Three ways analytics are improving clinical outcomes – Microsoft Industry Blogs

hospital management listening to doctor

According to Accenture Digital Health Technology Vision 2017, 84 percent of healthcare executives believe artificial intelligence (AI) will revolutionize the way they gain information. And many health organizations are already taking advantage of technologies such as AI and advanced analytics to gain insights that help them improve clinical treatment processes and outcomes. In fact, there’s a broad spectrum of use cases for clinical analytics.

Anticipating patient needs

Some of the first ways that health organizations have applied clinical analytics: looking for gaps in care and better predicting patient needs.

That’s becoming especially important with managed care models where health organizations receive reimbursement based not just on episodic health services but also on factors like length of stay (LOS) and readmission rates. Health systems are taking advantage of analytics to help them correlate staffing with anticipated patient needs and better coordinate care so they can improve patient outcomes and reduce LOS and readmission rates.

For example, Steward Health Care analyzed multiple types of data—such as CDC, flu, seasonality, and social data—using Microsoft Azure Machine Learning to predict patient volume so they could staff accordingly.

The results have been impressive. The private hospital operator can predict volumes one to two weeks out with 98 percent accuracy. And it reduced the average LOS for patients by one and a half days. In other words, improved nurse scheduling is helping patients get better faster. It has also increased patient satisfaction. All this, plus: Steward Health Care is saving $48 million per year.

Empowering care teams with predictive care guidance

The next level up in clinical analytics is predictive care guidance. A great example comes from Ochsner Health System—where they’ve integrated AI into patient care workflows.

Care teams there get “pre-code” alerts through an Azure-based platform (from our partner Epic) so they can proactively intervene sooner to help prevent emergency situations. The AI tool analyzes thousands of data points to predict which patients face immediate risks.

“It’s like a triage tool,” says Michael Truxillo, Medical Director of the Rapid Response and Resuscitation Team at Ochsner Medical Center, in this article. “A physician may be supervising 16 to 20 patients on a unit and knowing who needs your attention the most is always a challenge. The tool says, ‘Hey, based on lab values, vital signs, and other data, look at this patient now.’”

During a 90-day pilot project with the tool, Ochsner reduced the hospital’s typical number of codes (cardiac or respiratory arrests) by 44 percent. That incredible number demonstrates the impact AI-driven predictive care guidance can have on clinical outcomes.

Accelerating rare disease diagnoses

Yet another example on the clinical analytics continuum is the work we’re doing with Shire and EURORDIS to accelerate the diagnosis of rare disease. Together, we’ve formed The Global Commission to End the Diagnostic Odyssey for Children with a Rare Disease. As part of the commission’s efforts, phenotypic data (the physical presentation of a person) and genomic data are analyzed to gain insights that could help physicians identify and diagnose patients with a rare disease more quickly.

On average, it takes five years before a rare disease patient—of which approximately half are children—receives the correct diagnosis. Harnessing the power of AI-driven clinical analytics, the alliance aims to shorten the multi-year journey that patients and families endure before receiving a rare disease diagnosis. And that’s one of the most important issues affecting the health, longevity, and well-being for those patients and families.

Those are just a few examples of how AI and advanced analytics can transform healthcare and improve clinical outcomes.

Together with our partners, we’re dedicated to learning and growing alongside our customers and helping them achieve the quadruple aim through clinical analytics and other cloud-based health solutions. We’re also committed to helping them meet their security needs and safeguard the privacy of PHI. And our customers have peace of mind when innovating with us thanks to our Shared Innovation Principles that provide clarity around co-creating technology. We value our customers and partners’ expertise and don’t seek to own it. Rather, we help them monetize their technology assets.

However your health organization wants to use—or advance your use of—clinical analytics, you can learn how to take advantage of AI tools and see more real-world use cases in the e-book: Breaking down AI: 10 real applications in healthcare.

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Author: Steve Clarke

AI-enabled network automation supports milliseconds response time

BOSTON — Seconds and minutes no longer qualify as real time when responding to network events. Instead, real-time response takes five to 10 milliseconds — the time it takes to blink an eye.

That’s how long an IT system should take to effectively respond to an event and make the necessary adjustments, said Rohit Mehra, vice president of network infrastructure research at IDC, during IDC’s annual Directions conference this week. Although this response time sounds unachievable, it’s not. The essential piece of the puzzle is network automation with AI integration, Mehra said.

Network automation is nothing new, evident in command-line interfaces, scripting, network fabrics, software-defined networking, software-defined WAN and, more recently, intent-based networking. These are the building blocks toward an autonomous, self-driving network, Mehra said.

“We’re at the phase where AI is starting to enable a different degree of network automation,” he said.

In traditional networks, network teams would configure the network to send alerts if an attack or change occurred. But these configurations didn’t guarantee the team would respond to a possible alert immediately — like if a network admin took a coffee or bathroom break, for example.

Additionally, traditional monitoring capabilities didn’t always provide the visibility enterprises needed to meet application requirements. The network might not capture events or changes in real time, application context was missing, analytics were spotty and management tools were complex, he said.

We’re at the phase where AI is starting to enable a different degree of network automation.
Rohit MehraVP of network infrastructure, IDC

But with automation built into network infrastructure, networks can make real-time decisions and changes for application, network or security events. This real-time response becomes even more critical as autonomous cars, IoT and cloud networking grow more common. Further, third-party tools can integrate with this AI-enabled network automation to improve visibility and analytics.

“This gives us simpler, declarative management and verification policies, and it also gives us networks that can actually enforce and apply intent,” Mehra said.

AI-enabled network automation use cases

Until recently, the industry lacked the computing resources necessary to support AI and machine learning tools. But as computing power and capacity increased, these tools gained the capacity to support automation needs with real-time response, Mehra said.

As these tools developed, Mehra said three use cases have emerged for AI integration with network automation:

  • capacity planning and optimization;
  • network operations; and
  • visibility and security analytics.

Capacity planning and automation focuses on Day One of deployment. With AI-enabled network automation, IT teams get a real-world view of the network before deployment, gain improved network monitoring and provisioning, and they can dynamically optimize traffic flows, Mehra said.

In the second use case, network operations highlight the self-healing aspect of automation capabilities, along with real-time response, he said. These capabilities help the network better meet quality-of-service levels and application performance — and they help the network adjust automatically.

Security is the final driving use case for AI and network automation. AI coupled with the network can better identify anomalies and pinpoint changes in network behavior by using traffic analytics and behavior modeling, Mehra said.

“You can benchmark what your normal behavior is, and the moment your analytics and visibility tools inform you about an anomaly, your system remediates right away,” Mehra said. It filters down to the root cause of the issue.

Approach network automation pragmatically

Although AI with network automation is ripe with potential, Mehra recommended enterprises take a pragmatic approach to automation.

“Be judicious where you use automation,” he said. “Find the right balance between human capabilities and automation capabilities. You need to get automation right when you’re thinking about the network and using automation for mission-critical application needs.”

This encompasses using clean, relevant and secure data when building AI algorithms, as flawed data and algorithms can be detrimental for enterprises. When implemented correctly, network automation with AI will augment IT capabilities, he added.

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For Sale – Watercooled PC (i7 4770K, GTX 980ti, 16GB DDR3, Corsair AX860) 480mm + 420mm Rads

Plan to strip this down. Would like to sell as complete system first.

Price: 1,100 GBP

Case:
Phanteks Enthoo Primo (includes PWM fan controller)

IMG_20190211_125918.jpg

Price and currency: 1,100 GBP
Delivery: Delivery cost is not included
Payment method: BT
Location: Bristol / North Somerset
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Three ways analytics are improving clinical outcomes – Microsoft Industry Blogs

hospital management listening to doctor

According to Accenture Digital Health Technology Vision 2017, 84 percent of healthcare executives believe artificial intelligence (AI) will revolutionize the way they gain information. And many health organizations are already taking advantage of technologies such as AI and advanced analytics to gain insights that help them improve clinical treatment processes and outcomes. In fact, there’s a broad spectrum of use cases for clinical analytics.

Anticipating patient needs

Some of the first ways that health organizations have applied clinical analytics: looking for gaps in care and better predicting patient needs.

That’s becoming especially important with managed care models where health organizations receive reimbursement based not just on episodic health services but also on factors like length of stay (LOS) and readmission rates. Health systems are taking advantage of analytics to help them correlate staffing with anticipated patient needs and better coordinate care so they can improve patient outcomes and reduce LOS and readmission rates.

For example, Steward Health Care analyzed multiple types of data—such as CDC, flu, seasonality, and social data—using Microsoft Azure Machine Learning to predict patient volume so they could staff accordingly.

The results have been impressive. The private hospital operator can predict volumes one to two weeks out with 98 percent accuracy. And it reduced the average LOS for patients by one and a half days. In other words, improved nurse scheduling is helping patients get better faster. It has also increased patient satisfaction. All this, plus: Steward Health Care is saving $48 million per year.

Empowering care teams with predictive care guidance

The next level up in clinical analytics is predictive care guidance. A great example comes from Ochsner Health System—where they’ve integrated AI into patient care workflows.

Care teams there get “pre-code” alerts through an Azure-based platform (from our partner Epic) so they can proactively intervene sooner to help prevent emergency situations. The AI tool analyzes thousands of data points to predict which patients face immediate risks.

“It’s like a triage tool,” says Michael Truxillo, Medical Director of the Rapid Response and Resuscitation Team at Ochsner Medical Center, in this article. “A physician may be supervising 16 to 20 patients on a unit and knowing who needs your attention the most is always a challenge. The tool says, ‘Hey, based on lab values, vital signs, and other data, look at this patient now.’”

During a 90-day pilot project with the tool, Ochsner reduced the hospital’s typical number of codes (cardiac or respiratory arrests) by 44 percent. That incredible number demonstrates the impact AI-driven predictive care guidance can have on clinical outcomes.

Accelerating rare disease diagnoses

Yet another example on the clinical analytics continuum is the work we’re doing with Shire and EURORDIS to accelerate the diagnosis of rare disease. Together, we’ve formed The Global Commission to End the Diagnostic Odyssey for Children with a Rare Disease. As part of the commission’s efforts, phenotypic data (the physical presentation of a person) and genomic data are analyzed to gain insights that could help physicians identify and diagnose patients with a rare disease more quickly.

On average, it takes five years before a rare disease patient—of which approximately half are children—receives the correct diagnosis. Harnessing the power of AI-driven clinical analytics, the alliance aims to shorten the multi-year journey that patients and families endure before receiving a rare disease diagnosis. And that’s one of the most important issues affecting the health, longevity, and well-being for those patients and families.

Those are just a few examples of how AI and advanced analytics can transform healthcare and improve clinical outcomes.

Together with our partners, we’re dedicated to learning and growing alongside our customers and helping them achieve the quadruple aim through clinical analytics and other cloud-based health solutions. We’re also committed to helping them meet their security needs and safeguard the privacy of PHI. And our customers have peace of mind when innovating with us thanks to our Shared Innovation Principles that provide clarity around co-creating technology. We value our customers and partners’ expertise and don’t seek to own it. Rather, we help them monetize their technology assets.

However your health organization wants to use—or advance your use of—clinical analytics, you can learn how to take advantage of AI tools and see more real-world use cases in the e-book: Breaking down AI: 10 real applications in healthcare.

Go to Original Article
Author: Steve Clarke

Election security threats loom as presidential campaigns begin

Never has it been more important to have a mechanism to audit U.S. voting results, but experts say election security risks combined with the weaponization of social media make the task more difficult than ever.

The electronic voting systems used in a number of states are a concern for security experts who have seen serious flaws in these systems. If the 2020 U.S. election results are disputed by a candidate, there must be a clear way to show voting results are accurate to ensure a peaceful transition of government, said Avi Rubin a computer science professor at Johns Hopkins University, during an RSA Conference 2019 session on election hacking.

Lessons learned in election security

In 2000, a very close election and a confusing voting ballot in Florida led to a drawn out, contested presidential election. Afterward, congress appropriated $4 billion for states to implement electronic voting equipment, and technology vendors were eager to provide those systems.

Rubin became involved in election security in 2003 when he reviewed the Diebold e-voting machine source code. Diebold’s voting machine was based on a Windows platform, with a “pretty interface” that allowed voters to use a touchscreen to select candidates, but it “wasn’t developed with rigorous software engineering processes,” according to Rubin.

In 2003, Rubin was alerted to the fact that Diebold Election System’s source code had been accidentally put on an open FTP site. He published a report about the unavoidable security flaws that undermine the election process, just as states such as Maryland were promoting their new, multimillion-dollar electronic voting machines.

“All hell kind of broke loose in Maryland,” Rubin said during the RSA conference session. “The problems we found were things like, they were using encryption functions that were already obsolete, they were using them incorrectly and for things they shouldn’t have been using them for.”

One example is the use of encryption code on contents within the voting machines. “How are you supposed to perform a security analysis of a system if there is encrypted stuff on there?” he said. “They should have been using message authentication codes if they wanted to protect the integrity of things.”

Rubin and his research partners also criticized the use of the smartcards that stored all the voting data for each terminal; there was no authentication of smartcards to voting terminals, and the devices weren’t encrypted. What’s more — trusted election judges used administration cards to void or alter ballots filed in error by using an easily hackable PIN code, he said.

“We found that the PIN for the administrator that was hard-coded into the software was 1234,” Rubin said.

He later became an election judge in Maryland — without background checks or vetting, according to Rubin — and learned how simple it would be for a judge to swap out voter cards and shift election results.

“In my hand I had five [smartcards] … which corresponded to all the votes cast in that precinct,” he said. “Because the source code for the Diebold systems had been available online, I knew the format for those ballots. I could have come in with five cards and swapped the ones we actually used with ones in my pocket, and those would have been the results of the election in my precinct.”

Most states, including Maryland, have moved back to paper ballots, which are manually placed into scanners to tally votes. The scanners may have bugs, and manual audits are necessary — though Rubin said audits aren’t performed nearly enough. Without random audits, the scanners may cause problems — but the method still appears to be a more secure way to conduct voting than with e-voting machines, according to Rubin.

“We are so much better off now because we do have those ballots,” he said. “In contested races, we are able to go back and make recounts.”

Ronald Rivest, a professor in MIT’s Cryptography and Information Security research group, said during a separate session at RSA Conference that keeping it simple with low-tech paper ballots is the lesson learned over the past decade. We still need to know that the tabulation of those ballots is accurate, via audits, and states like Colorado and Rhode Island are piloting new risk-limiting audit systems, Rivest said.

However, voting methods vary by state and some states, including battleground states like Pennsylvania, continue to use direct recording electronic systems that produce no paper trails.

In March 2018, Congress allotted $380 million to help states tighten election security. The funding is to help states acquire more secure voting machines, conduct post-election audits and improve election cybersecurity training.

U.S. election audits are inadequate or need improvement to ensure election security in 2020.
A number of U.S. states have inadequate election auditing processes or need improvement. Security experts caution that election security must improve to ensure peaceful transitions of power.

Weaponization of social media

As critical as it is to ensure voting tallies are accurate, a more insidious election security problem is the amount of misinformation on social platforms that’s generated by foreign adversaries to influence the voting public before they ever punch a ballot.

“Forget whether the machine does the right thing; if [the voters] have already been hacked, then [adversaries] have already won the election,” Rubin said.

In 2016, 83% of Americans were active on social media and many engaged in politics on social platforms, stating their views on candidates or even their voting plans, said James Foster, CEO of ZeroFox, a social media and digital security company.

More than half of the people in an elective base will engage digitally around a candidate, and that boost in online engagement translates into an increase in vulnerability, he said. Social media has been weaponized, and published reports show over 10,000 Department of Defense (DoD) employees were targeted with Tweets carrying malicious links by nation-state threat actors overseas; once DoD employees clicked on the links, malware was downloaded to their devices, which gave threat actors control of their phones, PCs and social media accounts.

As widespread as disinformation efforts were in 2016, the use of text and images to spread political propaganda were rudimentary compared to the next wave, according to Foster.

The real scary stuff comes when you start hacking the minds of individuals [with fake news], and thats the stuff thats difficult to put your arms around, Foster said.

Social media took a lot of the blame for the spread of misinformation in 2016, and Facebook and Twitter have been working to improve authenticity, security and reduce misinformation.

But no social platform has determined how to eradicate fake news completely. Meanwhile, the next generation of misinformation isnt merely text or images — its artificial intelligence-based deepfake videos that make it even more difficult to identify malicious content, Foster said.

“It’s going to be very difficult to identify those kinds of fake videos out there, at scale. We know this because it’s been very hard to identify [malicious activity] in technology that’s much less rich,” he said.

Text content — the simplest kind of media to analyze — still has a high false positive and negative rate. It’s more difficult when OCR has to be used to pull data out of images. Video analysis is even more complex, and exponentially more expensive, Foster explained.

We will see much bigger issues in 2020, Foster said.

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For Sale – HP Desktops and Acer Monitors for Sale

8 Units- HP Desktop Computer Elite 8000 Core 2 Duo E8400 (3.00 GHz) 4 GB DDR3 160 GB HDD Windows 7 Professional 64-Bit

1 Unit- ASUS VS207T-P Black 19.5″ 5ms Widescreen LED Backlight LCD Monitor

7 Units- Acer K2 K202HQL Abd (UM.IX3AA.A04) Black 19.5″ Widescreen LED Backlight Monitors – LCD Flat Panel

All were purchased refurbished and were never used thereafter. All computers come with keyboard and mouse.

Whole Lot Available for $1200, pm if interested.

– Andrew

[​IMG]

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Price and currency: 1200
Delivery: Goods must be exchanged in person
Payment method: venmo, cash
Location: brooklyn, ny
Advertised elsewhere?: Advertised elsewhere
Prefer goods collected?: I prefer the goods to be collected

______________________________________________________
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By replying to this thread you agree to abide by the trading rules detailed here.
Please be advised, all buyers and sellers should satisfy themselves that the other party is genuine by providing the following via private conversation to each other after negotiations are complete and prior to dispatching goods and making payment:

  • Landline telephone number. Make a call to check out the area code and number are correct, too
  • Name and address including postcode
  • Valid e-mail address

DO NOT proceed with a deal until you are completely satisfied with all details being correct. It’s in your best interest to check out these details yourself.

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