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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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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Can Healthcare Big Data Reality Live Up to Its Promise?

Can Healthcare Big Data Reality Live Up to Its Promise? | Analytics & Social media impact on Healthcare | Scoop.it

Between electronic health record (EHR) systems, imaging systems, electronic prescribing software, healthcare claims, public health reports and the burgeoning market of wellness apps and mobile health devices, the healthcare industry is full of data that's just waiting to be dissected.

This data analysis holds much promise for an industry desperately seeking ways to cut costs, improve efficiency and provide better care. There are victories to be had, to be sure, but getting data from disparate, often proprietary systems is an onerous process that, for some institutions, borders on impossible.

 

Data Is Data, No Matter the Source

Generally speaking, most healthcare organizations' data comes from clinical, financial or operational applications. On its own, each type of data has a specific use, which The Institute for Health Technology Transformation (iHT2) outlines in a report, Analytics: The Nervous System of IT-Enabled Healthcare. Clinical data improves care quality and eases population health management; financial data helps hospitals conduct cost analyses pertaining to the bottom line, and operational data examines facilities management and resource utilization.

 

 

Related: Early Adopters Poised for Big Data Advances in Clinical Medicine

Put it all together, and organizations can start to assess larger issues such as staffing needs, efficiency and care quality. That's why Laura Madsen, business intelligence (BI) evangelist and healthcare services lead at Lancet Software, sees no need to differentiate among the different types of data sources. "Data is data," she says. "At the end of the day, it's just bits and bytes...If we're good data professionals, we should be integrating clinical data and business data."

Government programs-and mandates-place added pressure on the healthcare industry while giving organizations reasons to take a good, hard look at analytics. The meaningful use program that offers financial incentives to use EHR systems, the accountable care organization (ACO) model of coordinated patient care, the concept of the patient-centered medical home and the increased emphasis on improving care quality all require a more sophisticated approach to healthcare data analytics.

Abundant Unstructured Health Data Makes Analysis Difficult

Of course, organizations can't analyze data without first collecting it. In healthcare, the iHT2 notes, several factors complicate this. As much as 80 percent of healthcare data is unstructured, whether it's in paper format or in free-form fields that need to be manually abstracted, and even the structured data-that which comes from the health information exchange (HIE) process, for example-is often inadequate for analysis. As a result, the report continues, providers end up using claims data from insurance companies to get a broad view of their own organizations.

When it comes to healthcare BI, size matters, says Madsen, who literally wrote the book on the topic. The country's largest providers, namely Intermountain Healthcare and Kaiser Permanente, have been doing it for a long time, but the gap is "huge" for smaller providers. Most of these organizations see the value of BI, Madsen continues, but they can't come up with a clear answer to the question, "What should we be doing?"

Most opt to focus on BI as it pertains to regulatory reporting needs. This makes sense, as hospitals must file upwards of 1,000 reports to government agencies annually. With such an apparent need, though, Madsen says it's often difficult for organizations to take the next step and see how the data in those reports can be used to promote operational efficiency or other institutional improvements.

Analysis: How Big Data Will Save Your Life

Luckily, the iHT2 report offers several suggestions. Assessing a patient population's health needs, for example, can help organizations develop appropriate methods of service delivery while also identifying individual care gaps and even predicting which patients are likely to become seriously ill. In addition, evaluating provider performance can help drive quality improvement programs and also pinpoint reasons for variations in care.

It's also worth noting what will not work. Under the Medicare Shared Savings Plan as well as the ACO model, the aim is to generate savings that ultimately lead to lower healthcare costs, so revenue cycle management tools won't work, according to iHT2. In addition, today's cost accounting systems are ill-equipped to measure the total cost of care, which needs to consider that an early hospital discharge saves money for one facility (the hospital) but represents a missed revenue opportunity for another (the long-term care facility). Finding the total cost of care, iHT2 says, requires a "sophisticated, episode-based accounting system for bundled payments."

Healthcare Taking Data Analytics 'Wins' Wherever It Finds Them

Not all analytics systems in healthcare need to be sophisticated. At Springhill Memorial Hospital in Mobile, Ala., a recent automated medication dispensing cabinet system update came with Pandora Clinicals, an analytics package that has helped the facility reduce narcotics diversions.

The software, from Omnicell, tracks who removes narcotics from the medicine cabinet and when. Monthly reports help hospital management pinpoint outliers who dispense more medication than others. At worst, Clinical Pharmacist Joe Adkins says, this may mean a staff member is diverting the narcotics for sale or personal use-though it can also mean that a nurse or clinician has been proactive in treating a patient's pain. The software doesn't prove association, he says, but it's the first lead and often helps staff spot discrepancies before they otherwise would.

Critically, getting data from Pandora Clinicals has little effect on overall workflow, Adkins adds. Reports are automatically emailed and use bar graphs as opposed to lengthy written records. In short, little data manipulation or math is necessary: "It's a nice way to keep an eye on what's going on without having to think about it."

For payers, meanwhile, the aim is to improve the customers experience in a way that patients don't have to think about it, says Bob Dutcher, vice president of marketing for predictive analytics firm InsightsOne.

In a 2012 pilot, which is now live, the firm worked with Independence Blue Cross (IBC) to help the Philadelphia-area insurer identify patients who were likely to experience customer satisfaction issues and provide outreach to nip those problems in the bud-sometimes three months before they'd otherwise arise, Dutcher says. (The company also helped IBC identify potential new customers as well as existing members who may benefit from services they weren't using.)

To do this, IBC looks at data from its call center, to see which patients make frequent inquiries and therefore may need some extra attention. It also looks at data from member healthcare organizations, to see which medical procedures prompt the most follow-up inquiries from patients and also to see why an individual patient needed treatment. This analysis can alert IBC that a particular patient has "a high probability of a negative outcome," which may trigger the insurer to send information about preventive care (to avoid repeat hospitalizations for the same condition) or long-term or home health services (if an upcoming procedure will have a long recovery period).

Such a proactive approach improves the overall patient experience, Dutcher says, while potentially saving healthcare providers money on unnecessary or repeat procedures. InsightOne calls this sort of analytics " predictive intelligence," he says, and it lets analytics get specific enough to identify a "pattern of one" for a single patient.

Analytics Needs Talent, Data Warehouses; Both in Short Supply

Getting insurers to spend money on advanced analytics, as stated, is easier than getting healthcare providers to invest. But there are two key reasons providers can't stay on the sidelines for long, says Cynthia Burghard, research director for accountable care IT strategies with IDC Health Insights.

One is the argument that a patient is more likely to participate in a wellness program (that back-end analytics has identified her as a good candidate for) if the recommendation comes from her physician as opposed to her health plan.

Analysis: Big Data Surge From Federal Agencies Will Drive Health ITAlso: Lack of Health IT Workers Slows Tech Progress

The other is that healthcare reform efforts of the 1990s failed largely because of a lack of data. "Not only was the available information limited to claims but it was retrospective and not in a format that was useful to physicians in understanding their current performance compared with targets," Burghard notes in a recent report, Business Strategy: Analytics Leads Accountable Care Investment Priority. "Most discussions between payers and providers resulted in arguments about the accuracy and timeliness of the data."

The emerging ACO model, introduced in healthcare reform as a way to shift the industry from a fee-for-service model to one centered instead on coordinated care, placed added emphasis on analytics and data warehousing technology. The need here is identifying patients who will benefit from a particular care program, engaging those patients in order to manage and improve their care and to incorporate such care interventions into a physician or clinician workflow, Burghard says.

Down the line, as the ACO model and coordinated care expand, organizations will increasingly see the need to examine unstructured data, sentiment analysis and other data sources-including, perhaps, predictive intelligence and the mix of clinical and business intelligence-in the context of the patient encounter and clinical decision support systems, she adds.

To the challenges presented by such advanced analytics, Burghard says healthcare providers will "need fairly sophisticated people to leverage [data] warehouses and take advantage of what [they] invested in," and those who can afford neither a data warehouse nor the staff to manage them may find themselves pressured to consolidate or join larger integrated delivery networks.

This will disrupt the industry, no doubt. But in the end, having more data on hand-and being able to use it-will also improve the industry. "The adage 'You cannot manage what you cannot measure' applies to accountable care," Burghard writes in her report. "In the 1990s, healthcare organizations lacked an understanding of the critical nature of patient compliance in the management of chronic diseases; the industry is better informed today and is investing in technology to share data among payers, physicians, and patients to improve outcomes."

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Saving $1.7 million over 17,000 patients enrolled. How analytics enable the accountable care model

Saving $1.7 million over 17,000 patients enrolled. How analytics enable the accountable care model | Analytics & Social media impact on Healthcare | Scoop.it
The Institute of Health Technology Transformation recently released a report which identified data analytics for population health management to be one

The Institute of Health Technology Transformation recently released a report which identified data analytics for population health management to be one of the critical capabilities for a successful accountable care organization (ACO). The ability to identify care gaps, categorize patients based on their health risks, and focus on prevention rather than just reacting to health issues has always been considered a key requirement in an ACO model. However, the use of the latest technology advances for large scale, big data analytics across structured and unstructured health data sets has increasingly made the difference for successful ACOs, enabling them to achieve these requirements where others have struggled.

One such organization - Dartmouth-Hitchcock - recently showed just how successful this model can be if relevant patient data is analyzed effectively. It was able to hit all 33 quality benchmarks in the first year of the Pioneer ACO Model, while saving $1.7 million over 17,000 patients enrolled in the ACO. What made them so successful at reigning in cost? It turns out they had a head start of seven years. Dartmouth researchers were looking into the factors contributing to savings under an earlier ACO model already in place before the start of the Pioneer ACO program and gained some valuable insights. One of the key conclusions was that the organization needs to be able to identify patients with multiple chronic diseases and focus their attention on care coordination, prevention and outreach activities customized specifically to that population and those diseases.

The use of technology that can look across and analyze the entirety of patient health data, spotting the equivalent of a needle in a haystack therefore becomes a crucial factor to success. Many organizations are only beginning to realize the need for such large scale data analytics to gain better insights into their patient population. CMS published results on July 16th for all 32 participating organizations for year one of the Pioneer ACO program. While all of them succeeded in meeting quality measures for the first performance year, only 13 of them were able to lower their costs against benchmark accounts. A total of 9 of the participating organizations have indicated to CMS that they won't continue to participate in the second year. 7 of them will apply to the more traditional and lower risk Medicare Shared Savings Program (MSSP), while 2 of them will be dropping out altogether. While there were other factors at play, part of the problem was that these organizations did not achieve the same level of analytics excellence as Dartmouth Hitchcock did.

The Institute of Health Technology Transformation acknowledged in its recent report there is no single roadmap to achieving analytics excellence, but it cited several critical steps for the success of health data analytics in an ACO environment, as summarized by iHealthBeat:

Identifying care gaps and providing steps to close them.Categorizing patients based on their health risks so care teams can intervene with high-risk patients who generate the majority of health costs.Changing analytic perspective from episode-based analyses to patient- and population-based analyses.Making use of emerging technology to analyze the 80% of electronic health data that is unstructured, rather than solely relying on traditional structured data analytics (e.g. on claims data).

Many prevention and intervention activities depend on early detection of patients at risk of developing serious illnesses. A good example is abdominal aortic aneurysms (AAA). They typically develop in older patients over many years. If detected and tracked early on, preventive measures can be taken (treatment of hypertension, smoking cessation, low-fat diet) or surgery can be performed to repair the aorta long before there is a substantial risk of the aneurysm rupturing, which results in a medical emergency with substantial cost to the health system and less than an 80% chance of survival. Since AAAs are typically only documented in narrative physician notes (that make up a large part of the 80% of unstructured health data) as incidental findings that are easily missed next to the principal diagnosis that is treated and billed for, sophisticated natural language understanding technology that can parse unstructured clinical notes and identify references to AAAs and the size of the abdominal aorta is needed to effectively detect and track such patients.

These are just a few examples of how emerging technologies in support of population health analysis are quickly and vastly improving a health system's ability to identify high-risk patient populations, prevent serious illnesses from developing and to manage chronic diseases in a way that reduces cost and improves patient outcomes.

But we are still in the stone age of using data to drive ACOs and I have no doubt that we will see accelerated successes over the next years, as more and more health systems invest into the technology infrastructure that will allow them to become significantly more successful than the early results of the Pioneer ACO program indicate.

of the critical capabilities for a successful accountable care organization (ACO).
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What can predictive analytics do for healthcare reform?

What can predictive analytics do for healthcare reform? | Analytics & Social media impact on Healthcare | Scoop.it

The shift from pay-for-service to pay-for-performance in healthcare means that healthcare organizations and providers must approach care delivery in a different way, moving from diagnostic care to preventive medicine. Part of the challenge of adopting a forward-looking approach is having the right tools, namely health IT systems with the ability to predict what’s next. Analytics traditionally stops at the present time, and we’re now applying this to the future so that you can add predictive analytics,” says Simon Arkell, CEO of Predixion Software, a developer of predictive analytics solutions for healthcare. “Although they sound the same, they’re different ways of approaching problems. It’s great to have a dashboard with insight on what’s happening or has happened, but unless you’re projecting what’s going to happen and then recommending the right steps to take advantage of that new knowledge, then you’re leaving money on the table.”

One area of healthcare already showing promise involves avoiding unnecessary or preventable readmissions. “The readmission problem is a big one and that’s one of the areas we focus on. It’s a very expensive problem,” observes Arkell.Carolinas HealthCare System, a Cerner shop in Charlotte, NC, saw the potential for using predictive analytics to identify levels of readmission risk based on pulling data from a variety of sources.“We did a deal with them to implement predictive readmissions management,” continues Arkell, “and what that means is that through their different data sources — clinical, claims data, pharmacy, etc. — you’ve got information coming in so that once a patient is admitted into a hospital they’re immediately given a risk of readmission score.”The process, however, goes further than simply assigning risk scores. In order to be meaningful use, clinicians must have actionable information at their fingertips, says Arkell.“They need to know which patients are at risk of readmission, and not just too know what the risk is but what do I actually do about that,” he explains. “How am I going to intervene on the patient in a very specific way so that I know for a fact that that readmission risk is going to drop as a result of that intervention?”As it stands right now, the ceiling for predictive analytics in healthcare appears incredibly high. In fact, the limit to its applicability looks to be based more on what healthcare organizations want to do rather than what the technology is capable of.“Predictive analytics is a great technology to apply to almost any problem that you see in healthcare because it identifies the risk of something bad happening before it happens and then it allows you to take the necessary steps toward stopping the bad thing from happening,” Arkell explains. “And it’s underutilized in the industry.”Beyond Carolinas Healthcare System, Predixion is working with Kaiser Permanente and more recently the Indiana Health Information Exchange to apply predictive analytics to other pain points in healthcare. According to Arkell, it’s just the tip of the iceberg for all industries, not just healthcare.“We are projecting a massive trajectory as we go forward and take advantage of this intersection between big data, cloud, and especially with healthcare being 17 percent of GDP and all the problems there and the budget available to fix a lot of those problems,” he adds.As the requirements for adopting healthcare analytics (e.g., cost, infrastructure) become less burdensome and its capabilities faster and less resource-intensive, the technology has the potential to increase its foothold significantly — that is, so long as healthcare executives and providers are able to look forward to a more tangible return-on-investment by applying real-time machine learning to medicine.
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"Predictive analytics is a great technology to apply to almost any problem that you see in healthcare because it identifies the risk of something bad happening before it happens and then it allows you to take the necessary steps toward stopping the bad thing from happening"

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Why big data, healthcare analytics require key partnerships

Why big data, healthcare analytics require key partnerships | Analytics & Social media impact on Healthcare | Scoop.it
In order for healthcare organizations and providers to transition from reactive to proactive care, they will need to rely on new and emerging health IT tools that allow them to predict patient outcomes and provide intervention where necessary. This optimism for big data and healthcare analytics, however, is met by uncertainty as hospitals and physicians grapple with how to implement these solutions into their clinical workflows.

 

“That’s the part they’re most excited about and frankly that’s where they have the most deficiencies in terms of staff knowledge, tool sets, and capabilities,” says Curt Sellke, Vice President of Analytics at the Indiana Health Information Exchange (IHIE). “Predictive analytics is new enough for everybody that people are looking for a trusted partner.”To prepare for the move toward predictive analytics, IHIE recently announced a strategic partnership with Predixion Software to develop a solution for accountable care organizations (ACOs) and hospitals in Indiana to reduce preventable readmissions. The partnership brings together two crucial components for healthcare analytics: a rich data set and predictive modeling.According to Sellke, those looking to adopt healthcare analytics face a couple significant challenges. At the top of the list is access to health data.“First and foremost is it’s very hard to sometimes to get at the data you need in order to power your analytics,” he explains. “It is spread across different organizations or so many different silos within an organization, that just aggregating, accumulating, and cleaning it is really a tough thing to do.”Once that data is in hand, the challenge then becomes making that information meaningful (i.e., useful) to providers.“As you begin to generate some of these analyses you begin to see some trends and measures,” observes Sellke, “Can we make that information actionable, so that not only have you identified that something is happening or perhaps is going to happen on the predictive side? What do you do to intervene to make that better? That’s the second part of this.”Addressing these challenges is where establishing key partnerships comes into play and why IHIE is beginning its work on predictive analytics and readmissions with Indiana ACOs and hospitals. “We are starting with the ACOs because right now it would have the minimal impact on current workflows. They are laser focused on readmissions because they have both clinical and financial incentives,” says Sellke.By demonstrating the value of big data and healthcare analytics for ACOs and hospitals, IHIE hopes to build a case for widespread adoption of these tools and services in Indiana and beyond.“That’s why we’ve chosen the ACO side,” Sellke maintains. “That’s kind of the lowest-hanging fruit, and we figure as we roll out something new can we have it gain some traction, gain some favorable results there, and it makes the job of then bringing it out to a broader group even easier because there’s a case study behind it, return of investment kinds work, etc.”And as the value of predictive analytics grows, so too should its applicability to other areas of healthcare, claims Sellke.“Where we’re going to spend our energies over the next three to six months is going into our clinical data repository and sourcing the information that’s necessary to power the model,” he reveals. “There’s a notion that says we would like to be able to be in a position to produce a brand-new model for some specific use case in three- to six-month intervals.”The adoption of intelligent tools is half of the task of the healthcare industry’s shift to wellness. The other is the collaboration of forward-thinking healthcare organizations, providers, and vendors.
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Physicians, patients and payers exist in an integrated ecosystem enabling insights on what works and what doesn't, across segments.

Physicians, patients and payers exist in an integrated ecosystem enabling  insights on  what works and what doesn't, across segments. | Analytics & Social media impact on Healthcare | Scoop.it

UPMC has invested more than $1.6 billion in its IT infrastructure over the past five years, according to Pamela Peele, chief analytics officer of UPMC Health Plan. That's more money, she points out, than its home city has spent on three pro sports stadiums combined – "and we take sports seriously in Pittsburgh."

Those massive investments have paid big dividends, said Peele, speaking July 24 at the The Institute for Health Technology Transformation's Denver Health IT Summit, and showed how UPMC's strategies could be useful even for smaller organizations without that sort of financial muscle.

One of the advantages of being an integrated delivery system is that the physicians, patients and payers exist in what's essentially a "natural laboratory" where different types of data can be mined about what works and what doesn't, she said.

Robust analytics tools are essential to uncovering inefficiencies and pointing the way toward best practices. But many organizations miss the big-picture when implementing the technology: "Analysis is the generation of new knowledge," said Peele. "It's not reporting."

But a crucial first step, of course, is getting data that's uniform and usable – "fit for consumption," as she put it. Alas, "the sad part of it is, there's nothing sexy or fun about making data fit for consumption."

Whether it's claims data, prescription information, lab results or some combination thereof, aggregating and normalizing data is key, she said. "You can't manage what you can't see."  

But "no vendor tool is going to do this for you," she added. The way towards "a single source of truth," is to "roll up our sleeves and make it happen."

The future is promising, as healthcare organizations are increasingly "using data in a way it was never intended to be used," said Peele. "Lab data wasn't meant for pop health management."

UPMC has gained valuable knowledge from its self-developed care management platform, and has been able to make big strides in predictive analytics. "When someone shows up to the ED we predict the risk that they will be readmitted within 30 days."

The nature of those doing the analyzing has changed, too. Back several years ago, analysts tended to be business-minded bean counters, unable even to arrive at a consistent number identifying the number of diabetics in a given Pennsylvania county.

Now, UPMC's analytics personnel each have different tasks, from clinical evaluation to strategic business analysis to database quality to modeling.

 

UPMC has invested more than $1.6 billion in its IT infrastructure over the past five years, according to Pamela Peele, chief analytics officer of UPMC Health Plan. That's more money, she points out, than its home city has spent on three pro sports stadiums combined – "and we take sports seriously in Pittsburgh."

Those massive investments have paid big dividends, said Peele, speaking July 24 at the The Institute for Health Technology Transformation's Denver Health IT Summit, and showed how UPMC's strategies could be useful even for smaller organizations without that sort of financial muscle.

One of the advantages of being an integrated delivery system is that the physicians, patients and payers exist in what's essentially a "natural laboratory" where different types of data can be mined about what works and what doesn't, she said.

Robust analytics tools are essential to uncovering inefficiencies and pointing the way toward best practices. But many organizations miss the big-picture when implementing the technology: "Analysis is the generation of new knowledge," said Peele. "It's not reporting."

But a crucial first step, of course, is getting data that's uniform and usable – "fit for consumption," as she put it. Alas, "the sad part of it is, there's nothing sexy or fun about making data fit for consumption."

Whether it's claims data, prescription information, lab results or some combination thereof, aggregating and normalizing data is key, she said. "You can't manage what you can't see."  

But "no vendor tool is going to do this for you," she added. The way towards "a single source of truth," is to "roll up our sleeves and make it happen."

The future is promising, as healthcare organizations are increasingly "using data in a way it was never intended to be used," said Peele. "Lab data wasn't meant for pop health management."

UPMC has gained valuable knowledge from its self-developed care management platform, and has been able to make big strides in predictive analytics. "When someone shows up to the ED we predict the risk that they will be readmitted within 30 days."

The nature of those doing the analyzing has changed, too. Back several years ago, analysts tended to be business-minded bean counters, unable even to arrive at a consistent number identifying the number of diabetics in a given Pennsylvania county.

Now, UPMC's analytics personnel each have different tasks, from clinical evaluation to strategic business analysis to database quality to modeling.

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Influence in a digital age | DigitallySick

Influence in a digital age | DigitallySick | Analytics & Social media impact on Healthcare | Scoop.it

The pod discuss influence in the digital age. What is influence in the context of social media? How can it be applied to health and pharma? Can influence be measured and is there any point in doing so?

 

Faisal Ahmed, Alex Butler and Andrew Spong are joined this week by special guest John Nosta


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Ten IT-Enabled Business Trends for the Decade Ahead

Ten IT-Enabled Business Trends for the Decade Ahead | Analytics & Social media impact on Healthcare | Scoop.it

Three years ago, we described ten information technology–enabled business trends that were profoundly altering the business landscape. The pace of technology change, innovation, and business adoption since then has been stunning. Consider that the world’s stock of data is now doubling every 20 months; the number of Internet-connected devices has reached 12 billion; and payments by mobile phone are hurtling toward the $1 trillion mark.

This progress both reflects the trends we described three years ago and is influencing their shape. The article that follows updates our 2010 list. In addition to describing how several trends have grown in importance, we have added a few that are rapidly gathering momentum, while removing those that have entered the mainstream.

 

Read the complete article by clicking title

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Big Data in Healthcare: Social Media Can Help Track Disease Outbreaks, Pandemics

Big Data in Healthcare: Social Media Can Help Track Disease Outbreaks, Pandemics | Analytics & Social media impact on Healthcare | Scoop.it

While most industries today collect data – a lot of data – the healthcare industry may take the proverbial cake when it comes to the amount of potential data to collect. Think about it: given that science had now decoded the human genome, every patient walks into the front door of a doctor’s office, clinic or hospital automatically carrying about a terabyte of data before any patient history is even taken or physical examination is begun.

Beyond the codes contained in a patient’s body or the background contained in his or her medical history, or the images that can be captured via x-rays, CT scans, PET scans and MRIs, there is a plethora of other information that can be added to the “big data” pile, according to Frank X. Speidel, MD, writing for HIT Consultant.

“Beyond clinical, physiologic metrics, we ought also to capture the data of all that affects the patient,” writes Speidel. “Much of this expanded data will be unstructured such as is present in social network data set or quantified but predicted such as weather reports and pollen counts.”

Speidel recounts the story of two college students who presented to a hospital where he once worked as an emergency physician with lesions characteristic of meningococcemia. In an era before social media, university officials had to painstakingly piece together the students’ movements and activities over the past several days in order to determine whom the students had had close personal contact with.

“Flash forward to 2013,” writes Dr. Speidel. “Given the same presentation of two college students with meningococcemia, how much improved would our care be if we had access to their Twitter and Facebook data as we sought to identify those who had close contact with the students?”

Public health officials have already begun to tap social media as an excellent tool for tracking disease outbreaks. This, of course, raises privacy issues, which are much on the nation’s mind since the revelations about the NSA’s data tracking.

“There’s a challenge here in that some of these [data] systems are tightening in terms of access,” John Brownstein, director of the computational epidemiology group at Children’s Hospital Boston and an associate professor of pediatrics at Harvard Medical School, told the NIH publication Environmental Health Perspectives. “But we are seeing a movement towards data philanthropy in that companies are looking for ways to release data for health research without risking privacy. And at the same time, government officials and institutions at all levels see the data’s value and potential.”

In the future, we might see ourselves signing waivers or addenda to our social media accounts indicating that it’s OK for health officials to mine our data for critical information in case of an outbreak or a pandemic. It’s one more element of “big data” that could ultimately be used to save lives.

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First Ranking Of Top 30 CEOs On Social Media

First Ranking Of Top 30 CEOs On Social Media | Analytics & Social media impact on Healthcare | Scoop.it
This is the first global ranking of CEOs on social media. These CEOs are the pioneers and early adopters. Their impact is prompting other CEOs to rethink whether they should be involved in social
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Digital health funding is up, but growth slows, says Rock Health

Digital health funding is up, but growth slows, says Rock Health | Analytics & Social media impact on Healthcare | Scoop.it

Funding for digital health startups is on the rise, but growth is slowing overall, startup accelerator Rock Health said in a mid-year report on the industry.

On Monday, the San Francisco-based program for health technology startups, which tracks digital health investments that are more than $2 million, said startups in the field attracted $849 million in the first half of the year. That marks a 12 percent increase over the same period last year but suggests growth is slowing down: last year, funding climbed 73 percent in the first six months of the year, the accelerator said.

Rock Health’s report only includes data for three years and digital health is a relatively young industry, so it’s too early to say if this decline is truly an indicator of a broad investment trend. It’s also worth noting that Rock Health is fairly conservative with its definition of digital health and excludes seed rounds (which it says are too difficult to track systematically), so it will be interesting to see what other forthcoming industry reports reveal. But it’s still interesting to see that investments may be coming in at a slower pace.

Even if digital health funding is decelerating, the field is in a better place than traditional life sciences and the venture capital universe overall. As Rock Health points out, PricewaterhouseCoopers reports that, in the first quarter of 2013, medical device funding declined 29 percent and biotech funding dipped two percent. According to PwC, venture capital funding across all fields fell six percent in the first quarter of the year.

In digital health, Rock Health says that the biggest themes – which account for nearly half of the funding tracked – are remote patient monitoring, analytics and big data, hospital administration and electronic health records. That’s consistent with monthly reports from the New York-based health technology academy Startup Health.

The report also notes that while crowdfunding is emerging as a powerful platform for early-stage health startups, the health-specific platforms haven’t gained the same traction as general crowdfunding sites. (In June, we reported that Health Tech Hatch, a crowdfunding and codesign site for health startups, teamed up with Indiegogo after realizing the challenges of crowdfunding in healthcare.)

Rock Health also reported that The Social+Capital Partnership was the most active investor, with five digital health investments, followed by Norwest Venture Partners. In total, 146 investors participated in 90 deals that were more than $2 million. Of those companies, 31 percent are in California, 9 percent are in Massachusetts and 8 percent are in New York.

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The (Healthcare) Social Network

The (Healthcare) Social Network | Analytics & Social media impact on Healthcare | Scoop.it

Social media’s impact on the healthcare industry is greater than it’s ever been with entrepreneurs developing industry specific platforms and a cottage industry of “executive education” springing up

Social media – Twitter, Instagram, and Facebook to name but a few – have been impacting healthcare for as long as they have been around. Now, instead of simply using existing social media, healthcare entrepreneurs are developing platforms designed specifically for the industry.

Smart Phone Healthcare reports on ECG Capture, “an iPhone app that is being lauded as the ‘Instagram for Heart Attacks’” that was actually inspired by the online photo-sharing and social networking service. Developed by students and faculty from the University of Virginia, ECG Capture was tested more than 1,500 times and was found to transmit vital ECG data in less than six seconds, far less than the up to two minutes  traditional methods can take.

Forbes contributor Larry Husten describes ECG Capture by writing, “The iPhone app takes a photo of the ECG, reduces its size, and transmits the image over a standard cell phone network to a secure server. The image can then be viewed at the receiving hospital by physicians qualified to read an ECG.” This method of delivery, combined with drastic reduction of transmittal time, could save lives.

Facebook is also serving as inspiration to healthcare, from Wichita, KS, to Bristol in the United Kingdom.  The Wichita Business Journal reports on Adam Flynn, “a physician by trade (who) is leading an effort to push Electronic Medical Solutions LLC — a company he and two other partners own — forward to help health care providers share patient information securely and in real time.”

Flynn saw the need for a system to alert healthcare providers when electronically-stored patient information is available and designed a “Priorus system (that) works like other social media sites, such as Facebook, allowing information to be posted and shared quickly.” According to The Wichita Business Journal, “The main difference is that information is more secure, and Electronic Medical Solutions does an independent verification of each user before he or she is granted access.”

Flynn’s platform mirrors that of another Facebook-inspired clinical social network reported on by The Guardian. DocCom was an idea born in 2007 when “two young trainee surgeons frustrated by the ineffective communications that restricted (their) ability to make a difference” harnessed social networking technology to develop a secure cloud-based solution exclusively for healthcare. Dr. Jon Shaw, founder of DocCom, writes in The Guardian, “The DocCom system is like Facebook, and enables clinicians to find colleagues, connect, collaborate, and share information securely. The privacy of networks is protected by identity, validation, and authentication checks for users.”

Healthcare social media consultant Symplur didn’t repurpose an existing social media technology, rather it mined Twitter and incorporated the information found in tweets to design The Healthcare Hashtag Project. The goal of The Healthcare Hashtag Project is to make “the use of healthcare social media and Twitter more accessible for the healthcare community as a whole (by) lowering the learning curve of Twitter with a database of relevant hashtags.”

 

According to its website, Symplur’s database of hashtags reveal where healthcare conversations are taking place and who to follow within a specialty or disease, as well as provide trending information from conferences in real-time or archive.

Other organizations are following Symplur’s lead by helping organizations learn how to use existing social media effectively. The Chartered Institute of Personnel and Development (CIPD) reported on an NHS Employers guide for chief executives that “explores how using social media platforms can help … develop a collaborative leadership style that helps get results in the complex system of health and social care.” It lists the top five tips on how social media can help chief executives in their day-to-day jobs as:

deliberative engagementsetting, maintaining and communicating a visionconsistent communication with multiple stakeholdersnetworking with peershelping build a collaborative leadership style

Healthcare Finance News offers five social media tips specifically for hospitals courtesy of Lee Aase, director of the Center for Social Media at Mayo Clinic, who said, “Using social media may be a fairly new concept to hospitals and health organizations – hospitals, for the most part, are three to four years behind the general public – but the return on investment can be incredible. If you keep your investment really small, you keep your ROI really high.”

Aase’s five tips include keeping things simple, utilizing Twitter and Facebook, and establishing a hospital blog. Aase concludes by saying the “Mayo Clinic’s success in utilizing social media comes from its multi-platform approach in which the hospital utilizes as many social media outlets as possible.”

HealthCanal takes the impact of social media one step beyond enhancing healthcare to serving as a catalyst that “can revolutionize medicine,” writing, “Social media are often beyond the control of government, and allow citizen groups to form, share information and respond more quickly and with greater reach than ever before. With so much disaffection with modern healthcare, will healthcare too soon have its own Arab spring?”

HealthCanal concludes by writing, “No one is saying Facebook or Twitter are the solution to changing health patterns (although they might help). The opportunity we have is to learn from the success of these technologies, and to understand how we can use similar tools in healthcare.”

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ChandAgarwala's curator insight, June 24, 2013 10:57 PM

Of late, Healthcare stakeholders have started use public social media and their data for geenrating insights on required medication and influencing product development. We hope lack of clarity on regulation and ethical concerns will not styme it. There is huge scope to address inefficiencies and improve productivity to control wastage.

Lori Eddlemon's curator insight, July 2, 2013 4:24 PM

We are seeing IT plans for 2014 incorporating the management of this type of data as a top priority.

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The New Era of Cognitive Computing applied to healthcare

The New Era of Cognitive Computing applied to healthcare | Analytics & Social media impact on Healthcare | Scoop.it
As intelligent computer systems become more
adept at learning and adapting, they are introduced into new industries and
forge relationships with humans that recall something from a Science Fiction
novel.
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If You Build It, Will They Come?

If You Build It, Will They Come? | Analytics & Social media impact on Healthcare | Scoop.it

Meaningful Use requires a patient portal to successfully gain incentive dollars in 2014. Many healthcare organizations’ IT departments are forging ahead with portal implementations, secure in the knowledge that patients are clamoring to interact and communicate virtually with their physicians and hospital system.  With all of the effort and dollars healthcare organizations are putting into IT and eHealth, a large blind spot often exists in strategic thinking and planning.  While technology tends to focus on the “what,” or the features and functions that can be provided, the “how” and, more importantly, “why” people – providers and patients – are going to use the patient portal is a consideration lacking in most organizations.

Healthcare organizations must treat the roll out of a patient portal much like it does any other service they are promoting with a marketing campaign – with a clear strategy and objectives, a target audience, and a process to measure success.  Marketing and IT, along with leadership, must work together to achieve this.  IT is the subject matter experts that can clearly define what the capabilities of the technology are.  Marketing can then put together all of the materials needed to educate and drive portal enrollment and use.  The education process cannot be limited to patients.  Providers, call centers, admission and discharge staff and community outreach coordinators all need to be knowledgeable on the importance and benefits to portal adoption.  

Providers are on the front line of healthcare and still have the most influence over patient behavior. They will be the primary drivers of patient portal adoption and must understand its benefits to their patients and their health. The organization must work with providers and their staff to establish new workflows that incorporate the enrollment steps as seamlessly as possible in existing routines.  Internally, organizations must establish methodologies for verifying identities and rules for family and proxy accounts to ensure smooth, and compliant, processes. 

Technology can be disruptive and change is difficult.  Change management relies on communication, education and relationships.  Patient portals are a tool, part of an arsenal that organizations must employ to achieve patient engagement goals.  With cross functional participation, provider engagement and workflow alignment, patient portal adoption, and thus Meaningful Use incentives and population health goals, can be exceeded and long-term competitive advantage established.

To discover more about strategic plans to help you establish processes for patient portal rollout, adoption & utilization, learn more about the Virtual Influence Planning group, the experienced, unbiased healthcare and IT consultants.

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The business case for big data and healthcare analytics | EHRintelligence.com

The business case for big data and healthcare analytics | EHRintelligence.com | Analytics & Social media impact on Healthcare | Scoop.it

As is the case with most revolutions, it’s hard to see how the new order will establish itself while we’re still in the midst of the chaos, and “big data” is no exception to the rule.  “Data is good” and “the more data the better” may be familiar slogans by now, but why are we spending so much time collecting information, and what are we going to get out of it?  For providers on the front lines, overwhelmed with reporting initiatives, shrinking profits, expanding partnerships, and an explosion of new requirements, it’s not always easy to see the answer.  But there is a business case for incorporating analytics into healthcare, and it really will help providers in the long run.

 

Right now, the kingdom of analytics is ruled by the big academic medical centers and sprawling health systems like Kaiser Permanente.  With the financial and staff resources and the data to delve into the wealth of information collected by their long-standing EHR systems, the large systems are perfectlypositioned to turn deep pools of information into actionable business intelligence, population management tools, and financial trending forecasting.  Patient data is increasingly becoming a vital business asset to hospitals looking to cut costs and improve services while adhering to healthcare reform preceptsmandated by the government.So how will it help providers?  And how will it help patients?  The answer lies in identifying patterns and acting on them.  Analytics tools are actively reducing preventable readmissions, seeking out undiagnosed diabetes patients, helping ophthalmologists study eye diseases, creating designer treatments for cancer patients, and identifying population health trends through DNA analysis, among other practical applications that have an immediate impact on patient lives.Providers who feel like meaningful use criteria and quality reporting programs are a pointless and time-consuming addition to their already packed schedules can look at these use cases and take comfort in the fact that the data is going somewhere important.   The second and third stages of meaningful use are geared towards opening up EHR systems to health information exchange that allows providers to share data and aggregate information for analytics, which will help target high-risk patients and get them the treatment they need to prevent expensive hospitalizations in the future.As the industry moves towards accountable care and away from fee-for-service reimbursement, predictive risk analysis is going to become critical in the fight to keep patients out of the hospital, which will in turn keep providers raking in revenues.  “If a health system is going to try to take on risk and do bundled payment, they need to know where their variation is in their service lines,” says Brett Furst, CEO of ArborMetrix, a healthcare analytics company specializing in specialty and acute care. “The risk is shifting to the providers, so there’s a bit of inevitability to it.  For the smart and really leading-edge health systems or even health plans, good analytics to understand where the variation and opportunity are is upon us.”“Opportunity” is the key word when it comes to big data.  Analytics prepares business for what comes next, gives them a leading edge in a crowded, difficult market, and helps executives and managers make the right choices based on cold, hard facts.  “You want to get the big value of big data?” Furst asks. Successful businesses are going to be those “who are applying their own algorithms and filtering out the noise to get something that’s actionable: to find the signal, as we like to say in statistics.”
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4 Barriers to Big Data Analytics in Healthcare Organizations

4 Barriers to Big Data Analytics in Healthcare Organizations | Analytics & Social media impact on Healthcare | Scoop.it

84% of CIOs and other C-Suite health care executives believe that the application of big data analytics in healthcare organizations is a significant challenge, according to a survey from the eHealth Initiative and the College of Health Information Management Executives.

Key stakeholders from over 102 healthcare organizations participated in the survey conducted over a four week period from May 30 to June 28, 2013 examined the attitudes toward data use, trends in business use cases for data and analytics, the technological solutions employed by organizations, and associated challenges and barriers.

To adapt the growing volume of electronic data, healthcare organizations are increasing their focus on building a scalable plan to leverage data and predictive analytics that meets their organization’s strategic plans.

Despite the growing focus on big data and analytics, the survey identified four major barriers:

Lack of appropriate trained staff (64%)Data ownership and/or governance issues (53%)Data integration (40%)Lack of funding (39%)

Other survey findings include:

A large majority (82%) indicated that bi-directional sharing of clinical and/or patient data with local healthcare organizations is important or very important to their organization.Nearly 90 percent of respondents use analytics for revenue cycle management. The most common use case was managing accounts receivable metrics (82%), including denial rates, take back rates, claim/payment volumes and outstanding receivables.Two-thirds of respondents use analytics to prevent fraud and abuse, and only 26% of respondents viewed the use of analytics for fraud and abuse as a key business area in the coming years. The most common use cases were cost trending/forecasting (38%) and care utilization analysis (35%).82% of respondents identified population health management as a key analytics business area in the coming years.Quality improvement was the most commonly reported use case (90%) for analytics. Inpatient care utilization and outcomes analysis (80%) and adverse event reporting (75%), were among the most widely reported functionalities.The two most common data sources were administrative data (77%) and claims based data (75%). Unstructured textual data (47%) and remote monitoring device/sensor data (31%) will likely rise in prominence in coming years as technology advances and devices become more ubiquitous.
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Why your health records are stuck in the 20th century

Why your health records are stuck in the 20th century | Analytics & Social media impact on Healthcare | Scoop.it

"The United States has spent a lot of time and money to digitize healthcare records, but the effort has not gotten very far yet."


Via Andrew Spong
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Art Jones's curator insight, August 13, 2013 1:09 PM

Excerpt: About 93 percent of doctors say they use some type of electronic record-keeping, which can mean anything from physicians' notes to billing, according to management consulting company Accenture Plc.

  

But only 45 percent are using their systems to access data from outside their own organizations, which could simply be mean looking up labs

 

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Healthcare CIOs Cite Growing Impact of Analytics

Healthcare CIOs Cite Growing Impact of Analytics | Analytics & Social media impact on Healthcare | Scoop.it

The U.S. health care industry is increasingly turning to big data to improve outcomes, reduce costs and better allocate resources, despite obstacles. 

These were among the chief findings of a survey conducted this spring by the eHealth Initiative and the College of Health Information Management Executives. The survey of CIOs and other C-level health care executives at 102 provider groups, hospitals, health systems and health information exchange organizations examined trends in data use and associated challenges and barriers. 

Nearly 80 percent of the respondents agreed that big data and predictive analytics were important to their institution’s plans and priorities, but an even larger number – 84 percent – believe that their organization faces significant challenges in terms of applying these technologies. Only 45 percent of the respondents indicated that their organization has a workable plan for making use of the growing volume of health data that’s available to them.

Among the key findings:

Data analytics are used for a wide range of applications including revenue cycle management, resource utilization, fraud and abuse prevention, population health management, and quality improvement.Eighty-two percent of respondents are engaged in sharing patient and clinical data with local health care organizations.Nearly 90 percent of respondents are using analytics for revenue cycle management. Two-thirds of the respondents are using analytics to prevent fraud and abuse. The most common use of analytics, reported by 90 percent of the respondents, was for quality improvement.Administrative and insurance claims data were reported to be the most common data sources, but unstructured text-based data and device and sensor data are expected to become much more important going forward, as the technology matures and devices become more ubiquitous.Only 18 percent of respondents have staff sufficiently trained to collect, process, and analyze data. Sixteen percent address these shortages by employing third-parties such as consultants, while 26 percent report that although they have tried hiring more staff for analytics, they haven’t candidates who are sufficiently trained. Another 34 percent complain that their senior management hasn’t made analytics a staffing priority. Other common barriers to conducting more analytics include data ownership and governance issues, data integration challenges and lack of funding.  

The eHealth Initiative is a Washington D.C.-based non-profit organization that advocates for greater health care efficiencies through the applied use of information technology. eHealth is sponsored by over 200 members representing a broad spectrum of industry participants and stakeholders. 

CHIME is an executive association dedicated to serving chief information officers and other senior IT executives in the health care industry. Headquartered in Ann Arbor, Michigan, the association has more than 1,400 CIO members.

Elliot M. Kass is a freelance writer for Securities Technology Monitor and other SourceMedia publications.

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How to shift from a siloed use of analytics to gaining insight from an end to end clinical process

How to shift from a siloed use of analytics to gaining insight from an end to end clinical process | Analytics & Social media impact on Healthcare | Scoop.it

In the world of modern medicine, it takes approximately seventeen years of original clinical research to be integrated into the day-to-day practice of medicine. With all this knowledge, asking an individual doctor to rely on his memory is like asking travel agents to memorize all airline schedules.

But let’s fast forward into the future. Imagine this:

Triage being aided by an Intelligent Knowledge Application.All the medical knowledge in the world being available to a practitioner during a patient encounter.The right care being given to the right patient at the right time – all the time.

Read the complete article on site by clicking the title

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How Healthcare Will Benefit From Big Data

How Healthcare Will Benefit From Big Data | Analytics & Social media impact on Healthcare | Scoop.it

 

 

In the week leading up to this year’s MIT Chief Data Officer Information Quality Symposium, SiliconANGLE presented a series previewing the event, themed ‘Big Data Demands Good Data’. Our series focused on presenting a synopsis of some of the important topics scheduled to be covered in Cambridge the following week.

Today we are re-visiting the presentation offered by Dr. David Levine, Vice President of Informatics/Medical Director of Comparative Data and Informatics for United Healthcare. In his session, Levine addressed how the recent and rapid improvements in both data collection and data analytics must be embraced by health administrators in order to affect better patient care.

However, the difference between saying and doing is, at times, difficult to overcome. The mountains of data collected by the healthcare industry require education in the ability to analyze it with the intent of designing better predictive models for both patient care and hospital administration. As mentioned in our preview article, Chris Belmont, CIO at New Orleans’ Ochsner Health System stated, “We have the data points. We just have to do a better job of getting our hands around the data and understanding it better.”

On SiliconANGLEs theCUBE, which live-streamed from the symposium, hosts Dave Vellante and Paul Gillin sat down with Anthony Donofrio, Chief Technology Officer and Senior Vice-President with Truven Healthcare Analytics, to discuss the future of data collection and data analytics within the healthcare industry, specifically.

Donofrio’s company offers analytic products at all levels in the healthcare industry. He breaks it down to consumers, providers and payers, with each community receiving data analysis for their specific community.

In the interview, which you can watch in its entirety by clicking on the title

 

Read the complete article and watch the interview by clicking the title

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Healthcare Analytics Market with Trends & Global Forecasts to 2017

Healthcare Analytics Market with Trends & Global Forecasts to 2017 | Analytics & Social media impact on Healthcare | Scoop.it

Research and Markets ( http://www.researchandmarkets.com/research/qkwqbw/healthcare) has announced the addition of the "Healthcare Analytics/Medical Analytics Market - Trends & Global Forecasts to 2017" report to their offering.

(Logo: http://photos.prnewswire.com/prnh/20130307/600769 )

Healthcare organizations have large amounts of data but often do not have the tools to bring the data together for useful business information and planning. Healthcare analytics is the systematic use of data and related business insights developed through applied analytical disciplines such as, statistical, contextual, quantitative, predictive, cognitive, other including emerging model to drive fact-based decision making for planning, management, measurement and learning. Analytics may be descriptive, predictive or prescriptive.

Healthcare analytics involves application of statistical tools and techniques to healthcare-related data in order to study past situations such as operational performance or clinical outcomes to improve the quality and efficiency of clinical and business processes and performance.

The healthcare analytics market is showing a double-digit growth due to supportive elements such as digitization of world commerce, the emergence of big data and the advance of analytical technologies. Healthcare organizations can differentiate themselves through data analytics. Factors such as, federal healthcare mandates, wide scope of predictive analytics and improvements in the financial and operative function are driving the installation of healthcare data analysis in hospitals. While, the major concerns of this market are the security of data, privacy of individual patients and lack of manpower with cross-functional analytical skills. The healthcare analytics market is estimated to be $3.7 billion in 2012 and is growing at a rate of 23.7% from 2012 to 2017 to reach $ 10.8 billion.

Healthcare payers as well as the providers are leading the users of health care analytics for a range of functions from suggesting the most accurate diagnoses, cost reduction, fraud prevention, revenue generation, service improvement to real-time view of the business. The major driver for business analytics is the return on Investments (ROI), with a median of five years, from 10.0% to 1,000.0%.

The American Recovery and Reinvestment Act of 2009 (ARRA) offers incentives for hospitals and physicians who adopt technology and document related to patient safety, coordination, and quality of care. Data analytics tools are becoming an attractive purchase for decision makers, even in an economic climate forcing hospital budget cuts, layoffs and closures.

Healthcare Analytics provides several benefits across payers and providers, which are expected to increase, as the healthcare data analytics market is still in the nascent stages. In addition, as the analytics penetrates the Asian market there would be niche benefits, which would be discovered, based on the needs.

The healthcare analytics market is a well-established market in the U.S. It is showcasing double-digit growth compared to other healthcare IT market. The European market is the second largest market, growing at a lower rate due to the economic crisis. The Asian market is relatively new to the healthcare data analytics, however, the increasing initiatives and outsourcing will drive the market. Australia and New Zealand are developed market in terms of healthcare IT, and are setting an example for the use of healthcare data analytics.

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Early BI Lessons from an Analytics Pioneer

Early BI Lessons from an Analytics Pioneer | Analytics & Social media impact on Healthcare | Scoop.it

Shawn Griffin, M.D., chief quality and informatics officer at Memorial Hermann Physician Network, is surprised that nearly 60 percent of respondents to a KLAS Enterprises survey of data analytics vendors where able to pick a handful of firms they perceive to be early leaders in health care business intelligence applications.

A few years ago, there were no vendors with a decent level of experience in health care, Griffin noted at Health Data Management’s Healthcare Analytics Symposium in Chicago. Much of the analytics work that Memorial Hermann has done is development partnerships with vendors who still are learning the nuances of business intelligence in health care. Developers think that once you have metadata you have population health, he said. But you need to put data in the hands of doctors to know which of their patients are at greatest risk. “There’s not a single vendor being mentioned today that you should write their name down and say ‘that’s the one,’ because they don’t exist.”

Business intelligence professionals in health care organizations need to make sure they are in the room during contract negotiations for any project that involves analytics, Griffin advised. “How will you measure results of what you are buying? You need to understand what you want to measure.”

Everyone is a rookie in health analytics, so don’t be timid to get into the game, Griffin said. “Nobody is doing this right yet. Don’t be afraid to get out there and be a clumsy dancer.”

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using predictive analytics to improve medication compliance and reduce hospital readmissions

using predictive analytics to improve medication compliance and reduce hospital readmissions | Analytics & Social media impact on Healthcare | Scoop.it

IBMIBM +0.87% and several leading hospital groups which are part of the Premier healthcare alliance are using predictive analytics to improve medication compliance and reduce hospital readmissions.

Studies show that about half of Americans don’t take medications as prescribed, which leads to $100 billion a year in additional hospital re-admissions and treatment. For people with chronics conditions, delaying or missing even one dose can lead to major complications.

 

We have to make it much easier for the doctor to take information out of an electronic medical record system that has been designed for billing and make it available [for assistance in treatment],” said Paul Grundy, IBM’s director of health care transformation.

Some of the most valuable information in medical records is in unstructured data, he added, such as social factors that are useful predictors that someone with congestive heart failure will be readmitted after a hospital stay.

Now a group of IT and clinical experts have launched the Data Alliance Collaborative (DAC) to develop and share knowledge, data and resources to move toward more integrated systems for health care. Drawing on their knowledge of clinical care, they are developing analytics for population health management.

They have a challenge ahead of them. Legacy electronic medical records cannot integrate clinical, financial and operation data, so providers are making major investments in separate business intelligence and analytic solutions on data that is locked in silos.

“Instead of investing in and developing multiple, fragmented solutions that address the same problem, we’re pooling resources to develop single solutions we all can use,” said Terry Carroll, senior vice president of transformation and chief information officer for Fairview Health Services, and DAC chair. “We’re using big data, as opposed to local or siloed data, and will get richer insights as a result. Sharing assets and testing new and innovative ways to use analytics will help us achieve system-wide change that positively impacts quality, cost and the care experience.”

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Future of Drug Development Focuses on Patient Experience -- And Digital Health Tools Can Help

Future of Drug Development Focuses on Patient Experience -- And Digital Health Tools Can Help | Analytics & Social media impact on Healthcare | Scoop.it

In the latest NEJM, UNC oncologist (and former MGH colleague) Ethan Baschexplains with characteristic eloquence why the future of drug development will involve a more granular understanding of the patient’s experience of illness.

What’s especially interesting about Basch’s examples are that they come from a field – cancer — in which you might think so-called “hard” endpoints (like progression-free survival [PFS], say) would be all that mattered.  But you’d be wrong.

As Basch points out, in many cases, the key points of differentiation between therapeutics are (unfortunately) not differences in PFS, but rather the way the drugs make the patients feel during (and after) treatment.  An “incremental” drug that improves the tolerability of a cancer medicine can have a profound impact on patients.

There are two broader points here.

First, the increased emphasis on patient experience reflects a broader trend in healthcare, from a view of disease that focuses on the physician’s perception and assessment to a view – more appropriately –centered around the patient’s experience.  The need to better measure this experience has been recognized as a vitally important goal.

Second, medicine’s (including, as Basch accurately describes, pharma’s) need to better understand and reliably capture the patient’s experience of illness represents (as I’ve emphasized often in this space) a perfect fit for digital health, which should be able to provide exactly the right tools for this very important job.  This is an animating thesis of the MGH/MIT Center for Assessment Technology and Continuous Health (CATCH) (disclosure: I’m a co-founder), and many other digital health initiatives.

The need for improved patient-associated measurements is also a key reason medical product companies should care a lot more about digital health than they seem to.  As I’ve argued, most future drugs are likely to represent incremental improvements (again, somewhat unfortunately), and it will be essential (table-stakes) to understand, and have credible data around the often very important benefits a new product delivers to patients.

Who will deliver the robust digital health solutions medical product companies will increasingly require?  That is the question — and the opportunity.

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Donate Data! How Cultural Norms Will Shift for Healthcare Analytics | SiliconANGLE

Donate Data! How Cultural Norms Will Shift for Healthcare Analytics | SiliconANGLE | Analytics & Social media impact on Healthcare | Scoop.it

At the recently concluded O’Reilly Fluent conference we had a chance to talk with Roger Magoulas, research director of O’Reilly Media. A regular on theCUBE, Roger is on the cutting-edge of the developer market, noting the important trends and people, offering some great insight for developers and the Fluent event itself.

One of the things O’Reilly has been working on that piqued Roger’s interest is how health and data interact. It’s not the most technical topic right now, but health data has traditionally been manual and researchers used to conduct studies on a sample populations more or less around a hundred people. Today, there are sensors, medical records and genetic data that shows more than a hundred different factors that you can actually look at.  This was an impossible task to manage in the past, but the ability to cross-analyze more and more data points has led to some interesting discoveries, such as the correlation that people who floss are less likely to get congestive heart disease.

“There’s this whole correlation-causation thing. It’s just that people who floss take better care themselves, but others think there are some physiology thing around the microbes,”  Roger says.  ”People in noisy places as an example — they have different health outcomes than people in quite places. So we are trying to create this notion of a platform that helps bring lots of data sources together, and apply the Strata data science staff to changes in health care.”

Given the recent developments with PRISM, personal data protection is a topic that’s front and center when it comes to analytics, particularly in healthcare. So how do they do this whole health data curation without getting in trouble with HIPAA? They are backed by meaningful use Stage 2 under the ‘Health Information Technology for Economic and Clinical Act’ [HITECH Act] which states that people are entitled to their EMR data and they can do whatever they want with it, and that includes donating it to disease groups! That’s one of the big things O’Reilly is working on in the health data space right now; setting up a cultural norm of donating data.

Moreover, health data donors will love the fact that they will know different things about their lives that might have an effect on disease and how it’s expressed in their bodies. Obesity, for instance, is caused by a myriad of things, some of which aren’t immediately associated with personal weight gain, such as antibiotics.  Having a wider ream of data over longer periods of time will enable individuals to take more control over their own health, from a highly educated point of view.

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