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.
Big data analytics have become a common consideration in the R&D part of the pharmaceutical industry, allowing researchers to search and share remarkable amounts of data points. But Merck & Co. has turned to the process to solve a manufacturing problem and potentially save hundreds of millions of dollars in the process.
Walgreens (NYSE: WAG) (Nasdaq: WAG) is expanding its relationship with Inovalon Inc. a leading technology company to implement its patient assessment tool and technology platform to support improvements in care quality and risk score accuracy programs across more than 400 Healthcare Clinic at select Walgreens locations.
The convergence of Inovalon’s data-driven patient assessment tool Electronic Patient Assessment Solution Suite (ePASS®) and Healthcare Clinic at select Walgreens creates a unique offering within the health plan and retail clinic industry. With the implementation Inovalon’s analysis of more than 8.3 billion medical events brings analytic insights to Healthcare Clinic programs.
“By integrating data analytics we can gain even deeper insights to help improve patient care and ultimately outcomes” said Heather Helle divisional vice president Healthcare Clinic. “We continue to expand the scope of services capabilities and footprint at Healthcare Clinics. These types of innovative solutions enable our nurse practitioners and physician assistants to play an increasingly important role as part of a patient’s care team.”
Healthcare Clinic at select Walgreens improves members’ choice providing a convenient community-based access point for member assessments versus the traditional in-home model.
The combination of Inovalon’s advanced analytics and Healthcare Clinic’s nurse practitioners and physician assistants as well as its laboratory and immunization resources provides a superior solution to health plans ACOs and integrated care delivery organizations seeking to achieve goals in improving quality outcomes and risk score accuracy.
“Bringing advanced analytics to the point of care in real time is a powerful benefit for patients being seen in today’s highly complex health care environment” said Keith Dunleavy M.D. president and chief executive officer of Inovalon. “We are proud to be working with Walgreens on this industry leading initiative supporting its commitment to improve health care outcomes for Healthcare Clinic partners and patients nationwide.”
Inovalon’s ePASS system delivers a patient assessment tool with individualized predictive analytics to the point of care supporting advanced insight and efficient resolution of gaps in quality care patient assessment documentation and risk score accuracy. The risk score models of Medicare Advantage Commercial Health Insurance Exchange and state managed Medicaid are each supported within the ePASS system. Similarly the industry’s wide array of quality outcomes programs including HEDIS® CMS Stars state Medicaid programs and commercial accreditation requirements of NCQA and URAC are supported within the platform provided at Healthcare Clinic at select Walgreens locations.
As the nation's largest drugstore chain with fiscal 2013 sales of $72 billion Walgreens (www.walgreens.com) vision is to be the first choice in health and daily living for everyone in America and beyond. Each day Walgreens provides more than 6 million customers the most convenient multichannel access to consumer goods and services and trusted cost-effective pharmacy health and wellness services and advice in communities across America. Walgreens scope of pharmacy services includes retail specialty infusion medical facility and mail service along with respiratory services. These services improve health outcomes and lower costs for payers including employers managed care organizations health systems pharmacy benefit managers and the public sector. The company operates 8200 drugstores in all 50 states the District of Columbia Puerto Rico and the U.S. Virgin Islands. Take Care Health Systems is a Walgreens subsidiary that is the largest and most comprehensive manager of worksite health and wellness centers provider practices and in-store convenient care clinics with more than 750 locations throughout the country.
About Inovalon Inc.
Inovalon is a leading technology company that combines advanced data analytics with highly targeted interventions to achieve meaningful impact in clinical and quality outcomes utilization and financial performance across the healthcare landscape. Inovalon’s unique achievement of value is delivered through the effective progression of Turning Data into Insight and Insight into Action®. Large proprietary datasets advanced integration technologies sophisticated predictive analytics and deep subject matter expertise deliver a seamless end-to-end platform of technology and nationwide operations that bring the benefits of big data and large-scale analytics to the point of care. Driven by data Inovalon uniquely identifies gaps in care quality data integrity and financial performance – while also bringing to bear the unique capabilities to resolve them. Touching more than 540000 physicians 220000 clinical facilities and more than 140 million Americans this differentiating combination provides a powerful solution suite that drives high-value impact improving quality and economics for health plans ACOs hospitals physicians patients and researchers. For more information visit www.inovalon.com.
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
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).
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.
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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.
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.
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.
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.
Read complete article at source by clicking the title
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.
With apologies to Internet meme-makers everywhere, analytics experts have a message for healthcare providers trying to get their heads around business and clinical intelligence: "Big data, you're doing it wrong." So much attention and energy have been put toward "big data" in the last couple of years, for perfectly understandable reasons. For example, health systems collectively have spent billions of dollars installing EHRs in recent years. "They want to get their value," says Cora Sharma, analytics analyst for Chilmark Research, a Cambridge, Mass.-based health IT research firm. They certainly see a lot of potential in the data. A March poll from MeriTalk and EMC found that 63 percent of healthcare executives in the federal government believe that big data will improve population health management. Similar numbers show that advanced analytics would "significantly improve patient care" and make it easier to deliver preventive care in the Military Health System and Veterans Health Administration.
But so few have proper goals and strategies for their data, according to Graham Hughes, MD, chief medical officer of business analytics firm SAS, based in Cary, N.C. "They're looking to accumulate data and how to get data in," Hughes says. In his opinion, this is a faulty course of action. "It's not about the data. It's about how you're going to manage it." The focus, according to Hughes, should be on information management, including data governance, stewardship and quality. "If you are just about grabbing data, you will be on a data grab forever." Optum, the IT and analytics division of UnitedHealth Group, published a white paper in February that corroborated this belief, particularly when it comes to clinical analytics. "It may sound impressive to say that your organization has access to terabytes of patient information, but without robust technology and smart people to manipulate it, that data is simply words and numbers without context," researchers point out in the white paper.
"Raw data from claims or from an EMR database are not suitable for analysis. Turning raw data into usable information requires preparation, including normalization and validation. Only then can an organization gain trustworthy insights from the information and put it to use in maximizing patient care, reducing risk and strengthening a business's bottom line,” they add. Hughes says that organizations have been spending too much time and money on enterprise data warehouses, which he sometimes refers to as "data landfills." The repository is not as important as the location of the data, according to Hughes. "An EDW isn't where data goes to die. An EDW is a staging point for analytics," he says. "An EDW needs to be easy for clinicians to understand and interpret, and also needs to interoperate with and push data back out to other systems," Hughes continues. Too many organizations wrongly assume that data should get moved to the analytics software, he says. "Modern analytics run directly from transactional systems," so there is no need to replicate the data in every situation. "It's where the data is being moved to [that] makes it actionable," he says. Indeed, Hughes notes that analytics historically have been seen as retrospective reviews, but data stores are so great now that predictive analytics are now possible, even if much of the data remains unstructured. "Analytics is now about providing actionable insights back into workflow," in close to real-time if necessary, he says. "Sometimes this is done in too fragmented a fashion," Hughes says, a reflection of the "best-of-breed" strategy of years past. Organizations bring in pieces of analytics technology every time they see a new "shiny object," he says. "To me, this feels where we were with clinical systems in the late '80s and '90s," Hughes says. He suggests thinking about it not as "niche buying," but rather as a strategic, enterprise-wide investment. The MeriTalk-EMC study found that only a third of federal healthcare executives had invested in technology to optimize data processing, and less than 20 percent said their agency was "very prepared" to manage big data. In the private sector, according to Sharma, early adopters such as Intermountain Healthcare, Kaiser Permanente and Partners HealthCare are farther along than most, but she worries about smaller, less-tightly-integrated organizations. "There's no kind of off-the-shelf software out there for them," Sharma says. So they turn to the best-of-breed strategy.
A severe shortage of analytics pros makes navigating this landscape all the more difficult, according to Hughes. "It's also a mistake to think you can staff up on this easily," he says. Hughes suggests managing data in the cloud, through a vendor or looking for "self-service solutions" that provide expertise. "You need clinical analysts to create models, and let the system be smart enough to give good recommendations," Hughes says. Sharma views the lack of qualified data engineers as an opportunity for payers; Aetna and United are among those insurance companies that have been beefing up their analytics divisions lately. "They're basically offering ACOs in a box," Sharma says. However, she adds, "Providers are still more reluctant to work with payers, for a lot of reasons." Thus, big data in healthcare remains fraught with pitfalls.
By Todd Skrinar, principal in the Advisory Life Sciences practice and Thaddeus Wolfram, manager in the Advisory Life Sciences practice, Ernst & Young LLP
Near the end of 2013, many in the life sciences industry were looking for clear evidence that the FDA was willing to work with industry to get more needed drugs to patients. Eyes were focused on the “scorecard” of new drugs approved, which for the first eight months of 2013 reached 18."
The way in which information is used by healthcare organizations is in the midst of a revolution, shaped by emergent analytical techniques that change the value proposition. These new analytics can help drive fundamental improvements in patient health, complementing the traditional requirements for information to address cost management and resource utilization.
The ability to capture, integrate, and store healthcare “big data” sets are now tangible capabilities that are available to the healthcare sector. The connection, at an individual patient level, of electronic medical records, medications usage, lab results, demographics, care management activities, and potentially streaming data from ever-present medical and fitness devices, results in a complex data set, but delivers one that is focused on building an integrated view of the patient and their environment. This new found availability is supporting the development and proliferation of new analytic tools and processes that are designed to learn from the relevant patient collective experience and apply within the personalized context of the individual patient. Understanding the whys and hows of a patient achieving specific outcomes can be derived by combining the personal characteristics of the patient with the longitudinal engagement with multiple parts of the health system. Analytics therefore help improve not just the body of medical knowledge (i.e. exploiting real world evidence), but can also be the enabler for applying this knowledge directly to the individual and the intervention required.
These new analytics and the ability to analyze structured and unstructured data underpin emerging cognitive computing systems that learn and interact naturally. These systems are trained by using artificial intelligence and machine learning algorithms and establish a mechanism for assimilation of experience in the creation of enhanced medical knowledge. One great example is the collaboration between IBM Watson and Memorial Sloan-Kettering Cancer Center to help fight cancer with evidence-based diagnosis and treatment suggestions.
Taking advantage of this evolution in analytics not only prepares, but can active lead organizations to the value-based, outcomes-driven delivery demanded of participating healthcare organizations of tomorrow.
Many health organizations have now identified and put into practice an analytics strategy but fewer are driving analytics into the forefront across the enterprise. The IBM Institute for Business Value recently engaged with key constituents of the healthcare ecosystem around the world to understand their experience with analytics, surveying over 500 executives within the healthcare industry. To read more about this exciting study, read the paper, Analytics across the ecosystem A prescription for optimizing healthcare outcomes
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.
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.”
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.
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.”
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.
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.
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.
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.
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...