The New Merchants of Data: Creating a More Equitable Exchange of Data Between Digital Businesses and their Customers – SPONSOR CONTENT FROM MICROSOFT

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In his book “Who Owns the Future?” digital iconoclast Jaron Lanier, who was named one of the 100 most influential people in the world by Time magazine and is now a Microsoft employee, once pointed out that people should own their online data profile and be compensated if they choose to share some of it.

It was a compelling argument. Until recently, our global economy had been based around two models of value exchange: the first based on the exchange of goods and services, and then later, the exchange of attention in the form of media and entertainment. Now the rise of digital technologies has added a third construct: data equity. This is data that comes through search engines, social media platforms, loyalty points and other digital transactions, such as dynamic cookies.

But as Lanier rightfully questioned — who ultimately owns this data? And could we reshape a digital

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How Hospitals Are Using Patient-Reported Outcomes to Improve Care

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Increasingly, physicians’ every action and outcome is measured and reported. The data-gathering process can be frustrating, and many clinicians are growing skeptical of its clinical value. For them, outcomes measurement may seem like just another reimbursement requirement or process compliance task. However, measuring patient-reported outcomes (PROs) — patients’ own accounting of their symptoms, functional status, and quality of life — can and should be a clinical tool. In the past year, there has been a flurry of announcements by international organizations and governments declaring their commitment to making PROs a centerpiece of quality assessment. As outcomes-measurement programs move from individual hospital-led initiatives to large-scale, top-down efforts, it’s critical that clinicians are engaged in the change and understand the potential for PRO measurement to improve the care they provide.

Here we describe three examples of clinicians who are using outcomes measurement to improve clinical care. Communicating successes like these is a powerful way

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Why Hospitals Need Better Data Science

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Airlines are arguably more operationally complex, asset-intensive, and regulated than hospitals, yet the best performers are doing a better job by far than most hospitals at keeping costs low and make a decent profit while delivering what their customers expect. Southwest Airlines, for example, has figured out how to do well the two operational things that matter most: Keep more planes in the sky more often, and fill each of them up more, and more often, than anyone else. Similarly, winners in other complex, asset-intensive, service-based industries — Amazon, well-run airports, UPS, and FedEx — have figured out how to over-deliver on their promise while staying streamlined and affordable.

These examples are relevant to health care for two reasons.

First, hospital operations are in many ways like airline and airport operations and transportation services. There are many steps in the service operation (check-in, baggage, the security line, gates), high

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Using Technology to Improve Rural Health Care

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Providing health care in rural regions presents unique challenges. For some patients, the closest doctor may be a three-hour drive. Clinicians seeking an expert consult may find there’s no appropriate specialist within 100 miles. And vast distance can hinder the dissemination of best practices and coordination of care. At Sanford Health, one of the largest rural health-care-delivery systems, we’ve tackled this challenge by leveraging an array of technologies to provide high-value care to a population of around 2 million, dispersed across 300,000 square miles in the Dakotas. We’ve adopted a single electronic medical record (EMR) platform, embraced telehealth technologies, developed enterprise-wide departments, and committed to data transparency.

EMR platform. So far, we have rolled out our integrated EMR platform to 45 hospitals and more than 300 clinics. Key to its success in rural care delivery is that we can rapidly disseminate common decision-support tools across the entire network. For

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Analytics Training Isn’t Enough to Create a Data-Driven Workforce

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When it comes to creating a more data-and-analytics-driven workforce, many companies make the mistake of conflating analytics training with data adoption. While training is indeed critical, having an adoption plan in place is even more essential. Any good adoption plan should focus on continual learning. This might include online or recorded refresher sessions; mentors; online resources for questions, feedback, and new ideas; or a certification process. It might even mean rethinking your organization’s structure or core technologies. Based on my experience, here are three ways leaders can shift a company culture from a one-and-done focus on “training” employees in analytics to an “always on” focus on analytics adoption: Form competency centers. At a high level, a competency center is a collection of domain experts who are given a goal to improve agility, foster innovation, establish best practices, provide training (and mentoring), and be a communications engine. These centers should be “owned
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