Using AI to Improve Electronic Health Records


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Electronic health record systems for large, integrated healthcare delivery networks today are often viewed as monolithic, inflexible, difficult to use and costly to configure. They are almost always obtained from commercial vendors and require considerable time, money, and consulting assistance to implement, support and optimize.

The most popular systems are often built around older underlying technologies, and it often shows in their ease of use. Many healthcare providers (including the surgeon and author Atul Gawande) find these systems complex and difficult to navigate, and it is rare that the EHR system is a good fit with their preferred care delivery processes.

As delivery networks grow and deploy broad enterprise EHR platforms, the challenge of making them help rather than hinder clinicians is increasing. Clinicians’ knowledge extends far beyond their clinical domain — care procedure knowledge, patient context knowledge, administrative process knowledge — and it’s rare that EHRs

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Using Behavioral Nudges to Treat Diabetes


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Health care practitioners and payer organizations increasingly use big data to overcome what might be called a “flaw of averages” in traditional medicine: a treatment that has been tested at a population level might in fact work better for some individuals than others. The goal of precision medicine is therefore to identify treatments appropriate to an individual — rather than a population — based on granular genotype and phenotype data from his or her medical records. The individual data-driven nature of such treatment protocols improves the odds that a specific treatment will work for a specific patient.

But both traditional and precision medicine confront a “last mile problem” involving patient behavior change: even the most appropriate medical treatment will be effective only if the patient follows through on it. The cost of medication non-adherence is conservatively estimated at more than $250 billion a year

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How B2B Software Vendors Can Help Their Customers Benchmark


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Knowing which organizations perform the best on any particular dimension used to require subjective surveys or painstaking research. Today, the data to answer those questions exists — it’s captured by the software-as-a-service firms whose services companies use to run their businesses. Mainstream software companies are beginning to hold “data mirrors” up to their customers, allowing scoring and benchmarking of their customers’ strategies. We’ve already seen that it’s possible to use external data to evaluate firms on what business models they are employing, and what those business models mean for their valuations.  Those analyses rely on publicly available data sources, but software providers have accumulated growing amounts of private data on almost every aspect of their customers’ technology, operations, people, and strategies. It’s time that these data accumulators begin to share insights back to the creators of all this data, and several firms are beginning

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Before Automating Your Company’s Processes, Find Ways to Improve Them


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One of the most recent automation technologies to emerge is robotic process automation, or RPA. RPA is a category of software tools that enable complex digital processes to be automated by performing them in the same way a human user might perform them, using the user interface and following a set of predefined rules. What sets RPA apart from other automation technologies is that its ability to imitate a human user of one or more information systems reduces development time and extends the range of functions that can be automated across a much wider range of business activities. It is frequently used to automate financial processes, such as comparing invoices with shipment notices, or transfering data from email and call center speech-to-text systems into transactional systems of record. Many organizations have adopted it for automating back- and middle-office processes, and many have achieved rapid returns on their investments.

To

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AI Will Change Radiology, but It Won’t Replace Radiologists


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Recent advances in artificial intelligence have led to speculation that AI might one day replace human radiologists. Researchers have developed deep learning neural networks that can identify pathologies in radiological images such as bone fractures and potentially cancerous lesions, in some cases more reliably than an average radiologist. For the most part, though, the best systems are currently on par with human performance and are used only in research settings.

That said, deep learning is rapidly advancing, and it’s a much better technology than previous approaches to medical image analysis. This probably does portend a future in which AI plays an important role in radiology. Radiological practice would certainly benefit from systems that can read and interpret multiple images quickly, because the number of images has increased much faster over the last decade than the number of radiologists. Hundreds of images can be taken for one patient’s disease

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Why So Many High-Profile Digital Transformations Fail


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In 2011, GE embarked upon an ambitious attempt to digitally transform its product and service offerings. The company created impressive digital capabilities, labeling itself a “digital industrial” company, embedding sensors into many products, building a huge new software platform for the Internet of Things, and transforming business models for its industrial offerings. GE also went to work on transforming internal processes like sales and supplier relationships. Some performance indicators, including service margins, began to improve.  The company received much acclaim for its transformation in the press (including some from us).

However, investors didn’t seem to acknowledge its transformation. The company’s stock price has languished for years, and CEO Jeff Immelt—a powerful advocate of the company’s digital ambitions—recently departed the company under pressure from activist investors. Other senior executives have left as well. The new CEO, John Flannery, is focused primarily on cutting costs.

GE is hardly

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Big Companies Are Embracing Analytics, But Most Still Don’t Have a Data-Driven Culture


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For six consecutive years NewVantage Partners has conducted an annual survey on how executives in large corporations view data. Each year the response rate increases, and the reported urgency of making effective use of data increases as well. This year the results are both more encouraging and more worrisome than in the past.

Six years ago, the primary focus of questions and answers in the survey was big data, which was relatively new on the business scene. In the 2018 survey, the primary attention has moved to artificial intelligence. AI is now a well-established focus at these large, sophisticated firms. There is both a stronger feeling that big data and AI projects deliver value and a greater concern that established firms will be disrupted by startups.

The survey includes senior executives from 57 large corporations. The industry group with the most firms represented in the survey is one

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Robo-Advisers Are Coming to Consulting and Corporate Strategy


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Does a robot manage your money?  For many of us, the answer is yes. Online and algorithmic investment and financial advice is easy to come by these days, usually under the moniker of “robo-advisor.” Startups such as Wealthfront, Personal Capital, and Betterment launched robo-advisors as industry disruptors, and incumbents, such as Schwab’s (Intelligent Advisor), Vanguard (Personal Advisor Services), Morgan Stanley and BlackRock have joined the fray with their own hybrid machine/advisor solutions. It’s clear that robo-advisors and AI play an important and growing role in the financial services industry, but a question remains.  Will robo-advisors disrupt corporate capital allocation the same way they have personal capital allocation? And, will they shake up the trillion-dollar corporate consulting and advisory industry?

Robo-advisors, which were introduced in 2008, are steadily eating up market share from their human counterparts much the way that Amazon and Netflix

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How Machine Learning Is Helping Morgan Stanley Better Understand Client Needs


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Systems that provide automated investment advice from financial firms have been referred to as robo-advisers. While no one in the industry is particularly fond of the term, it has caught on nonetheless. However, the enhanced human advising process — augmented by machine learning — that was recently announced by Morgan Stanley goes well beyond the robo label, and may help to finally kill off the term.

New York–based Morgan Stanley, in business since 1935, has been known as one of the more human-centric firms in the retail investing industry. It has 16,000 financial advisors (FAs), who historically have maintained strong relationships with their investor clients through such traditional channels as face-to-face meetings and phone calls. However, the firm knows that these labor-intensive channels limit the number of possible relationships and appeal primarily to older investors (according to a Deloitte study, the average wealth management client in the U.S. across the

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How Analytics Has Changed in the Last 10 Years (and How It’s Stayed the Same)


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Ten years ago, Jeanne Harris and I published the book Competing on Analytics, and we’ve just finished updating it for publication in September. One major reason for the update is that analytical technology has changed dramatically over the last decade; the sections we wrote on those topics have become woefully out of date. So revising our book offered us a chance to take stock of 10 years of change in analytics.

Of course, not everything is different. Some technologies from a decade ago are still in broad use, and I’ll describe them here too. There has been even more stability in analytical leadership, change management, and culture, and in many cases those remain the toughest problems to address. But we’re here to talk about technology. Here’s a brief summary of what’s changed in the past decade.

The last decade, of course, was the era of big

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How P&G and American Express Are Approaching AI


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There is a tendency with any new technology to believe that it requires new management approaches, new organizational structures, and entirely new personnel. That impression is widespread with cognitive technologies — which comprises a range of approaches in artificial intelligence (AI), machine learning, and deep learning. Some have argued for the creation of “chief cognitive officer” roles, and certainly many firms are rushing to hire experts with deep learning expertise. “New and different” is the ethos of the day.

But we believe that successful firms can treat cognitive technologies as an opportunity to evolve or grow from previous work. For firms that have been producing results with big data analytics, machine learning isn’t too much of a stretch. If firms had previous experience with expert systems, they are familiar with some of the necessary organizational and process changes arising from contemporary cognitive tools. These firms are likely to have already

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Why Trump Doesn’t Tweet About Automation


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Since winning the U.S. presidency in November, Donald Trump has tweeted frequently about job loss. He’s railed against corporations that plan to move jobs to Mexico or China. He’s taken credit for persuading companies, including Ford and Carrier Corporation, for keeping jobs at home. He’s been unalterably opposed to both outsourcing and big trade deals like NAFTA and the Trans-Pacific Partnership (TPP) because of the U.S. job losses they might engender.

Automation, however, does not seem to be on his mind at all. He doesn’t talk or tweet about it, nor does he complain about job losses that result from it. Yet economists, including the MIT labor economist David Autor, argue that automation is a much greater factor in manufacturing job loss than outsourcing or trade deals is. And as I and others have argued, new automation technologies are going to have similar impacts on non-manufacturing workers.

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Why Trump Doesn’t Tweet About Automation


This post is by Thomas H. Davenport from HBR.org


Click here to view on the original site: Original Post




jan17-12-648822913

Since winning the U.S. presidency in November, Donald Trump has tweeted frequently about job loss. He’s railed against corporations that plan to move jobs to Mexico or China. He’s taken credit for persuading companies, including Ford and Carrier Corporation, for keeping jobs at home. He’s been unalterably opposed to both outsourcing and big trade deals like NAFTA and the Trans-Pacific Partnership (TPP) because of the U.S. job losses they might engender.

Automation, however, does not seem to be on his mind at all. He doesn’t talk or tweet about it, nor does he complain about job losses that result from it. Yet economists, including the MIT labor economist David Autor, argue that automation is a much greater factor in manufacturing job loss than outsourcing or trade deals is. And as I and others have argued, new automation technologies are going to have similar impacts on non-manufacturing workers.

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Wall Street Jobs Won’t Be Spared from Automation


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I participated in a cognitive technologies conference a few days after the November 8 election, and much of the talk at breaks (and some on the stage) was about the election results and the reasons behind them. There was a general feeling that the “Rust Belt” had been largely responsible for Donald Trump’s victory, and a growing, if belated, understanding of the economic plight of some citizens from that Midwestern region.

Some conference participants were concerned that this beleaguered region might grow. In fact, one attendee — an old friend who strategizes about technology for a big New York bank — commented that perhaps Wall Street would become “the new Rust Belt.” His concern was that automation of the finance industry would hollow out jobs in that field in the same way that robotics and other technologies have reduced manufacturing employment.

This is a sobering prospect, but there is

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Move Your Analytics Operation from Artisanal to Autonomous


This post is by Thomas H. Davenport from HBR.org


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Many organizations today are wondering how to get into machine learning, and what it means for their existing analytics operation.

There are many different types of machine learning, and a variety of definitions of the term. I view machine learning as any data-driven approach to explanations, classifications, and predictions that uses automation to construct a model. The computer constructing the model “learns” during the construction process what model best fits the data. Some machine learning models continue to improve their results over time, but most don’t.

Machine learning, in other words, is a form of automating your analytics. And it has the potential to make human analysts wildly more productive.

To illustrate the movement from “artisanal analytics” to “autonomous analytics,” I’ll provide an (anonymous) detailed example. The company involved is a large, well-known technology and services vendor, with over 5 million businesses as customers, 50 major product and service categories,

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7 Ways to Introduce AI into Your Organization


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I’m teaching a new course this semester on cognitive technologies (AKA artificial intelligence) to Babson MBAs. Many of them are new to this set of technologies, and seeing the topic through my students’ eyes has made me realize how overwhelming it can be. There are so many different types of AI, each requiring some technical knowledge to fully grasp, that newcomers to the field often have difficulty figuring out how to jump in.

In the simplest case, cognitive technologies can be just more autonomous extensions of traditional analytics — automatically running every possible combination of predictive variables in a regression analysis, for example. More complex types of cognitive technology — neural or deep learning networks, natural language processing, and algorithms — can seem like black boxes even to the data scientists who create them.

Insight Center

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    How robotics and machine learning are changing business.

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Will AI Companies Make Any Money?


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I was recently consulting with a publishing company that is exploring various ways to digitize and contextualize its content. Knowing that some of the company’s competitors had signed deals with IBM’s Watson, I asked several executives why they had not done a Watson deal themselves. “We think that the market for AI software is rapidly commoditizing, and we believe we can assemble the needed capabilities ourselves at much lower cost,” was this company’s party line. Some particularly knowledgeable managers mentioned that they expected the company would instead make use of open source cognitive software made available from various providers. These potential open source providers are not small vendors; they include, for example, Google, Facebook, Microsoft, Amazon, and Yahoo.

Upon hearing this company’s strategy, I was initially a bit surprised. Could machines that can think already be so cheap and available? How could the cognitive software market be commoditized when the

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