Analytics & Social media impact on Healthcare
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Analytics & Social media impact on Healthcare
A view on how analytics and social media is used for shaping the healthcare industry
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The role of analytics in the evolving healthcare market

The role of analytics in the evolving healthcare market | Analytics & Social media impact on Healthcare | Scoop.it
With healthcare spending at about $3 trillion per year and accounting for nearly a fifth of gross domestic product (GDP), managing costs and improving outcomes are top priorities for healthcare providers, insurance companies and consumers alike.

 

KMWorldinterviewed five experts in the field, who offered insights into how business intelligence solutions can help organizations take on the challenge of a new and sometimes confusing environment.

Those interviewed by Judith Lamont, KMWorld senior writer, include John Carew, assistant VP, advanced analytics for Carolinas HealthCare System; Michael Corcoran, chief marketing officer, Information Builders; Graham Hughes, M.D., chief medical officer, Center for Health Analytics and Insights, SAS; Vi Shaffer, research VP at Gartner; and Alex White, managing director for corporate finance/restructuring, FTI Consulting.

Q Lamont: What are the most significant driving forces in healthcare today?

A Hughes: Multiple forces are putting pressure on the healthcare system, but the biggest change is the unstoppable shift from volume to value. Traditionally, revenue in the healthcare industry has been a function of the number of products and services provided. The Affordable Care Act (ACA) is requiring a focus on outcomes—keeping patients healthy. This means that healthcare has to pivot and make some dramatic changes in its business model.

 

A Shaffer: Another major factor is the shift in demographics. We are dealing with the diseases of an aging population as the baby boomers hit 65 and above, as well as a range of chronic diseases. It requires a different continuum of care. We are seeing innovative changes in how healthcare is paid for and delivered. Providers have more incentives to keep patients healthy.

A White: The method of care is also shifting, with greater emphasis on outpatient care and the care continuum. These changes represent good opportunities for improving the quality of care because of the continuity across multiple settings, and also for cost savings—for example, by eliminating redundancy in diagnostics and treatment or identifying health risks earlier. That, combined with new technologies such as wireless sensors and mobile devices, means there is a tidal wave of data that people are struggling to capture and analyze from across a host of care settings.

Q Lamont: How are analytics solutions helping to address these issues?

A Hughes: Understanding individual risks as well as the risks within population is a data-driven exercise. Healthcare providers who are being rewarded for value will have to measure whether they are achieving the patient outcomes that are expected of them, and evaluate the extent to which they are proactively managing the risk of the patients they are taking accountability for.

A White: Multiple studies have shown that around a third of the nearly $3 trillion the United States spends on healthcare is wasted. As the paradigm shifts from volume- to value-based reimbursement, that means companies that are well positioned to identify and reduce inefficiencies should, in theory, be at an advantage. The bet that many are making is that analytics can help them do that—whether it's identifying unwarranted use of interventions, reducing fraud and abuse or managing care more effectively.

A Shaffer: Many of the innovations in treatment and reimbursement depend on technology for analytics, use of electronic records and monitoring seriously ill patients outside the hospital. More information is available about the patient, which allows better analysis of what approaches are most effective. Information is a strategic asset, and it must be infused rapidly to drive the clinical process, because there is a lot of downward pressure on costs now.

Q Lamont: From the viewpoint of a healthcare provider, how is your organization responding to these challenges?

A Carew: Carolinas HealthCare System is a non-profit healthcare system with 40 hospitals and 900 provider locations, as well as home healthcare, skilled nursing and hospice care. We are doing extensive analyses to measure quality of care across this continuum, evaluating outcomes and costs. We also need to understand which patients are at risk for developing severe or chronic illnesses, and try to avert those conditions.

Q Lamont: What is an example of an analysis that is being done right now in response to changes in healthcare regulations?

A Carew: We are looking very carefully at hospital readmissions, because that is one of the provisions of the ACA that has already been implemented. If a patient is readmitted within 30 days of a hospital stay, there is an impact on reimbursement. One of the strongest predictors of readmission is past utilization, so we monitor those statistics as well as other factors in ?the patient's environment, such as ?family support.

Q Lamont: What sort of interventions are you conducting in response to this information?

A Carew: When we identify a high-risk patient, we have several approaches. One is a program called TeachBack. This program assists with health literacy, meaning the ability of an individual to understand his or her condition and cope effectively with it. TeachBack explains the disease and how to take medication, and then the patient explains it back to the provider. We use this method because teaching is one of the most effective ways for someone to gain mastery of information.

A Hughes: It is important to address patient engagement head-on as part of multiyear population management strategy. For example, a 14-year-old diabetic does not interact with the healthcare system the same way as an acutely ill 70-year-old. Even though analytics-powered customer engagement approaches are common in other industries, its adoption in today's healthcare system is very immature, and only the pioneers are experimenting with these technologies.

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Converting Big Data And Analytics Insights Into Results

Converting Big Data And Analytics Insights Into Results | Analytics & Social media impact on Healthcare | Scoop.it
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Chaturika Jayadewa's curator insight, October 31, 2013 3:36 PM

In today’s competitive marketplace, executive leaders are racing to convert enterprise insights into meaningful results. Successful leaders are infusing analytics throughout their enterprises to drive smarter decisions, enable faster actions and optimize outcomes.

In this exciting new piece of research, the IBM Institute of Business Value surveyed 900 business and IT executives from 70 countries. Through our research, we identified nine levers that together enable organizations to create value from an ever-growing volume of data from a variety of sources – value that results from insights derived and actions taken at every level of the organization.

By examining the leaders, the top 19 percent who identified as substantially outperforming their industry and market, the study offers insight into how these nine levers are crucial in gaining optimal value from analytics. Three key insights emerged:

 

Analytic implementation strategies need to support business objectivesThe technology in place needs to support the analytics strategyThe organization’s culture needs to evolve so people take action on the strategy and technology.
Paulo Machado's curator insight, January 9, 2014 10:50 AM

The capture & conversion of health data into actionable insight will drive the transformation of healthcare delivery & our culture of Health & Wellness.

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Cognitive computing project that enables more natural interaction between physicians, data and electronic medical records: WatsonPaths

Cognitive computing project that enables more natural interaction between physicians, data and electronic medical records: WatsonPaths | Analytics & Social media impact on Healthcare | Scoop.it
Learn about a new cognitive computing project that enables more natural interaction between physicians, data and electronic medical records.
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Enabling organizational strategies with analytics could drive better patient outcomes

Enabling organizational strategies with analytics could drive better patient outcomes | Analytics & Social media impact on Healthcare | Scoop.it

 

 

Complete White paper: https://www14.software.ibm.com/webapp/iwm/web/signup.do?source=ind-enurture_short_form&S_PKG=ov18071&ce=ISM0219&ct=swg&cmp=ibmsocial&cm=h&cr=is&ccy=us

 

 

While almost two-thirds of organizations across the healthcare ecosystem have analytics strategies in place, our research shows that only a fifth are driving analytics adoption across the enterprise.

 

 

The IBM Institute for Business Value has been listening to what members of the healthcare ecosystem around the world have been saying about their experiences with analytics.  We have surveyed 555 executives within the healthcare industry and are about to launch our latest point-of-view, Analytics across the ecosystem: A prescription for optimizing healthcare outcomes. This blog briefly explores just one of the aspects covered in the paper; ‘Importance of enabling organizational strategies with analytics’

 

The healthcare ecosystem is the convergence of otherwise separate entities, such as life sciences organizations, providers and payers, as well as social and government agencies. Going foreword, gaining and sharing meaningful insights from data across the entire healthcare ecosystem will be a necessity to correlate cost and quality of care. For example, increased interaction among providers, payers, life sciences organizations and patients can help reduce unplanned adverse events. Patients can benefit from more individualized care. Insights from analytics can facilitate continuous learning and promote quality improvement. However, organizations are still struggling with using advanced analytics for gaining such insights. Only 34% of our study’s respondents said they think in terms of analytics that can help gain actionable insight from data.

 

Enabling organizational strategies using analytics can lead to a significant impact. For example, in a recent IBM Institute for Business Value study about big data, the percentage of respondents in the healthcare and life sciences industries reporting a competitive advantage from analytics rose from 35% in 2010 to 72% in 2012, a 106% increase in two years.

To derive the most value, analytics must become an increasingly important factor in corporate strategy decisions. To position analytics accordingly, organizations must define the enabling analytics strategy, prioritize their roadmaps to address internal requirements and create strategies for future collaborative partnerships across the healthcare ecosystem. A comprehensive plan for governance is a foundational to drive adoption of any analytics strategy. High-level sponsorship of key analytics projects is an important success factor. The most effective analytics initiatives embed small, action-oriented analytics into key decision points of specific business processes that are used widely across the ecosystem. Metrics to measure success should be in place from day one and be tracked. To get the most out of these projects, organizations should focus on early insights that enable refinement of processes over time.

 

The point-of-view will explore this topic in further detail taking into context the requirements within the organization as well as across the entire ecosystem. Read the paper to learn more and discover the three areas of focus that can have a dramatic impact on your organization and entire ecosystem.

 

 

White paper: https://www14.software.ibm.com/webapp/iwm/web/signup.do?source=ind-enurture_short_form&S_PKG=ov18071&ce=ISM0219&ct=swg&cmp=ibmsocial&cm=h&cr=is&ccy=us

 

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Solving Healthcare's Big Data Analytics Security Conundrum

Solving Healthcare's Big Data Analytics Security Conundrum | Analytics & Social media impact on Healthcare | Scoop.it

CIO — Big data holds much promise for healthcare. Analytics use cases — which focus on heady tasks such as giving physicians more information at the point of care, reducing hospital readmissions and better treating chronic diseases — continue to emerge, while vendors such as SAP and Oracle increasingly pitch their in-memory platforms as the solution to solving healthcare's exceedingly complex problems.

Most of medicine's data is unstructured, though. It exists largely in free-form physician notes fields in electronic health record (EHR) systems or, worse, in manila folders. On top of that, the complexities of interoperability and health information exchange make it difficult for healthcare organizations to share information, structured or otherwise.

There's another, often overlooked wrinkle: Much of that data is personal health information strictly protected by the Health Insurance Portability and Accountability Act, which the HIPAA omnibus rule recently strengthened to bring PHI security into the 21st century.

Similar to this ArticleCan Healthcare Big Data Reality Live Up to Its Promise?6 Big Data Analytics Use Cases for Healthcare IT11 Ways to Make Healthcare IT Easier

This means tomorrow's data scientists, not to mention today's, must make the task of keeping patient data secure as much of a priority as actually analyzing that data in order to improve outcomes and reduce costs.

 

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How IBM's Watson Will Transform Business And Society

How IBM's Watson Will Transform Business And Society | Analytics & Social media impact on Healthcare | Scoop.it

After Watson won on the TV quiz show Jeopardy!, a lot of people didn’t really understand what “Watson” was. They thought it was a particular piece of hardware: a glowing blue supercomputer that IBM built in one of its labs.

But now, as Watson comes of age and makes the transition from science experiment to a force to be reckoned with in business and society, I think it’s time to give people a new way of thinking about it. So here goes:

Watson is a cognitive capability that resides in the computing cloud — just like Google and Facebook and Twitter. This new capability is designed to help people penetrate complexity so they can make better decisions and live and work more successfully. Eventually, a host of cognitive services will be delivered to people at any time and anywhere through a wide variety of handy devices. Laptops. Tablets. Smart phones. You name it. 

 

In other words, you won’t need to be a TV producer or a giant corporation to take advantage of Watson’s capabilities. Everybody will have Watson — or a relative of the Watson technologies — at his or her fingertips.

Indeed, Watson represents the first wave in a new era of technology: the era of cognitive computing. This new generation of technology has the potential to transform business and society just as radically as today’s programmable computers did so over the past 60+ years. Cognitive systems will be capable of making sense of vast quantities of unstructured information, by learning, reasoning and interacting with people in ways that are more natural for us.

You may be familiar with the first steps for Watson after the Jeopardy! victory. Our scientists and engineers have been working with Memorial Sloan Kettering Cancer Center, Cleveland Clinic,WellPoint and other healthcare institutions. The goal is to help professionals and organizations deal with the deluge of medical information and transform how medicine is taught, practiced and paid for. For patients, the quality and speed of care will be improved through individualized, evidence based medicine.

But healthcare is just the start. IBM is working with companies in a wide range of industries to bring new cognitive capabilities to the way they do business. In a next step, we recently announced a new service called IBM Watson Engagement Advisor, which is being used by companies in retail, banking, insurance and telecommunications, to crunch big data in real time and transform the way they engage clients via customer service, marketing and sales.

Many more applications will come:

–In a big city, cognitive systems will help city leaders react, prioritize and respond to citizens more effectively by using data to gain insights into complex systems.

–In the home, intelligent assistant apps on smart phones will help elderly citizens and their health care providers better manage chronic diseases and promote wellness.

–In companies, cognitive systems will help engineers and designers create new products and services that respond better to the demands of consumers or even anticipate their needs.

IBM will create some of these services and continue to play a major role as the cognitive era unfolds. Our clients will embed Watson-like technologies in many aspects of how they run their businesses: from supply chain management and manufacturing, to accounting and market research.

We also anticipate that many other companies will develop new capabilities enabled by cognitive technologies. In addition, independent software and services companies will build new cognitive services on top of IBM’s technology platform. You can think of these as cognitive apps, just like Apple offers apps made by others to run on its iPhones and iPads.

So, don’t think of Watson as something that’s locked up in a box. Rather, think of it as a cloud service, available anywhere. And think of it as the foundation for an ecosystem of innovative companies — all of them focused on bringing new capabilities to individuals, businesses and society.

If you’re like to learn more about cognitive technologies and their impact on the world, you can download a free chapter of the upcoming book, Smart Machines: IBM’s Watson and the Era of Cognitive Computing, by IBM Research Director John E. Kelly III.

As IBM General Manager of Watson Solutions, Manoj Saxena is responsible for the commercialization efforts of IBM’s Watson technology globally. Prior to this role, Saxena held several other leadership positions at IBM. Before joining IBM in 2006, Saxena was an active member of the IT venture capital community and led two successful venture-backed software companies. 


  
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Why physicians' performances benefit from healthcare data analytics

Why physicians' performances benefit from healthcare data analytics | Analytics & Social media impact on Healthcare | Scoop.it
Feedback in the form of healthcare data analytics is one way to promote better preventative care and motivate physicians who are performing poorly.

 

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Doctors Use Social For Continuous Medical Education

Doctors Use Social For Continuous Medical Education | Analytics & Social media impact on Healthcare | Scoop.it
There are serious medical conversations going on every day on Twitter, squeezed in between the celebrity news and the millions posting what they had for lunch. To find them, just search for #FOAMed.

The hashtag refers to the concept of Free Open Access Meducation (medical education), or FOAM, first promoted at the 2012 International Conference on Emergency Medicine in a lecture by Mike Cadogan, an emergency medicine physician, educator and digital media enthusiast from Australia. Frustrated by the resistance of many physicians and medical educators to the serious potential of social media, he decided to rebrand what he and others were doing online as a form of continuing education.

 

 

I'd always seen blogging and podcasting as an amazing medium to use for medical education," Cadogan said in a Skype interview. He saw the rebranding as a way to "get people on board with something they felt was very beneath them."

The past year has seen proliferating use of the hashtag and specialty-specific variants on it (such as #FOAMcc for critical care doctors). While the Twitter feed itself, with its 140-character limit, doesn't lend itself to in deep exploration, it's acting as a carrier wave for broader conversations a click away in blogs, podcasts, videos and video chats. While this information has not gone through the same peer review filters as an article in a medical journal, enthusiasts say it is often more current and useful -- not necessarily for major research findings but as a way to share practical tips on techniques for the everyday practice of medicine.

 

"We've actively managed to engage a large group of researchers and significant academics who are moving away from writing textbooks and journal articles to doing more in the online arena," Cadogan said. "That's lending a sense of credence to what we're doing."

"The journals are still an essential part of the culture we work in," he allowed, but medical education is starting to be influenced by the open source and open content trends on the Internet, where "you take all the simple stuff, all the basic knowledge, and make it free." As an author of medical textbooks himself, Cadogan has decided it is more productive for him to spend his time blogging than to produce a new edition of one of those books.

Textbooks tend to be "outdated and expensive," whereas information gleaned from blogs and wikis can be better for fostering a "lifelong learning habit," said Michelle Lin, an associate professor of clinical emergency medicine at the University of California San Francisco and a contributor to the Academic Life in Emergency Medicine blog. Many FOAM enthusiasts will start a blog and use links posted to Twitter as "a means of directing people to their grander thoughts." Because of the growing number of clinical experts participating on Twitter, it can also be a powerful research tool, she said.

FOAM is distinct from the uses of social media for marketing or patient communication. Instead, the focus is on peer-to-peer networking of doctors.

Considered as education, FOAM mirrors what has been going on in other sectors of higher education with open educational resources (OERs) such as free digital textbooks and massive open online courses (MOOCs). At the undergraduate level, OER textbooks and other course materials are often promoted as a tool for lowering the cost of education, but also as a way of keeping instructional videos up to date by making them modular and digital. Although healthcare has some open textbook type projects of its own -- such as WikEM for emergency medicine -- most of the open education momentum is taking place outside of medical school.

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Kym Elaine Guy's curator insight, September 25, 2013 12:59 PM

Did you know there are serious medical conversations going on every day on Twitter?  I've always enjoyed blogging and used it as a medium to share useful or generic education but it looks like with everything else in 2013  the tides are turning.

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Fewer than half of healthcare organizations have clinical and business intelligence tools, and most who do are still learning how to use them, says HIMSS Analytics survey.

Fewer than half of healthcare organizations have clinical and business intelligence tools, and most who do are still learning how to use them, says HIMSS Analytics survey. | Analytics & Social media impact on Healthcare | Scoop.it
Despite all the hoopla about clinical and business intelligence (C&BI) applications, the use of these tools by hospitals and healthcare systems is still in an early phase, a new report indicates. Only 46% of the 529 respondents to a HIMSS Analytics surveysaid they were using C&BI, and the majority of those indicated they were still learning how to use these analytic tools.

Also revealing was the fact that more than half of C&BI users said they were using the analytic modules imbedded in their electronic health record/hospital information system (EHR/HIS). In contrast, less than a quarter of the C&BI users had purchased "best of breed" solutions, which tend to be more robust than those in EHR/HIS products.

 

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4 deft ways hospitals use social media | Healthcare IT News

4 deft ways hospitals use social media | Healthcare IT News | Analytics & Social media impact on Healthcare | Scoop.it

The Mayo Clinic ranks No. 1 for Twitter, with more than half a million followers. Cleveland Clinic is third on YouTube, with nearly 3 million views. And the University of Texas M.D. Anderson Cancer Center, in Houston, Texas, ranks 30th on Flickr, with 115 Flickr photos.

Where do the paths of these three organizations cross?  Well, they’re ranked 1st, 2nd, and 3rd, respectively, in a recent Top 50 Most Social Media Friendly Hospitals for 2013 listing developed by a group called MHADegree.org.

Founded in 2007, MHADegree.org is funded by a range of colleges and universities with the goal of providing free information to students and healthcare professionals who want to get a master’s degree in health administration.

And these days, of course, a job in administration in almost any sector will invariably involve making some kind of information available to the public. And, one way or another, that means plugging into social media.

According to Bethanny Parker, editor of MHADegree.org and the social media list’s author, there’s no shortage of reasons why healthcare organizations should have a solid, ever-evolving social media strategy in place.

 

Awareness - According to Parker, one of the most important uses of social media is as a multi-faceted means of getting new, and perhaps critical, healthcare information out to the public.  “Perhaps a new test has been developed that can catch a certain cancer earlier,” she said.  The viral nature, so to speak, of social media can be a very effective means of disseminating information quickly, particularly when that information comes from a highly regarded medical source and can be of immediate use to patients.Connecting with customers -  Any business needs to maintain its reputation, and hospitals and other providers are no different. A recent study published by the Journal of Medical Internet Research found that “approximately 60 percent of Internet users report using the Internet to look for health information.” Put those two facts together and it becomes clear that hospitals that want to serve the public need to meet the public where they are, which increasingly means on the Internet.“Neutral” information - For Parker, one of the subtly valuable uses of social media involves “the way it can provide a way to connect with a healthcare provider without committing to an appointment.”  That is, it’s widely understood that some patients are reluctant, depending on the condition with which they’re struggling, to speak directly to a healthcare provider as the first step toward receiving treatment. With Facebook, for example, providers can offer information and guidance in “non-threatening” ways, with the ultimate goal of making prospective patients more comfortable when it comes to reaching out directly.Flash mobs - OK, the actual category for this use of social media might be dubbed “Unorthodox Outreach.” And while the chances are slim that flash mobs and other “new communications” are going to become a regular option in, say, the Mayo Clinic’s communication strategy, Parker pointed to a group called Tobacco Control Nigeria that recently used a flash mob to educate passers-by about the dangers of smoking. The point is, as everyone knows, social media options keep evolving, so you really never know how it might come in handy.

With that shifting landscape in mind, Parker said she’s seeing an uptick in the use of Pinterest by healthcare organizations and a drop in the use of Flickr.  Instagram, too, is growing.  So even as the terms “Like” and “Tweet” have become widely understood as part of the communications lexicon, it’s probably safe to assume that it won’t be long before new references emerge as new media evolve.

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Jérôme Buisson's curator insight, September 4, 2013 8:51 AM

If hospitals rank high on social media, why can't pharma do the same?

Franklin Delano Williams's curator insight, September 16, 2013 10:23 PM

....some good ideas for getting the findings from the Frontier Medicine Better Health Partnership's Innovation Challenge out the the medical world and general public.

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Successful long term care should be person-centred and institutional boundaries must be redrawn or erased

Successful long term care should be person-centred and institutional boundaries must be redrawn or erased | Analytics & Social media impact on Healthcare | Scoop.it

For much of the 20th century, the biomedical model of care constructed around hospitals served us well. It was ideal for a time when a paternalistic approach to disease was deemed as the right way to go. But in a world of Web 2.0, with an ageing demographic and where multiple conditions are becoming the norm, it is no longer appropriate to suggest that "doctor knows best".

In the report, A promise to learn – a commitment to act, Don Berwick, the legendary patient safety guru, set out four guiding principles. One of these is to engage, empower and hear patients and carers throughout the entire system and at all times.

The point made was that engaging and hearing patients will lead us to understand how current experience suggests poor co-ordination between healthcare and community support. It also hints that poor engagement results in individuals not admitting when and with what they need help – and this cannot be good for patients or the professionals trying to help them.

Yet, the solution must lie in the system and not within the walls of an institution. Integration is a term much bandied about in healthcare and could become a weasel word in the healthcare lexicon if the many pilots we have at present are not effectively implemented; if they don't begin to provide more care at home and in the community, and if they don't ensure a more seamless experience.

It's against this background that KPMG recently conducted a survey of 1000 patients, asking them about the future of the NHS. It revealed – surprisingly – that only 36% were comfortable with the idea of using technology, with 54% also arguing that taxes should rise to pay for healthcare.

So the public are ready to have a much more mature and sophisticated dialogue – but we need to support their informed choice so that the hospital and the A&E department are not seen as the inevitable solution.

While news of an additional £500m to A&E departments may defer some of the pain of demand over the winter, the sticking plaster approach is not going to yield the sustainable change required. The best integrated systems in the world have sustained leadership, an effective clinical leadership and engagement and have usually invested considerable sums in their IT infrastructure.

Two examples speak volumes. To begin with, take Virginia Mason in Seattle – where Gary Kaplan, the medically qualified CEO, has been in post for 15 years and where Toyota and Six Sigma approaches to clinical practice are a way of life. He says that "you don't have to be a champion, but you can't be a burier". There's also Kaiser Permanente, where almost 50% of the 9 million population can access their healthcare records online.

Yet despite these great examples of care being made fit for the 21st century, KPMG's latest report, called An Uncertain Age: re-imagining long-term care in the 21st century, suggests that few, if any societies, are facing up to the long-term care problem. Commissioned by the Lien Foundation – a Singapore philanthropic foundation – it makes the point that an ageing population coupled with changing demographics, where people move away from their home base, means the threat of less family support is becoming a reality. Add to this the growing cost of healthcare and the dwindling available funding and we need to redraw the way we provide care and the way we engage the population in that change.

As our report suggests, person-centred care is a must, institutional boundaries must be redrawn or erased and technology must play a part. Perhaps this is Berwick's most important guiding principle as, without that engagement, empowerment and listening we will not be able to make the seismic shift required to a holistic view of the system. In other words, we will not be able to move beyond the walls of the hospital, where patients and the public expect increased autonomy in return for greater responsibility for their health.

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Julie Hankin's curator insight, September 7, 2013 4:46 AM

A challenge to start to think outside our organisations and in systems.  This is how our patients experience us

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What about healthcare must change for analytics to flourish?

What about healthcare must change for analytics to flourish? | Analytics & Social media impact on Healthcare | Scoop.it
The amount of information about a patient continues to increase and its sources are likewise increasing, which in part is driving a growing interest in the use of data analytics in the field of healthcare. Looking at the successful application of these tools and services in other industries such as financial and retail services, many organizations in and around healthcare are coming two terms with what big data and analytics can do for providers and patients.
“Think of the power of that in healthcare where you’re talking about somebody’s health, which is by definition a very data-intensive endeavor,” says Tom Olenzak, Director of Innovation at Independence Blue Cross. “You think about the amount of data that is generated around your health — not just doctor’s visits — claim history, labs, scripts (both filled and unfilled), diagnoses, and even things that are not thought of as traditional healthcare.”This optimism, however, has yet to be transformed into widespread use of healthcare analytics. The chief stumbling block in the way of the healthcare industry making use of this technology remains getting the necessary information together in a central place as well as in the right format to apply statistical analysis.“In healthcare, there has never been anybody who has pulled the pieces together,” explains Olenzak. “Historically, there hasn’t even been anybody who could aggregate claims and clinical data in one place and labs from somewhere else and scripts in still a third place.”Those technological barriers aren’t the only hurdles that need to be crossed in healthcare. Even with the technology in hand, healthcare organizations and providers face the challenge of fitting it into clinical workflows.“You look at your average provider — whether it’s a primary care physician or a specialist — and with everything going on there, there so overworked that any new wrinkled that you introduce into the workflow is going to be an issue,” adds Olenzak.The challenge of clinical adoption of healthcare analytics necessarily leads to a conversation about payment and liability, the two needing to be aligned for healthcare organizations and provider to even consider making use of these tools and services.“We wanted to feed blood glucose levels on a real-time basis to a physician,” continues Olenzak, “but they’re not set up to receive that data, they’re not set up to react real time because that’s not how a physician’s practice works, and there are legal issues that once they get that data if they haven’t appropriately acted on them they could be liable. And they’re not paid to work with data in real time.”So what needs to change in healthcare for analytics to flourish? According to Olenzak, it’s the culture that needs to change. The industry needs to think differently about how its partners, from providers to payers, work together to improve patient outcomes and the cost of high-quality care.“Unfortunately, the way our health system is designed, it has sometimes pitted payers against health care providers. We have to be able to break outside of that,” he observes. “We have to be able to break outside of that and work together with physicians and hospitals to create an environment where we can build a reimbursement model that focuses on the good of the patient.”The task for health plans like his own, argues Olenzak, is to move from “a transaction-processing-based company to a manager of care” and in doing so making itself a valuable and more importantly trusted player in aggregating and sharing actionable data among healthcare organizations and providers.“We are the one group in this very fragmented healthcare system that kind of sits at the hub, not just because of our place in the center of the financial transaction but because we are in a position to become not just the aggregator or intermediaries for the transaction but also for the data,” he argues. “It has to come from so many different places in so many different forms, and somebody needs to be that single source of truth, that quarterback, that can translate and get it to where it needs to be.”
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Healthcare analytics reduces hypertension for KPNC patients

Healthcare analytics reduces hypertension for KPNC patients | Analytics & Social media impact on Healthcare | Scoop.it
Heart disease is the leading cause of death in the United States, so why not leverage health information technology in the form of healthcare analytics to intervene more effectively in the treatment of patients with this condition? That’s exactly what a Kaiser Permanente North California (KPNC) program determined to figure out and has now shared in research published in the Journal of American Medical Association (JAMA).

 

In the study, “Improved blood pressure control associated with a large-scale hypertension program,” Jaffe et al. highlight the benefits of combining a “comprehensive hypertension registry, development and sharing of performance metrics, evidence-based guidelines, medical assistant visits for blood pressure measurement, and single-pill combination pharmacotherapy” in the treatment of adults diagnosed with hypertension.“Although feedback at the individual clinician level has long been used to promote change, we focused on clinic-level feedback to facilitate operational and system-level change,” the authors write. “Health system–wide adoption, evaluation, and distribution of an evidence-based practice guideline that had timely incorporation of new evidence facilitated the ability to introduce new treatment options and to re-emphasize existing evidence-based recommendations.”The results for the program spanning from 2001 to 2009 and including as many as 652,763 patients provide strong support for positively impacting population health through health data aggregation and the sharing of best practices among providers and across clinical settings.Over that period of time, the integrated healthcare delivery system in Northern California increased its National Committee for Quality Assurance (NCQA) Healthcare Effectiveness Data and Information Set (HEDIS) measurement for hypertension control from 43.6 percent to 80.4 percent. The NCQA HEDIS national average paled in comparison, increasing from 55.4 percent to 64.1 percent, during the same timeframe.According to Jaffe et al., the program’s success hinged on clinicians providers making use of a 4-step hypertension control algorithm made available to them through a variety of sources:

The guideline was updated every 2 years based on emerging randomized trial evidence and national guidelines. Clinicians were encouraged to follow the algorithm unless clinical discretion required otherwise. Dissemination of guidelines occurred through distribution of printed documents, e-mail, clinical tools (e.g., pocket cards), videoconferences, lectures, partnering with pharmacy managers, and use of the electronic medical record to optimize selection of medication.

With the healthcare industry shifting from a pay-for-service to a pay-for-performance model of reimbursement, this transformation places a significant emphasis on proactive rather than reactive care. As has become clear in innovative healthcare organizations, rising to this new challenge requires a new approach to care delivery and the health IT systems (e.g., healthcare analytics) to predict outcomes and intervene in the form most appropriate to the needs of individual patients.If organizations such as KPNC were capable of achieving such positive results prior to the Health Information Technology for Economic and Clinical Health Act and meaningful use, today and tomorrow’s hospitals and practices should have tools available to them to make this sharing of evidence-based practices meaningfully useful to themselves and their patients.

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Providers Look for Next Level of Healthcare Analytics

 OREM, UT, January 07, 2014 /24-7PressRelease/ -- When it comes to healthcare analytics, providers simply cannot have it all. A newly released KLAS report, Healthcare Analytics: Making Sense of the Puzzle Pieces , indicates that most vendors are struggling to deliver ease of use and robust functionality.
With movement toward value-based care, newer healthcare models, and ACOs, providers' analytics needs and expectations are by necessity continuing to rise. As providers look to go to the next level with healthcare analytics, there is still a notable discrepancy between what most vendors deliver and what providers require.
"The pressure is mounting," said Joe Van De Graaff, report author. "Providers see analytics as a strategic compass for the changing healthcare world ahead, and their need for better results and better ways to understand outcomes through data analytics and BI is critical."
The energy around healthcare analytics continues to surge, and concurrently, the opportunity for revolutionary solutions and outcomes is more urgent than ever before. This report helps providers understand the many different pieces of the analytics puzzle and highlights the successes and struggles of vendor products.
KLAS spoke to over 400 healthcare providers to capture their experiences with their BI vendor products. Cross-industry vendors discussed in this study include Deloitte (Recombinant), Dimensional Insight, Harris (Carefx), IBM, Infor, Information Builders, Kofax (Altosoft), Kronos, Microsoft, MicroStrategy, Oracle, QlikTech, SAP, SAS, Tableau and Xerox (Midas+). Healthcare-specific vendors discussed in this study include Allscripts, Advisory Board, athenahealth, Caradigm, Cerner, Epic, Explorys, Humedica, Health Catalyst, Health Care DataWorks, McKesson, Premier, UHC and Siemens. For more information about this study, check out the full report, Healthcare Analytics: Making Sense of the Puzzle Pieces. Visit www.KLASresearch.com/KLASreports.
About KLAS KLAS is a research firm on a global mission to improve healthcare delivery by enabling providers to be heard and to be counted. Working with thousands of healthcare executives and clinicians, KLAS gathers data on software, services, medical equipment and infrastructure systems to deliver timely reports, trends and statistical overviews. The research directly represents the provider voice and acts as a catalyst for improving vendor performance. KLAS was founded in 1996, and KLAS' staff and advisory board members average 25 years of healthcare information technology experience. For more information, go to www.KLASresearch.com, email marketing@KLASresearch.com or call 1-800-920-4109 to speak with a KLAS representative. Follow KLAS on Twitter https://twitter.com/klasresearch

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9 Ways to Build Confidence in Big Data

9 Ways to Build Confidence in Big Data | Analytics & Social media impact on Healthcare | Scoop.it
9 Ways to Build Confidence in Big Data
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Paulo Machado's curator insight, January 9, 2014 10:52 AM

and a clear vision for the role of data in your organization...

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North Carolina hospital gains medical insights from big data analytics

North Carolina hospital gains medical insights from big data analytics | Analytics & Social media impact on Healthcare | Scoop.it
Clinicians can quickly access and analyze critical patient information

UNC Health Care (UNCHC) is using big data analytics to improve patient care and manage information better. Eighty percent of the institution’s data is unstructured, including such medical information as physician notes, registration forms, discharge summaries, phone calls and more.

To analyze that medical data more effectively, UNC Health Care has chosen IBM’s Smarter Care solution, with the ultimate goal of reducing readmissions, decreasing mortality rates and improving the quality of life for patients. With the solution, UNCHC clinicians can quickly access and analyze critical patient information using natural language processing. The institution also can identify high-risk patients, understand in context what is causing them to be hospitalized and take preventative steps, IBM reports.

Dr. Carlton Moore, associate professor of medicine at UNCHC, says, “IBM Content Analytics allows us to quickly transform raw information into healthcare insights. It can reveal trends, patterns and deviations while predicting the probability of outcomes so that we can make decisions in minutes versus weeks or months.”

Previously, UNCHC used IBM Content Analytics to mine clinical data to improve the accuracy of its 2012 Physician Quality Reporting System (PQRS) measures, achieving quality improvements in the areas of mammogram, cancer and pneumonia screening, according to IBM.

UNC Health Care is focusing the new IBM solution on three additional areas:

timely follow-up of abnormal cancer screening results,reducing costly 30-day readmissions (preventable readmissions impact one in five U.S. patients), andengaging more patents (transforming clinical data into a simpler format so that patients can under their health information better).
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More proof that health care loves big data

More proof that health care loves big data | Analytics & Social media impact on Healthcare | Scoop.it
Two new research partnerships whose participants range from pharmaceutical companies to IT vendors are taking aim at improving disease treatment via data analysis.

It’s no secret that medical research and health care have already benefited pretty significantly from the technologies and analytic techniques that comprise big data, and two new partnerships underscore the promise.

 

One is a five-year research partnership between the Berg pharmaceutical company and the Icahn School of Medicine at Mount Sinai, which is focused on using data to derive new therapies for cancer, as well as central nervous system and endocrine disorders. The other is a $2 million grant from the National Institutes of Health to IBM, Sutter Health and Geisinger Health System to study how electronic health records can help predict heart failure.

The Berg-Mount Sinai partnership is particularly interesting because of its scope. It’s focused on analyzing so-called “multi-omic” biology, which means the study of various systems and fields, including genomics, proteomics and metabolomics. According to Icahn professor Eric Schadt, in the press release announcing the partnership, “Working with Berg, we plan to analyze big data and create predictive models to discern similarities and differences in disease patterns, identify the most effective treatment and diagnostics, and ultimately, provide better care for our patients.”

The IBM-Sutter-Geisinger partnership is actually an extension of earlier work into this same area — identifying symptoms that often result in heart failure years before any serious issues might occur. According to that press release, “The NIH funding allows the team to look deeper into the progression of factors that are predictors of heart failure so clinicians can implement timely care-management plans to improve health outcomes. They will begin testing predictive methods for heart failure in clinical practice over the next several years.”

Seton Healthcare (an IBM customer, actually) has already reaped the benefits of this exact type of analysis. I wrote about it in 2012:

“Following a CEO mandate to find better ways to detect congestive heart failure early in order to save the exorbitant costs of treatment as the disease progresses, [Seton Healthcare VP of Analytics Ryan] Leslie’s team analyzed a stockpile of data ranging from billing records to patient charts. It found that a distended jugular vein — something that can be spotted during any routine physical exam — is a particularly high risk factor.”

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It’s likely we’re just seeing the tip of the iceberg of what’s possible with big data and health care, though. Obamacare places a heavy emphasis on electronic health records and better data collection, generally, and patients are now able totrack an increasing number of potentially valuable data points using smartphones and wearable devices. Health care is huge business tied to lots of IT spending, so if there’s data that can help health care organizations do their jobs better, there will be plenty of researchers and companies willing to help analyze it.

 
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The Digital Hospital: A Way of Thinking About the Future

The digital hospital is as much a way of thinking about the future of healthcare as it is about technology. It’s not about the EMR or about the data. There is no single “solution” for digital hospitals to implement, no magic data integration that suddenly creates a digital hospital.

Digital hospital is not a technology story.  It’s about the customer value mindset and those healthcare organizations that are offering their traditional and very new global customers a more compelling patient, client, or member experience than a visit to the local doctor’s office or Emergency department.

 Here’s an example of the digital hospital in action.  At the Ottawa Hospital in Canada, care teams were struggling with very high occupancy and highly variable manual processes. As a result, patients experienced delays in their care, clinicians were frustrated, and the hospital was not satisfied with the overall patient experience they could offer.  The hospital equipped every physician with a tablet and  implemented IBM’s care process orchestration engine to  model the readiness for discharge process from admission to post discharge. The system dynamically creates communication and knowledge links between a patient and the circle of care providers around them and allows automatic texting and videoconferencing between members of the team to help the physicians, nurses and therapists review what the patient can and can’t do and make different therapy decisions or revise the date of discharge.
Combined with business intelligence tools, the clinicians are doing extensive analytics – to see what days of week are heavy, to track how many consults each therapist has and to balance their workflow – and are moving into rapid cycle testing of new hypotheses and improvements of the process – eg does early referral of social work help to hit estimated discharge dates.  Dale Potter, senior vice president and chief information officer, of The Ottawa Hospital says, “What we are doing is putting process orchestration and process models in place, so that you can literally see the characteristics of the hospital system. You can see, for example, that the flow in the emergency department is too fast to be taken up in the admitting units, and you can then influence that.”
In this version of the digital hospital combining mobile communication tools, care process modeling and analytics, patients are given accurate information about their care process and their predicted length of stay,  the hospital is better managed and clinicians are more satisfied “Personally, I am going to spend more time focusing on the right things and less time focusing on the mechanics, the bureaucracy, the paperwork and other things,” says Glen Geiger, chief medical information officer at The Ottawa Hospital. “I am not spending time chasing information, I am spending time dealing directly with the patients.”
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IBM and Boston Children's Hospital Team to Improve Care of Critically Ill Children Across the Globe

IBM and Boston Children's Hospital Team to Improve Care of Critically Ill Children Across the Globe | Analytics & Social media impact on Healthcare | Scoop.it

Armonk, N.Y. and Boston, Mass. -- IBM (NYSE: IBM) and Boston Children's Hospital today announced the world's first Cloud-based global education technology platform to transform how pediatric medicine is taught and practiced around the world. The initiative aims to improve the exchange of medical knowledge on the care of critically ill children no matter where they live. 

Every year, nearly 7 million children under age 5 die from illnesses like pneumonia, diarrhea and malaria despite the availability of life-saving medical solutions. The new Cloud-based technology platform - called OPENPediatrics - equips doctors and nurses with the knowledge and skills they need to save children's lives during intensive care situations. As the platform grows, content will extend beyond critical care. Developed in IBM Labs in Cambridge, Mass., OPENPediatrics trains medical professionals using a unique on-demand, interactive, digital and social learning experience, equipping them to perform life-saving procedures and treatments for children who would not otherwise have access to intensive care. The content is supplied by experts at Boston Children's Hospital and includes seminars from international expert clinicians. 

The benefit of Cloud, particularly in under-developed nations, is that it overcomes the need to build a global technology infrastructure in favor of a highly efficient, cost-effective model. IBM has invested more than $4 billion in software acquisitions and organic development to build out its global cloud portfolio, which is based on open standards. By putting OPENPediatrics in the cloud, clinicians are guaranteed to have access to the latest medical information, training modules, best practices, and social interactions between users. 

"Nothing breaks down walls and brings people together like caring for a critically ill child," said Jeffrey Burns, MD, MPH, chief of Critical Care Medicine at Boston Children's Hospital. "With IBM's technology and services arsenal and our critical care expertise, we partnered to bring our vision of stronger pediatric care to countries across the globe. In doing so, we're extending the reach of medical education to help save children's lives and laying the groundwork for the Digital Hospital of the future." 

IBM will supply the technology infrastructure, including its social networking, cloud, data analytics, video, and simulation technologies, and combine it with the world-class knowledge and medical expertise of Boston Children's Hospital to bring pediatric care to global communities. IBM interactive, the company's digital agency, developed the technology interface. 

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The Big Promise of Predictive Analytics in Healthcare

The Big Promise of Predictive Analytics in Healthcare | Analytics & Social media impact on Healthcare | Scoop.it

At Duke University’s Fifth Annual Technology and Healthcare Conference, Eric Siegel, founder of Predictive Analytics World and executive editor of the Predictive Analytics Times called new predictive analytical tools “inevitable” disruptions to the way physicians make treatment decisions and patients receive care.


Whether you’re at a casino in Las Vegas, or a patient on the active arm of a clinical trial, no knowledge is more coveted than what’s going to happen next.

 

Of course, no one can know with certainty what the future holds – there are far too many variables, known and unknown – but that’s not really the goal of predictive analytics anyway. For his purposes, Eric Siegel defined predictive analytics for conference attendees as “technology that learns from experience – i.e. data – to predict the outcome or behavior of individuals.” But even that definition is a bit deceptive; technology itself is subject to the same chaotic undercurrent that defines the lives of human beings and their machines.

The famous baseball statistician Bill James, who brought scientific analysis and big data to bear on the sport back in the 1970s, began his project by obsessively studying box scores in an attempt to understand why some teams win and others lose. Despite James’s undying interest in hard numbers and percentages as tools for understanding and predicting the game, he always stressed the anomalous factors, and the need to wed traditional player statistics with the more ethereal characteristics the players embody. Things like luck, the effects of playing at home or away, and clutch performances in the bottom of the 9th, with two outs and the bases loaded, turn out to be pretty unpredictable.

This isn’t an attempt to debunk predictive analysis as a marketing tool and a potential route to better health outcomes. The ROIs are written on the walls. But the dramatic increase in the number of people wearing biometric sensors, paired with all of the “listening” or spying campaigns being conducted on social media platforms, to name just two small streams in the flood of new and accessible data, have made certain commercial enterprises increasingly confident about the degree to which they can predict an individual’s behavior.

That capability, always described at conferences as “the holy grail” or, in Siegel’s parlance, “the golden egg,” is starting to make the question of what technology can accurately predict about people less interesting than what it still can’t.

At any rate, Siegel got around to admitting that predictive analysis is “not necessarily [about] predicting individual outcomes,” but is more about segmenting risk levels. The easiest and most basic form of predictive analysis begins with a decision tree. But even before constructing the decision tree, the crucial first step is to prepare the data by organizing it so that two time frames are juxtaposed: historic data on the one hand, and present day data, which companies would like to be able to predict. Siegel says the relationship between past data and present data is analogous to the relationship between present data and future data. Once the data is prepped, the decision tree can take root.

In an example from Chase Bank’s mortgage business, Siegel described the top of the decision tree as an interest rate of <7.94%. By asking a series of yes or no questions, involving income level, total mortgage amount, lone-to-value ratio, etc. etc., Chase was able to very accurately predict an individual’s risk of loan defection.

In healthcare, the idea is that a similar decision tree, based on extensive patient data and clinical drug information might help bring personalized medicine a lot closer to home for many patients. And it might also upend traditional treatment pathways and protocols, since no two people are exactly alike. Siegel said predictive analytics at the patient bedside is “inevitable,” although it could start happening in five years or 20. Not because the technology and methodology isn’t ready for prime time, and not because predictive analysis is too complicated, but because “cultural change is hard…we have to learn to trust the machine.”

The three most promising applications for predictive analytics in the healthcare space, according to Siegel, are in the areas of clinical (diagnosis, outcome prediction, and treatment decision-making); marketing; and insurance coverage. In his presentation, Siegel cited examples of pharma companies who have dabbled in clinical predictive analysis – GSK has experimented with predicting clinical trial enrollment, Pfizer with predicting health outcomes – but Siegel himself hasn’t fully waded into the healthcare industry as of yet. That will change next year; Siegel announced an inaugural healthcare-focused conference that his organization, Predictive Analytics World, will host in Boston next October.

Prediction has come a long way since Nostradamus. Today’s predictive analysis isn’t concerned with causality, for two major reasons. One, it’s often impossible to determine; and two, it’s largely irrelevant. What matters are the correlations, which readily emerge once the datasets grow large enough. The owners of those datasets, or the people and machines that have the best access to them, are in a position of power that will only increase. Toward the beginning of his keynote, Siegel told attendees “your experience today depends on how organizations and companies treat you.” The most unsettling thing about that statement is that it’s probably true.

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How healthcare analytics can ensure delivery of high quality patient care

How healthcare analytics can ensure delivery of high quality patient care | Analytics & Social media impact on Healthcare | Scoop.it

Healthcare vertical can leverage big data analytics to achieve better prognosis, effectively do remote patient monitoring and dig into combined clinical and genomics research data to suggest personalized treatments for patients

 

In a bid to deliver high quality healthcare care and improve patient satisfaction, public and private sector hospitals are today looking at streamlining workflow processes, integrating healthcare related data and securing information exchange. Like many developing nations, India too is exploring all ways and means in providing good, cost effective healthcare to its citizens. In doing so, healthcare organizations are increasingly realizing that IT solutions can actually help them meet this challenge by optimising resource allocation and plugging inefficiencies that cause delay in treatment. 

One of the technology solutions that can be leverage quite effectively by healthcare organizations is big data analytics which can go a long way in reducing the cost of healthcare care and improving patient outcomes which in turn could pave the way for a new age in healthcare. Let us look at some of the ways in which healthcare vertical could leverage big data and analytics for providing high quality of patient care both for inpatients and outpatients. 

Healthcare analytics

The healthcare industry is fast moving away from a paper based systems to Electronic Medical Record (EMR) systems. So far, much of this data was locked in a system designed to treat patients on an episodic fashion, and may not have contained the full longitudinal health record of the patient. But with the maturing of some solutions based on big data architectures, the ability to unlock and analyze this information is now possible. The Chief Medical Information Officer or Chief Research Officer  at many healthcare organizations are using these tools to derive scientific evidence that will help them validate the treatment being given to a patient as the most effective and efficient care at the best cost.

Remote patient monitoring 

In many countries, technology is enabling healthcare providers to closely monitor patients in their home on a real time basis. The care givers are monitoring home devices such as glucometers, weight scales, pedometers and others to understand how the patient is faring day to day. For example, if a patient is suffering from a chronic disease such as diabetes or congestive heart failure, the ability to monitor him for weight gain, blood sugar levels and exercise attempts will allow the care team to proactively contact the patient and provide help or recommend his report to an emergency room for immediate treatment if need be.

Another example where real time in-home devices can be used is, for independent living. Just because many countries are experiencing an ageing population, does not mean that the people will want to give up the ability to live alone. In such a situation having the ability to covertly monitor the person, with their permission, provides a level of safety to determine if someone has fallen, not gotten out of bed, or has been missing meals. 

The facilities to extend the healthcare system into the home of a person allows for a much better quality of life for the patient as well as to reduce operational cost for hospitals. However the volume and velocity of the data being collected, as well as the real time nature of the analysis and action require health care organisations to put in place a big data solution. 

Tapping into clinical and genomics research data for personalized treatments

Advances in medical technology have changed the way doctors monitor and treat patients. With the cost of DNA sequencing becoming affordable in many parts of the world, the emergence of personalized medicine is becoming a reality. There are many drug therapies that have been found to be effective for a certain group of patients with specific gene expressions. The ability to determine if a patient has the genetic gene expression before treatment begins allows for a better prognosis. 

Many research institutes, academic medical centres, drug makers and contract research organization are looking for technology solutions that will help them combine clinical and genomics research data in order to determine the effectiveness of personalized treatments.  In order to achieve this, many hospitals will be looking at adopting solutions such as big data analytics, over the next few years.

No-where is the transformative power of big data analytics more meaningful than in the health care sector. The need is to identify the potential that big data analytics holds in itself to transform the way healthcare vertical has been traditionally responding to the patients needs, so far.

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Mobile Diagnostics by Smartphone and Image Analysis for Detecting Antibiotic Resistance

Mobile Diagnostics by Smartphone and Image Analysis for Detecting Antibiotic Resistance | Analytics & Social media impact on Healthcare | Scoop.it
Fraunhofer FIT demonstrates a mobile wireless system that monitors the health of elderly people in their own homes, using miniature sensors, and also a novel optical system for detecting antibiotic resistance, which can determine in just two...
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Cancer and Clinical Trials: The Role of Big Data In Personalizing the Health Experience - Strata

Cancer and Clinical Trials: The Role of Big Data In Personalizing the Health Experience - Strata | Analytics & Social media impact on Healthcare | Scoop.it

Despite considerable progress in prevention and treatment, cancer remains the second leading cause of death in the United States. Even with the $50 billion pharmaceutical companies spend on research and development every year, any given cancer drug is ineffective in 75% of the patients receiving it. Typically, oncologists start patients on the cheapest likely chemotherapy (or the one their formulary suggests first) and in the 75% likelihood of non-response, iterate with increasingly expensive drugs until they find one that works, or until the patient dies. This process is inefficient and expensive, and subjects patients to unnecessary side effects, as well as causing them to lose precious time in their fight against a progressive disease. The vision is to enable oncologists to prescribe the right chemical the first time–one that will kill the target cancer cells with the least collateral damage to the patient.

How data can improve cancer treatment

Big data is enabling a new understanding of the molecular biology of cancer. The focus has changed over the last 20 years from the location of the tumor in the body (e.g., breast, colon or blood), to the effect of the individual’s genetics, especially the genetics of that individual’s cancer cells, on her response to treatment and sensitivity to side effects. For example, researchers have to date identified four distinct cell genotypes of breast cancer; identifying the cancer genotype allows the oncologist to prescribe the most effective available drug first.

Herceptin, the first drug developed to target a particular cancer genotype (HER2), rapidly demonstrated both the promise and the limitations of this approach. (Among the limitations, HER2 is only one of four known and many unknown breast cancer genotypes, and treatment selects for populations of resistant cancer cells, so the cancer can return in a more virulent form.)

How data can improve clinical trials

As with treatment, progress in developing better cancer drugs has been hindered by a lack of genomic and metabolic understanding. The historical approach to cancer drug clinical trials is to recruit uncharacterized (without any genomic, metabolic or other differentiators that may affect response to the candidate treatment) subjects to test one-size-fits-all drugs. Given what we know now about cancer genotypes and individual response to drugs, it’s amazing any drugs were able to show statistically significant efficacy and reach the market.

Using personal medical and population genomics data, clinicians now have tools to design more targeted clinical trials by matching cancer cell types and individual metabolic response to the drug candidate, recruiting subjects who will be more likely to respond and excluding those likely to have treatment-limiting side effects.

Using new data to address unmet medical needs

In addition to cancer, there are many diseases with wide individual variability and a dearth of effective treatments: e.g., Alzheimer’s, depression, diabetes, asthma, and arthritis. A flood of new data streams in health care (from digitized medical records, genomics, pharmaceutical data, and data from trackers and sensors) may enable clinicians to make better diagnoses and prognoses that can give patients better prevention and treatment choices. Furthermore, aggregated health data can enable researchers to determine which patients are good candidates for particular clinical trials or treatment protocols. Using sensors, at-home monitors, and smartphone device trackers, clinicians can capture clinical data in real time to monitor patients’ progress outside of the hospital between visits. This new approach is becoming possible through a combination of data sources and improved data management and analytics to move toward more effective treatments–and ultimately, personalized medicine.

New ways to analyze medical data: GNS, Ayasdi, Explorys

Companies are applying old and new methods to analyze the multidimensional data sets collected from cancer and clinical trial research. GNS Healthcare, Ayasdi, and Explorys are a few of these companies, using topology, causal models, and multiple processors, respectively, to analyze and visualize the data.

GNS Healthcare uses machine learning and statistics to create software models that let users predict the outcome of “what if” scenarios. Their next-generation REFS machine-learning engine (on a cloud platform) extracts these models directly from multiple sources of data to determine comparative effectiveness and create simulations across an entire patient population as well as on an individual level. This can help determine which treatment or line of action will be best for individuals and for the health system as a whole. For example, GNS recently announced that they are using EMR and genomic data to create a computer model that can predict which pregnant women are at risk of preterm labor.Ayasdi uses a more esoteric topological analysis, a “math of shapes”, on their Iris platform, to visualize data in a multidimensional graphic that readily shows outliers as well as high and low-response groups in the data, even without pre-specifying the characteristics of those clusters. The outliers can represent unknown biomarkers, or subgroups of patients that would be well (or poorly) suited to a clinical trial of a particular drug. Other clusters in the visualization could point to data sets that demand further analysis that are invisible through other analytic methods. Ayasdi has found a number of novel biomarkers, the first of which was a new subset of “triple negative” survivors that had elevated expression levels of genes involved in the immune system for breast cancer.Explorys focuses on the aggregation, storage, and analysis of multiple data sources, including all clinical, financial, and operational data related to patient care. Massive parallel processing allows Explorys to look at multiple data sets from multiple angles at the same time, processing the data in real time for real time results.Why We Care: The Future of Medicine

These trends in clinical trials and cancer research represents the dawn of a new age of personalized medicine.

For pharma researchers, designing more precise clinical trials can reduce drug development failures and costs. For clinicians, matching drug treatments to patients could mean improved response and lower costs. For patients, reduced side effects and avoiding trial-and-error dosing improves quality of life. All this has potential to save the healthcare system $300 billion dollars a year, and hundreds of thousands of lives.

For data scientists, new research into disease mechanisms relies on the aggregation of vast stores of data from many different sources. That presents a major data management and analysis challenge as medical records convert to digital ones, growing sets of genomic and pharmaceutical data become available, and mobile data flows come on stream from millions of people.

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Setting a New Trend in Rare Disease R&D: Turning Small Data into Long Data

Setting a New Trend in Rare Disease R&D: Turning Small Data into Long Data | Analytics & Social media impact on Healthcare | Scoop.it

The Vitiligo Research Foundation (VRF) is seeing the massive opportunity in collecting cheap Longitudinal Data as opposed to costly Big Data in order to expedite R&D in rare diseases, using standard healthcare and bioinformatics tools.

Conventional wisdom dictates that data analytics can make typically $1.4 billion drug development faster and cheaper. Big Data deploys sophisticated analytics to parse huge quatities of data from many disparate sources across the healthcare ecosystem to discover patterns that could be useful in problem solving.

The major problem with the current Big Data mega-trend, one that is dominating media casts and conferences nowadays, is that there is not enough data to crunch in a field of more than 7,000 rare diseases. An only slightly lesser problem is that, even if there was enough Big Data on rare diseases, it would be made up of millions of loosely related disease data snapshots, like a movie reel where each frame is technically correct but has no real relation to the surrounding frames.

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How to avoid choosing the wrong healthcare analytics tool

How to avoid choosing the wrong healthcare analytics tool | Analytics & Social media impact on Healthcare | Scoop.it

The healthcare industry is in the process of moving to a performance-based model of reimbursement after decades of pay-for-service. As part of this move, healthcare organizations and providers are searching for tools that help identify risk before it manifests itself in the form of preventable readmissions or procedures. This is where healthcare and analytics intersect as well as where many healthcare organizations have the potential to choose the wrong healthcare analytics tool.

 

“One of the biggest challenges is that everybody everywhere now is using the word analytics,” says JaeLynn Williams, Senior VP of Client Operations at 3M. “Everyone is doing big data and healthcare analytics. As an industry, it’s very hard to figure out exactly what you’re evaluating, what you’re buying, what’s real, what’s of value today, what takes incremental investment like developed resources or content experts on top of it.”According to Williams, the buzz around big data and analytics in healthcare circles is giving the impression that the marketplace has products that can deliver fully on the promise of this emerging technology:
There isn’t today this gorgeous, one-size-fits-all, uber analytics solution that you’re going to buy and it’s going to be the magic eight ball and you’re going to be able to put something in and it’s going to spit out all of your needs on the other side. But I think people are marketing and talking about things in that way. It’s just like any topic: You need to get familiar with it, understand what you’re talking about, and then be able to make wise strategic plans and decisions from there.
So what should a health system or hospital do to avoid investing in the wrong healthcare analytics tools?First things first, healthcare organizations need to convene a selection committee that includes the CIO and CMIO along with staff already familiar with using similar tools. An organization participating in meaningful use can turn to those working on clinical decision support. “If your organization has a strong decision support, that is a huge place to go because they understand the data. They understand the content; they understand the workflow. Those people should really be brought into the picture,” adds Williams.Secondly, a health system or hospital must ensure that they have a “big, large, and meaningful” data set that can be accessed efficiently for the purpose of real-time analytics. Coupled with that, privacy and security measures need to be in place to provide conditions of trust, says Williams.Lastly, healthcare organizations and providers should carefully consider any analytics vendor’s experience in healthcare, domain or content expertise. “Nobody has it across everything, but if you don’t it’s very hard to get and make something that’s applicable and gets to the problems that we’re talking about,” emphasizes Williams.In the end, the successful selection and adoption of healthcare analytics tools and platforms comes down to being able to show tangible benefits, not just the promise of returns.“We have to show the financial benefit, and we have to get to where the money is,” Williams explains. “A lot of the data collection or input today is done manually. It takes a lot of people time. Through automation using tools like natural language processing, we have the opportunity to streamline this so you’re removing the labor required for the analytics as well as the avoidable cost of care.”The use of big data and analytics in healthcare is inevitable. However, similar to other health IT tools and services, healthcare analytics needs time to mature. While certain solutions are currently demonstrating the ability to affect the healthcare cost curve (e.g., readmissions, accountable care), more robust tools need time to emerge to have farther-reaching effects.
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