained the model on data from hospitalized patients who did not have Covid-19 a limitation that raises questions about whether it will be generalizable for patients with a novel disease whose data it was never intended to analyze.

Nearly 50 health systems which cover hundreds of hospitals have been using the model to identify hospitalized patients with a wide range of medical conditions who are at the highest risk of deterioration, according to a spokesperson for Epic. The company recently built an update to help hospitals measure how well the model works specifically for Covid-19 patients. The spokesperson said that work showed the model performed well and didnt need to be altered. Some hospitals are already using it with confidence, according to the spokesperson. But others, including Stanford, are now evaluating the model in their own Covid-19 patients.

In the months before the coronavirus pandemic, Li and his team had been working to validate the model on data from Stanfords general population of hospitalized patients. Now, theyve switched their focus to test it on data from dozens of Covid-19 patients that have been hospitalized at Stanford a cohort that, at least for now, may be too small to fully validate the model.

Were essentially waiting as we get more and more Covid patients to see how well this works, Li said. He added that the model does not have to be completely accurate in order to prove useful in the way its being deployed: to help inform high-stakes care decisions, not to automatically trigger them.

As of Tuesday afternoon, Stanfords main hospital was treating 19 confirmed Covid-19 patients, nine of whom were in the intensive care unit; another 22 people were under investigation for possible Covid-19, according to Stanford spokesperson Julie Greicius. The branch of Stanfords health system serving communities east of the San Francisco Bay had five confirmed Covid-19 patients, plus one person under investigation. And Stanfords hospital for children had one confirmed Covid-19 patient, plus seven people under investigation, Greicius said.

Stanfords hospitalization numbers are very fluid. Many people under investigation may turn out to not be infected, and many confirmed Covid-19 patients who have relatively mild symptoms may be quickly cleared for discharge to go home.

The model is meant to be used in patients who are hospitalized, but not yet in the ICU. It analyzes patients data including their vital signs, lab test results, medications, and medical history and spits out a score on a scale from 0 to 100, with a higher number signaling elevated concern that the patients condition is deteriorating.

Already, Li and his team have started to realize that a patients score may be less important than how quickly and dramatically that score changes, he said.

If a patients score is 70, which is pretty high, but its been 70 for the last 24 hours thats actually a less concerning situation than if a patient scores 20 and then jumps up to 80 within 10 hours, he said.

Li and his colleagues are adamant that they will not set a specific score threshold that would automatically trigger a transfer to the ICU or prompt a patient to be intubated. Rather, theyre trying to decide which scores or changes in scores should set off alarm bells that a clinician might need to gather more data or take a closer look at how a patient is doing.

At the end of the day, it will still be the human experts who will make the call regarding whether or not the patient needs to go to the ICU or get intubated except that this will now be augmented by a system that is smarter, more automated, more efficient, Li said.

Using an algorithm in this way has potential to minimize the time that clinicians spend manually reviewing charts, so they can focus on the work that most urgently demands their direct expertise, Li said. That could be especially important if Stanfords hospital sees a flood of Covid-19 patients in the coming weeks. Santa Clara County, where Stanford is located, had confirmed 890 cases of Covid-19 as of Monday afternoon. Its not clear how many of them have needed hospitalization, though San Francisco Bay Area hospitals have not so far faced the crush of Covid-19 patients that New York City hospitals are experiencing.

That could change. And if it does, Li said, the model will have to be integrated into operations in a way that will work if Stanford has several hundred Covid-19 patients in its hospital.

This is part of a yearlong series of articles exploring the use of artificial intelligence in health care that is partly funded by a grant from the Commonwealth Fund.

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Stanford launches an accelerated test of AI to help with Covid-19 care - STAT

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Stanford is Using Artificial Intelligence to Help Treat Coronavirus Patients – ETF Trends

Clinicians and researchers at Stanford University are developing ways that artificial intelligence can help identify which patients will require intensive care amid a surge in coronavirus patients. Rather than build an algorithm from scratch, the goal by Stanford experts was to take existing technology and modify it for a seamless transition into clinical operations.

The hardest part, the most important part of this work is not the model development. But its the workflow design, the change management, figuring out how do you develop that system the model enables, said Ron Li, a Stanford physician, and clinical informaticist.

Per a STAT news report, the machine learning model Lis team is working with analyzes patients data and assigns them a score based on how sick they are and how likely they are to need escalated care. If the algorithm can be validated, Stanford plans to start using it to trigger clinical steps such as prompting a nurse to check in more frequently or order tests that would ultimately help physicians make decisions about a COVID-19 patients care.

As more technology flows into fighting the coronavirus pandemic, this can only open up opportunities for investors in healthcare-focused exchange-traded funds (ETFs).

ETF investors can look for opportunities in theHealth Care Select Sector SPDR ETF (NYSEArca: XLV),Vanguard Health Care ETF (NYSEArca: VHT)and theiShares US Medical Devices ETF (IHI).

XLV seeks investment results that correspond generally to the Health Care Select Sector Index. The index includes companies from the following industries: pharmaceuticals; health care equipment & supplies; health care providers & services; biotechnology; life sciences tools & services; and health care technology.

VHT employs an indexing investment approach designed to track the performance of the MSCI US Investable Market Index (IMI)/Health Care 25/50, an index made up of stocks of large, mid-size, and small U.S. companies within the health care sector, as classified under the Global Industry Classification Standard (GICS).

IHI seeks to track the investment results of the Dow Jones U.S. Select Medical Equipment Index composed of U.S. equities in the medical devices sector. The underlying index includes medical equipment companies, including manufacturers and distributors of medical devices such as magnetic resonance imaging (MRI) scanners, prosthetics, pacemakers, X-ray machines, and other non-disposable medical devices.

Another fund to consider is theRobo Global Healthcare Technology and Innovation ETF (HTEC). HTEC seeks to provide investment results that, before fees and expenses, correspond generally to the price and yield performance of the ROBO Global Healthcare Technology and Innovation Index.

The fund will normally invest at least 80 percent of its total assets in securities of the index or in depositary receipts representing securities of the index. The index is designed to measure the performance of companies that have a portion of their business and revenue derived from the field of healthcare technology, and the potential to grow within this space through innovation and market adoption of such companies, products and services.

For more market trends, visitETF Trends.

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Stanford is Using Artificial Intelligence to Help Treat Coronavirus Patients - ETF Trends

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How Artificial Intelligence is Going to Make Your Analytics Better Than Ever – Security Magazine

How Artificial Intelligence is Going to Make Your Analytics Better Than Ever | 2020-03-31 | Security Magazine This website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more. This Website Uses CookiesBy closing this message or continuing to use our site, you agree to our cookie policy. Learn MoreThis website requires certain cookies to work and uses other cookies to help you have the best experience. By visiting this website, certain cookies have already been set, which you may delete and block. By closing this message or continuing to use our site, you agree to the use of cookies. Visit our updated privacy and cookie policy to learn more.

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How Artificial Intelligence is Going to Make Your Analytics Better Than Ever - Security Magazine

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STAT’s guide to how hospitals are using AI to fight Covid-19 – STAT

The coronavirus outbreak has rapidly accelerated the nations slow-moving effort to incorporate artificial intelligence into medical care, as hospitals grasp onto experimental technologies to relieve an unprecedented strain on their resources.

AI has become one of the first lines of defense in the pandemic. Hospitals are using it to help screen and triage patients and identify those most likely to develop severe symptoms. Theyre scanning faces to check temperatures and harnessing fitness tracker data, to zero in on individual cases and potential clusters. They are also using AI to keep tabs on the virus in their own communities. They need to know who has the disease, who is likely to get it, and what supplies are going to run out tomorrow, two weeks from now, and further down the road.

Just weeks ago, some of those efforts might have stirred a privacy backlash. Other AI tools were months from deployment because clinicians were still studying their impacts on patients. But as Covid-19 has snowballed into a global crisis, health cares normally methodical approach to new technology has been hijacked by demands that are plainly more pressing.

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Theres a crucial caveat: Its not clear if these AI tools are going to work. Many are based on drips of data, often from patients in China with severe disease. Those data might not be applicable to people in other places or with milder disease. Hospitals are testing models for Covid-19 care that were never intended to be used in such a scenario. Some AI systems could also be susceptible to overfitting, meaning that theyve modeled their training data so well that they have trouble analyzing new data which is coming in constantly as cases rise.

The uptake of new technologies is moving so fast that its hard to keep track of which AI tools are being deployed and how they are affecting care and hospital operations. STAT has developed a comprehensive guide to that work, broken down by how the tools are being used.

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This list focuses only on AI systems being used and developed to directly aid hospitals, clinicians, and patients. It doesnt cover the flurry of efforts to use AI to identify drug and vaccine candidates, or to track and forecast the spread of the virus.

This is one of the earliest and most common uses of AI. Hospitals have deployed an array of automated tools to allow patients to check their symptoms and get advice on what precautions to take and whether to seek care.

Some health systems, including Cleveland Clinic and OSF HealthCare of Illinois, have customized their own chatbots, while others are relying on symptom checkers built in partnership with Microsoft or startups such as Boston-based Buoy Health. Apple has also released its own Covid-19 screening system, created after consultation with the White House Coronavirus Task Force and public health authorities.

Developers code knowledge into those tools to deliver recommendations to patients. While nearly all of them are built using the CDCs guidelines, they vary widely in the questions they ask and the advice they deliver.

STAT reporters recently drilled eight different chatbots about the same set of symptoms. They produced confusing patchwork of responses. Some experts on AI have cautioned that these tools while well-intentioned are a poor substitute for a more detailed conversation with a clinician. And given the shifting knowledge-base surrounding Covid-19, these chatbots also require regular updates.

If you dont really know how good the tool is, its hard to understand if youre actually helping or hurting from a public health perspective.

Andrew Beam, artificial intelligence researcher

If you dont really know how good the tool is, its hard to understand if youre actually helping or hurting from a public health perspective, said Andrew Beam, an artificial intelligence researcher in the epidemiology department at Harvard T.H. Chan School of Public Health.

Clover, a San Francisco-based health insurance startup, is using an algorithm to identify its patients most at risk of contracting Covid-19 so that it can reach out to them proactively about potential symptoms and concerns. The algorithm uses three main sources of data: an existing algorithm the company uses to flag people at risk of hospital readmission, patients scores on a frailty index, and information on whether a patient has an existing condition puts them at a higher risk of dying from Covid-19.

AI could also be used to catch early symptoms of the illness in health care workers, who are at particularly high risk of contracting the virus. In San Francisco, researchers at the University of California are using wearable rings made by health tech company Oura to track health care workers vital signs for early indications of Covid-19. If those signs including elevated heart rate and increased temperature show up reliably on the rings, they could be fed into an algorithm that would give hospitals a heads-up about workers who need to be isolated or receive medical care.

Covid-19 testing is currently done by taking a sample from a throat or nasal swab and then looking for tiny snippets of the genetic code of the virus. But given severe shortages of those tests in many parts of the country, some AI researchers believe that algorithms could be used as an alternative.

Theyre using chest images, captured via X-rays or computed tomography (CT) scans, to build AI models. Some systems aim simply to recognize Covid-19; others aim to distinguish, say, a case of Covid-19-induced pneumonia from a case caused by other viruses or bacteria. However, those models rely on patients to be scanned with imaging equipment, which creates a contamination risk.

Other efforts to detect Covid-19 are sourcing training data in creative ways including by collecting the sound of coughs. An effort called Cough for the Cure led by a group of San Francisco-based researchers and engineers is asking people who have tested either negative or positive for Covid-19 to upload audio samples of their cough. Theyre trying to train a model to tell the difference, though its not clear yet that a Covid-19 cough has unique features.

Among the most urgent questions facing hospitals right now: Which of their Covid-19 patients are going to get worse, and how quickly will that happen? Researchers are racing to develop and validate predictive models that can answer those questions as rapidly as possible.

The latest algorithm comes from researchers at NYU Grossman School of Medicine, Columbia University, and two hospitals in Wenzou, China. In an article published in a computer science journal on Monday, the researchers reported that they had developed a model to predict whether patients would go on to develop acute respiratory distress syndrome or ARDS, a potentially deadly accumulation of fluid in the lungs. The researchers trained their model using data from 53 Covid-19 patients who were admitted to the Wenzhou hospitals. They found that the model was between 70% and 80% accurate in predicting whether the patients developed ARDS.

At Stanford, researchers are trying to validate an off-the-shelf AI tool to see if it can help identify which hospitalized patients may soon need to be transferred to the ICU. The model, built by the electronic health records vendor Epic, analyzes patients data and assigns them a score based on how sick they are and how likely they are to need escalated care. Stanford researchers are trying to validate the model which was trained on data from patients hospitalized for other conditions in dozens of Covid-19 patients. If it works, Stanford plans to use it as a decision-support tool in its network of hospitals and clinics.

Similar efforts are underway around the globe. In a paper posted to a preprint server that has not yet been peer-reviewed, researchers in Wuhan, China, reported that they had built models to try to predict which patients with mild Covid-19 would ultimately deteriorate. They trained their algorithms using data from 133 patients who were admitted to a hospital in Wuhan at the height of its outbreak earlier this year. And in Israel, the countrys largest hospital has deployed an AI model developed by the Israeli company EarlySense, which aims to predict which Covid-19 patients may experience respiratory failure or sepsis within the next six to eight hours.

AI is also helping to answer pressing questions about when hospitals might run out of beds, ventilators, and other resources. Definitive Healthcare and Esri, which makes mapping and spatial analytics software, have built a tool that measures hospital bed capacity across the U.S. It tracks the location and number of licensed beds and intensive care (ICU) beds, and shows the average utilization rate.

Using a flu surge model created by the CDC, Qventus is working with health systems around the country to predict when they will reach their breaking point. It has published a data visualization tracking how several metrics will change from week to week, including the number of patients on ventilators and in ICUs.

Its current projection: At peak, there will be a shortage of 9,100 ICU beds and 115,000 beds used for routine care.

To focus in-person resources on the sickest patients, many hospitals are deploying AI-driven technologies designed to monitor patients with Covid-19 and chronic conditions that require careful management. Some of these tools simply track symptoms and vital signs, and make limited use of AI. But others are designed to pull out trends in data to predict when patients are heading toward a potential crisis.

Mayo Clinic and the University of Pittsburgh Medical Center are working with Eko, the maker of a digital stethoscope and mobile EKG technology whose products can flag dangerous heart rhythm abnormalities and symptoms of Covid-19. Mayo is also teaming up with another mobile EKG company, AliveCor, to identify patients at risk of a potentially deadly heart problem associated with the use of hydroxychloroquine, a drug being evaluated for use in Covid-19.

Many developers of remote monitoring tools are scrambling to deploy them after the Food and Drug Administration published a new policy indicating it will not object to minor modifications in the use or functionality of approved products during the outbreak. That covers products such as electronic thermometers, pulse oximeters, and products designed to monitor blood pressure and respiration.

Among them is Biofourmis, a Boston-based company that developed a wearable that uses AI to flag physiological changes associated with the infection. Its product is being used to monitor Covid-19 patients in Hong Kong and three hospitals in the U.S. Current Health, which makes a similar technology, said orders from hospitals jumped 50% in a five-day span after the coronavirus began to spread widely in the U.S.

Several companies are exploring the use of AI-powered temperature monitors to remotely detect people with fevers and block them from entering public spaces. Tampa General Hospital in Florida recently implemented a screening system that includes thermal-scanning face cameras made by Orlando, Fla.-based company Care.ai. The cameras look for fevers, sweating, and discoloration. In Singapore, the nations health tech agency recently partnered with a startup called KroniKare to pilot the use of a similar device at its headquarters and at St. Andrews Community Hospital.

As experimental therapies are increasingly tested in Covid-19 patients, monitoring how theyre faring on those drugs may be the next frontier for AI systems.

A model could be trained to analyze the lung scans of patients enrolled in drug studies and determine whether those images show potential signs of improvement. That could be helpful for researchers and clinicians desperate for signal on whether a treatment is working. Its not clear yet, however, whether imaging is the most appropriate way to measure response to drugs that are being tried for the first time on patients.

This is part of a yearlong series of articles exploring the use of artificial intelligence in health care that is partly funded by a grant from the Commonwealth Fund.

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STAT's guide to how hospitals are using AI to fight Covid-19 - STAT

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