香港挂牌2018第72期

请分析一下美原油的走势

来源:我要做网店  作者:军迎月   发表时间:2019-12-16 00:23:41|香港挂牌2018第72期

  

  Ten years ago, the federal government enacted a sweeping bill designed to stimulate growth in the aftermath of the 2008 recession. The American Reinvestment & Recovery Act included guidelines for healthcare providers to move away from paper records and make meaningful use of electronic health records (EHR).

  The result, which you may have seen phased in over the last few years, is that nurses and doctors are walking around hospitals with tablets instead of thick binders of paper, and even the paperwork you fill out at the doctor's office now happens on a screen instead of a clipboard.

  Now that your doctor, your hospital, your regional healthcare system has all this information in digital form, the expectation is that we'll see fewer errors, lower costs, and improved outcomes. But that's not always the case.

  "Healthcare constitutes 18 percent of our GDP in the United States, as well as a multi-trillion dollar space outside. And the costs are spiraling out of control," said Samir Manjure, co-founder and CEO of healthcare analytics startup KenSci.

  According to government estimates, diabetes and heart disease account for the vast majority of healthcare costs in the US, and those costs are rising every year.  The American Diabetes Association estimates that the costs of diagnosed diabetes rose 26% from 5 billion in 2012 to 7 billion in 2017.

  "This is a national problem, in fact, a global problem," Manjure said. "What is even more astonishing is, even after spending so much money in the United States, the health outcomes have not actually improved."

  KenSci was created to tackle this twin challenge of improving healthcare outcomes while lowering costs, using the data generated by all these years of EHR collection.

  "KenSci is dedicated to using AI (artificial intelligence) to fight death with data science," explained Sunny Neogi, the company's chief growth officer. "We manage better healthcare and better outcomes for over 57 million patients worldwide."

  "We provide a risk prediction platform," Manjure added. "We ingest various kinds of data -- and this is patient data, claims data, even billing and ledger data and so on. We bring those data sources, make them amenable to machine learning, and create the system of intelligence so that you can start seeing the future before it is going to happen. Then we apply those insights back into the workflow for various kinds of efficiencies… The end result is basically being able to deliver better patient outcomes while saving money."

  The AI algorithms developed by KenSci look at millions of patient records, hospital charts, billing statements, and more, and find patterns. Certain patient behaviors and clinical decisions lead to a higher risk of hospital readmission, for example. Lowering those risks means fewer people need to visit the hospital a second or third time to treat their illness. Fewer readmissions lead to lower costs and enable the facilities to treat more patients more quickly. The benefits extend from the individual patient level all the way up to the system as a whole, and KenSci works with healthcare systems as near as Southern California and as far as Scotland and Singapore.

  Jeff Lumpkin, VP of analytics at KenSci, demonstrated some of the models used by hospitals:

  "One of the great challenges faced by clinicians is determining which patients they need to focus on. If I select high-risk patients for [heart failure], and high-risk patients for risk of readmission, we've gone from 1,500 patients in this sample down to just 43. Now we know the 43 patients upon whom we should focus. I can email a list of those patients to anyone who needs it."

  Lumpkin also demonstrated the ways KenSci helps physicians tailor treatment plans. If a particular patient shows a high risk-of-readmission score, doctors can see exactly how much her risk is reduced if she improves her medication compliance, normalizes blood glucose, and stops smoking. Doctors can communicate this directly to patients and help them move from high-risk to medium-risk.

  "One of the strongest drivers of our growth has been our ability to bring together two very diverse skill sets: math and medicine," Neogi said. "We're bringing in engineers and data scientists, who really understand data science and math and the industry that supports it, and then we're bringing in doctors and nurses and clinicians and caregivers, to sit next to those people and solve it for healthcare."

  "We do really act as translators between the hospitals and health systems that do not have experience with machine learning, and the data scientists and developers here at KenSci who don't have as much of a background in healthcare," said Carly Eckert, MD, a former surgeon who now serves as medical director at KenSci. "We talk to them about what it means to be a patient, what it means to be a provider and what different contexts of the healthcare environment are like."

  Having medical professionals in the room helps the KenSci developers tune their models to achieve practical goals like reducing readmissions, optimizing ER staff schedules, and even improving the efficiency of hospital pharmacies.

  "If you reduce those inefficiencies and reduce the costs," Manjure said, "that money can then funnel back into better care."

  AI and machine learning are very resource-intensive, which is why many cutting-edge companies host their data stores and run their heavy computing workloads in the cloud. In KenSci's case, Microsoft Azure serves as the platform for its solutions.

  "Cloud technologies have come to a point, from Microsoft and others, where you can innovate very quickly on top of them. So we are not spending three years building the infrastructure, we are spending all of our time building the applications and solutions to solve these problems… In a different context, to get to 57 million patients would have taken a company ten years. We have gotten there in less than ten quarters," Neogi said.

  "Microsoft Azure provides you with utilities for moving the data to the cloud, [and shows] how to organize the data in an efficient, scalable, elastic way. It also provides tools to build rapid applications, prototypes for the internal IT users, as well as unique tools like Power BI for visualization," Manjure said.

  KenSci designers work closely with developers to create graphical interfaces in Power BI that communicate complex analytics results in an understandable way. Power BI also incorporates Microsoft AI seamlessly, enabling users to query the data with natural language and find out what trends are driving specific metrics.   

  "In addition to that, what is unique about Microsoft is that the security, governance, and compliance models are built-in," Manjure added, which ensures that private healthcare data is kept private. "Microsoft helps us with all the building blocks, whether it's storage, compute, cloud-level security, governance, or AI architecture."

  The relationship with Microsoft AI actually extends both ways, with KenSci offering its Clinical Analytics solution as a download in Microsoft's AppSource marketplace, so other customers can leverage it for predicting healthcare risks. "It's a very symbiotic partnership," he said.

  Everyone at KenSci feels a strong sense of purpose and pride in the work they're doing. "This technology has the promise of changing the way the care is delivered, the conversations that happen between the physician and the patient, and very precise nature of what needs to be altered to change the trajectory of the patient," Manjure said. "Imagine the cumulative effect of that at a macro level -- the ways that it changes the outcomes for society at large."

  Neogi concluded: "This is about early intervention. This is about precise prevention. This is about delivering care more cheaply. And the result is, not only does one person have a better outcome in life and live for longer and live better, but on a daily basis he sleeps better, because he is breathing better. At a system level, for the country of Scotland, for example, you have fewer and fewer such patients coming into the hospital and taking up beds, because all you need to do is to call them or do a video conference and set them right. Or, all you need to do is call them ten years before they fall sick... And that's such a new way of thinking about healthcare. We need to do that to take care of the 7 billion, 10 billion patients that we are going to have on the earth 20-30 years from now."

  To learn more about AI and cloud solutions from Microsoft, visit.

B:

  

  香港挂牌2018第72期【一】【直】【到】【夜】【晚】【十】【一】【点】【过】,【二】【杆】【依】【旧】【没】【出】【现】,【我】【只】【得】【非】【常】【难】【堪】【地】【去】【告】【知】【俊】【哥】【今】【天】【二】【杆】【估】【计】【不】【会】【出】【现】【了】…… 【这】【种】【无】【果】【的】【蹲】【守】【俊】【哥】【经】【历】【的】【也】【不】【是】【一】【次】【两】【次】,【他】【拍】【拍】【手】【把】【大】【家】【吆】【喝】【过】【来】,【毕】【竟】【白】【白】【守】【了】【一】【晚】【上】,【总】【得】【给】【大】【家】【一】【点】【甜】【头】。 【说】【罢】,【俊】【哥】【邀】【约】【连】【同】【我】【在】【内】【的】【所】【有】【人】【去】【他】【朋】【友】【的】【宵】【夜】【摊】【吃】【点】【东】【西】,【我】【不】【甘】【心】【地】【看】【了】【看】

【最】【近】【想】【更】【新】,【考】【虑】【了】【一】【阵】【子】,【决】【定】【尽】【量】【发】【免】【费】【章】【节】。5【月】【份】【的】【时】【候】【就】【想】【更】【新】【的】,【结】【果】【遇】【到】【起】【点】【大】【审】【查】,【电】【脑】【端】【看】【不】【到】【我】【的】【作】【品】【了】,【就】【没】【有】【续】,【拖】【到】【现】【在】,【兴】【致】【又】【减】【了】【一】【些】,【不】【过】【还】【是】【有】【点】【想】【写】【的】。

【这】【本】【书】【到】【此】【结】【束】【了】,【我】【也】【不】【给】【予】【太】【多】【的】【评】【论】,【自】【己】【的】【问】【题】。 【新】【书】【的】【话】【在】【十】【一】【月】【份】【会】【上】【传】,【具】【体】【什】【么】【几】【号】【不】【知】【道】,【先】【休】【息】【几】【天】【吧】,【新】【书】【准】【备】【写】《【一】【拳】【超】【人】》【的】【同】【人】,【到】【时】【候】【还】【希】【望】【多】【多】【关】【注】。

  【那】【个】,【新】【书】《【史】【上】【最】【强】【画】【师】【系】【统】》【已】【经】【发】【布】,【大】【家】【可】【以】【看】【看】,【小】【寒】【寒】【感】【激】【不】【尽】。香港挂牌2018第72期“【我】【不】【打】【算】【完】【全】【听】【从】【元】【老】【院】【的】【安】【排】。 【如】【果】【按】【照】【他】【们】【的】【指】【令】,【那】【么】【就】【是】【在】【一】【步】【一】【步】【地】【把】【我】【们】【引】【向】【无】【尽】【的】【蛮】【族】【战】【争】【中】。 【为】【了】【不】【把】【事】【情】【搞】【得】【太】【僵】【的】,【可】【以】【有】【选】【择】【的】【完】【成】【任】【务】。” 【奥】【古】【斯】【都】 “【嗯】,【对】【的】。【就】【用】【阳】【奉】【阴】【违】【的】【计】【策】,【你】【要】【求】【你】【的】,【我】【做】【我】【的】。【顺】【带】【的】【可】【以】【顾】【及】【一】【下】,【与】【我】【们】【的】【目】【的】【背】【道】【而】【驰】【的】【根】【本】【不】

  “【你】【这】【种】【心】【思】【恶】【毒】【的】【家】【伙】,【留】【你】【不】【得】,【今】【天】【我】【就】【将】【你】【就】【地】【诛】【杀】。” 【听】【着】【这】【话】,【张】【维】【海】【终】【于】【确】【定】【了】,【这】【特】【么】【就】【是】【一】【个】【精】【神】【分】【裂】【的】【神】【经】【病】【啊】。 【这】【已】【经】【石】【锤】【的】【不】【能】【再】【石】【锤】【了】。 【没】【看】【到】【对】【方】【说】【这】【番】【话】【的】【时】【候】,【还】【在】【疯】【狂】【的】【对】【他】【打】【眼】【色】【吗】? 【李】【秦】【朝】【自】【然】【也】【明】【白】【自】【己】【此】【时】【的】【状】【态】,【可】【能】【会】【让】【别】【人】【觉】【得】【自】【己】【是】【个】【神】【经】【病】

  【江】【安】【疑】【惑】。 【囚】【牛】【的】【眼】【眸】【盯】【着】【地】【上】【的】【断】【手】,【声】【音】【有】【些】【低】【哑】:“【睚】【眦】【逼】【出】【自】【己】【的】【魂】【魄】,【还】【化】【做】【原】【身】……【似】【乎】【只】【是】【为】【了】【带】【那】【个】【女】【孩】【离】【开】。” 【囚】【牛】【闭】【眼】【叹】【息】:“【虽】【然】【他】【已】【经】【活】【不】【长】【了】,【也】【算】【是】【了】【却】【了】【我】【的】【一】【桩】【心】【愿】,【但】【终】【究】【觉】【着】……【我】【们】【似】【乎】【亏】【欠】【他】【了】【一】【点】【什】【么】……” 【囚】【牛】【说】【话】【云】【里】【雾】【里】,【江】【安】【却】【是】【注】【意】【到】【其】【中】【的】【细】

  【听】【着】【丁】【羽】【的】【说】【话】,【王】【庄】【微】【微】【的】【倒】【吸】【了】【一】【口】【冷】【气】!【自】【己】【好】【像】【感】【悟】【到】【了】【什】【么】! 【一】【名】【医】【生】【不】【喝】【酒】,【是】【因】【为】【有】【着】【相】【当】【的】【职】【业】【操】【守】,【武】【者】【不】【喝】【酒】,【因】【为】【什】【么】?【怕】【控】【制】【不】【住】【自】【己】【的】【血】【气】?【军】【人】【不】【喝】【酒】?【又】【是】【因】【为】【什】【么】?【因】【为】【有】【着】【规】【定】【和】【原】【则】?! 【但】【这】【里】【面】【所】【表】【述】【的】【含】【义】【连】【贯】【的】【看】【起】【来】,【有】【那】【么】【一】【些】【不】【太】【对】【劲】!【至】【少】【不】【符】【合】【现】【在】

编辑:皋清菡

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