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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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Enabling organizational strategies with analytics could drive better patient outcomes

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

 

 

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

 

 

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

 

 

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

 

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

 

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

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

 

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

 

 

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

 

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Poor interoperability a significant barrier for ACOs

Poor interoperability a significant barrier for ACOs | Analytics & Social media impact on Healthcare | Scoop.it

Though designed to improve care by sharing data from various sources, poor interoperability remains a huge barrier to accountable care organizations (ACOs), according to a survey from Premier and the eHealth Initiative.

In fact, all 62 ACOs responding to the online poll reported that access to data from external sources was a challenge for their organization.

"Even when ACOs have successfully adopted and merged HIT systems, they aren't able to effectively leverage data and analytics to derive value out of their investments," Keith J. Figlioli, Premier's senior vice president of healthcare informatics and member of the Office of the National Coordinator's Health IT Standards Committee, said in an announcement.

 

 

Those problems could stymie cost and quality improvements going forward.

Among the findings:

88 percent of the ACOs face significant obstacles in integrating data from disparate sources83 percent report challenges integrating technology analytics into workflowInteroperability of disparate systems is a significant challenge for 95 percent of organizationsAt least 90 percent of respondents cited the cost and return on investment of HIT as a key barrier to further implementationsAs ACOs pull data from more sources, they also report lower abilities to leverage their HIT infrastructure to support care coordination, patient engagement, population health management and quality measurement

These organizations have technology in place to improve clinical quality, the most common being electronic health records (86 percent), disease registry (74 percent), data warehouse (68 percent), clinical decision support (58 percent), and health information exchange (44 percent.)

However, the technology for distance-based medicine was less common. Only 38 percent had secure messaging, 36 percent had referral-management tools, 34 percent provided phone-based telemedicine and 26 percent had video-based telemedicine. That raises concern about rural ACOs' ability to leverage health IT to effectively manage remote populations, the authors said.

A previous eHealth Initiative survey found ACOs have made little progress in boosting their health IT capabilities in the past year. Seventy-six percent of respondents in that poll did not participate in a health information exchange at an enterprise, community or state level.

A study from Johns Hopkins, meanwhile, found robust analytics infrastructure that reports data in real time to care teams key to success for ACOs.

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Big Data: The Amazing Ways GE Is Using It For Success

Big Data: The Amazing Ways GE Is Using It For Success | Analytics & Social media impact on Healthcare | Scoop.it

General Electric – a literal powerhouse of a corporation involved in virtually every area of industry, has been laying the foundations of what it grandly calls the Industrial Internet for some time now.

But what exactly is it? In this post I will try and give a basic overview of the ideas which they are hoping will transform industry, and how it’s all built around big data.

If you’ve heard about the Internet of Things which I’ve written about previously, a simple way to think of the industrial internet is as a subset of that, which includes all the data-gathering, communicating and analysis done in industry.

In essence, the idea is that all the separate machines and tools which make an industry possible will be “smart” - connected, data-enabled and constantly reporting their status to each other in ways as creative as their engineers and data scientists can devise.

This will increase efficiency by allowing every aspect of an industrial operation to be monitored and tweaked for optimal performance, and reduce down-time – machinery will break down less often if we know exactly the best time to replace a worn part.

Data is behind this transformation, specifically the new tools that technology is giving us to record and analyze every aspect of a machine’s operation. And GE is certainly not data poor – according to Wikipedia its 2005 tax return extended across 24,000 pages when printed out.

And pioneering is deeply engrained in its corporate culture – being established by Thomas Edison, as well as the first private company in the world to own its own computer system, in the 1960s.

So of all the industrial giants of the pre-online world, it isn’t surprising they are blazing a trail into the brave new world of big data.

GE generates power at its plants which is used to drive the manufacturing that goes on in its factories, and its financial divisions enable the multi-million transactions involved when they are bought and sold. With fingers in this many pies, it’s clearly in the position to generate, analyze and act on a great deal of data.

Sensors embedded in their power turbines, jet engines and hospital scanners will collect the data – it’s estimated that one typical gas turbine will generate 500Gb of data every day. And if that data can be used to improve efficiency by just 1% across five of their key sectors that they sell to, those sectors stand to make combined savings of $300 billion.

With those kinds of savings within sight, it isn’t surprising that GE is investing heavily. In 2012 they announced $1 billion was being invested over four years in their state of the art San Ramon, California analytics centre, in order to attract pioneering data talent to lay the software foundations of the Industrial Internet.

In aviation, they are aiming to improve fuel economy, maintenance costs, reduction in delays and cancellations and optimise flight scheduling – while also improving safety.

Abu Dhabi-based Etihad Airways was the first to deploy their Taleris Intelligent Operations technology, developed in partnership with Accenture.

Huge amounts of data are recorded from every aircraft and every aspect of ground operations, which is reported in real-time and targeted specifically to recovering from disruption, and returning to regular schedule.

And last year it launched its Hadoop based database system to allow its industrial customers to move its data to the cloud. It claims it has built the first infrastructure which is solid enough to meet the demands of big industry, and works with its GE Predictivity service to allow real-time automated analysis. This means machines can order new parts for themselves and expensive downtime minimized – GE estimates that its contractors lose an average of $8 million per year due to unplanned downtime.

Green industries are benefitting too – its 22,000 wind turbines across the globe are rigged with sensors which stream constant data to the cloud, which operators can use to remotely fine-tune the pitch, speed, and direction the blades are facing, to capture as much of the energy from the wind as possible.

Each turbine will speak to others around it, too – allowing automated responses such as adapting their behaviour to mimic more efficient neighbours, and pooling of resources (i.e wind speed monitors) if the device on one turbine should fail.

Their data gathering extends into homes too – millions are fitted with their smart meters which record data on power consumption, which is analyzed together with weather and even social media data to predict when power cuts or shortages will occur.

GE has come further and faster into the world of big data than most of its old-school tech competitors. It’s clear they believe the financial incentive is there – chairman and CEO Jeff Immelt estimates that they could add $10 trillion to $15 trillion to the world’s economy over the next two decades. In industry, where everything including resources is finite, efficiency is of utmost importance – and GE are demonstrating with the Industrial Internet that they believe big data is the key to unlocking its potential.

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Data analytics, patient safety are top FDA priorities in 2014 | HealthITAnalytics.com

Data analytics, patient safety are top FDA priorities in 2014 | HealthITAnalytics.com | Analytics & Social media impact on Healthcare | Scoop.it
The increased use of data analytics for speedier clinical trials, more efficient regulation, and better population health management is at the top of the priorities list for the Food and Drug Administration (FDA) in the next four years, according to its latest FDA Strategic priorities report.  Analytics will be the foundation of several of the FDA’s ongoing initiatives, including reducing tobacco use, developing new methods of testing medical products and conducting clinical trials, and identifying health consequences of certain products or behaviors.“An increasingly global and complex marketplace, rapidly evolving technologies, and emerging areas of science are having a major impact on FDA’s mission to promote and protect the public health,” the report says. “FDA must tackle these new challenges expeditiously, as we continue to meet our core responsibilities. Working collaboratively with our international regulatory partners, we will continue efforts towards international harmonization and regulatory convergence. We achieve this by using smart regulatory approaches to streamline and modernize our regulatory programs and minimize regulatory uncertainty for industry, while protecting and maximizing public health and safety.”In order to accomplish its four core goals of oversight, access, informed decision-making and organization excellence, the FDA will rely on regulatory science, globalization, safety and quality, smart regulation, and financial stewardship.  At the heart of these strategies will be the use of analytics to inform standards for product development, distribution, and safety, as well as ways to detect consumer safety issues, risks, and adverse events.“FDA is already taking concrete steps to advance safety and quality across the Agency,” the report added.  “For example, the Case for Quality Initiative, which includes a voluntary compliance improvement program pilot, promotes medical device quality. The planned Office of Pharmaceutical Quality will highlight and consolidate quality principles and review throughout the drug lifecycle.”The Case for Quality Initiative will include a voluntary compliance improvement pilot program for medical devices, which “aims to reduce the risk of patient harm by helping manufacturers identify and deploy quality-related design and production practices,” the brief explains.  The Agency will also work to improve understanding of regulatory requirements and provide guidance to healthcare stakeholders and consumers through new rules and standards.“If biomedical science is to deliver on its promise, scientific creativity and effort must also focus on improving the medical product development process itself, with the explicit goal of robust development pathways that are efficient and predictable and result in products that are safe, effective, and available to patients,” the report concludes.

“Although FDA’s primary responsibility is to review the safety and effectiveness of new medical products developed by industry, the Agency is also committed to assisting product developers in translating discoveries in basic science into new therapies that will save lives and improve health care.”

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Keith McGuinness's curator insight, October 8, 3:37 PM

It would seem that a behavior change apps that demonstrate continued effectiveness would delight the FDA, and not require 'regulation.'  This seem as it should be.  Now we just have to incentivize behavior change app developers to measure outcomes, and get someone respected in the halls of academic research to bless the method of app data surveillance.  We are on it.


Oh yeah, one more thing...we need a payer to make this work.

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Analytics in the era of value-based care

Analytics in the era of value-based care | Analytics & Social media impact on Healthcare | Scoop.it

The power of revenue cycle and population health analytics multiplies when you manage them together

 

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Shift to Healthcare Consumerism: The Business of Healthcare

Our last healthcare blog, Meaningful Use: A Valuable Asset, focused on utilizing Meaningful Use and theHITECH Act as a means to effect positive change. We left off with the recommended shift to healthcare consumerism and provider relationship management.

What does that even mean? Healthcare consumerism is generally accepted as the movement to empower patients to be more involved in their own care and its associated clinical decision making. While accurate, I believe it should be expanded to include the shift of a provider organization's sole focus on the physician and move more towards the patient, which in this realm serves as the true customer / consumer.

In the world of business, the current healthcare model is unheard of. The healthcare provider market specifically has shunned a traditional "business model" for too long. It is hard to name another industry where the entire organization is so fiercely focused and built around the product or service provider, even at the expense of the customer relationship.

Most providers don't even have a classic sales, marketing, or business development function focused on patient acquisition. They serve whatever patients walk in the door, or are referred to them by other providers. While there is some focus around it in the market today, even referral management isn't a major organizational priority.

Organic growth is often not even considered. Growth most often happens through the acquisition of other providers, individual physicians, or physician practices.

Providers also tend to land on one end of the spectrum or the other; either specialty clinics focused on a specific service line or specialty, or a Walmart approach providing a full line of services to all patients.

This is the multi-net fishing approach; the more nets that you have out, the more fish you will catch, rather than the utilization of a specific bait to lure a specific kind of fish.

For too long, the focus has been on just clinical and operational improvement. Healthcare needs to start additionally focusing on patient acquisition and revenue generation.

Don’t get me wrong, I believe the best source for new patients is still physician and specialty referrals, but physician relationship management should be viewed as a vessel for patient acquisition rather than the goal itself.

Shouldn't providers identify and focus resources on service lines that are the most profitable and provide the highest standard of care? Acquiring patients that you want to serve based on a variety of factors that may include payer-mix, disease, service line, demographics, etc.

Even if your mission is to serve all patients, even including indigent care, as a great deal of the faith- and religious-based providers do, you should still have a focused patient acquisition strategy in order to better serve your entire patient base. The resulting revenue and profitability increase can be invested back into care delivery and clinical operations, by providing top tier physicians and technology and additional capacity in order to better serve your entire patient population.

To support these ideas, we need a new emphasis on provider and physician relationship management with a goal of targeted patient acquisition. We will address that in our next post Patient Acquisition Through Physician Relationship Management so stay tuned!

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A smarter way to find new drugs

A smarter way to find new drugs | Analytics & Social media impact on Healthcare | Scoop.it

The pharma sector needs to embrace emerging technologies like Big Data analytics and cloud computing

What is the secret sauce of accelerating innovation when it comes to critical areas such as drug discovery, personalised medicines or simulated healthcare? Embracing continual innovation was always an imperative for the life sciences companies to stay relevant, and stay alive. This is not just confined to the new drug discovery team within the company, it spans the entire value chain of the innovation ecosystem. The question is whether enough is being done to drive R&D innovation in the pharmaceutical industry.

And we cannot find the answers in silos. A cross-pollination of ideas across the ecosystem is vital as pharmaceutical companies are caught in the conundrum of navigating an increasingly complex global environment driven by competing market and consumer pressures, regulatory changes and a constant need for life-saving innovations.

Pressure on R&D
The truth is with investors getting wary, the price of drugs being perceived as high, and regulatory compliance getting increasingly stringent, R&D productivity is under tremendous pressure. Added to this, the introduction of innovative drugs has hit a slow track.

What compounds the woes is a high failure rate. Of every 5,000 projects, only one completes the drug development process and only one in five of these actually returns its R&D investment.

The time from drug discovery to approval can take up to 15 years; the average cost of bringing a pharmaceutical product to the market is $800 million and growing. The key to reducing costs lies in compressing the discovery cycle by eliminating redundant research and identifying new business models.

To sail smart in the new normal and thrive, pharmaceutical companies must tap emerging technologies such as big data analytics and cloud to streamline their IT operations and deliver safer and affordable healthcare for all. Advanced analytics, mathematical modelling and simulation tools and machine-based discovery technologies, enable enterprises to mine terabytes of data, uncover innovation opportunities and predict the most profitable research outcomes.

In addition to reducing cost, the optimal usage of such technologies can help save millions of lives and improve patient outcomes.

Disruptive technology
The good news is that with rising healthcare costs becoming a key constraint, enterprises, practitioners and policymakers are keen on exploring disruptive technologies to help solve critical, healthcare challenges.

For instance, a novel cloud-based clinical trial supply management solution helps life sciences companies significantly enhance efficiency of clinical trial processes by driving greater collaboration between pharmaceutical companies and contract research organisations.

Besides improving the productivity of the overall drug development process, this ensures timely and accurate supply of drugs to patients at reduced costs. As a result, enterprises can price products competitively while adhering to the stringent standards required to bring products safely to consumers.

Likewise, Big Data promises smarter healthcare — a paradigm shift from corrective to preventive medicine and personalised medicine, as silos of disparate information gives way to novel actionable insights for medicos.

With sophisticated data analytics technologies, machine learning software can point to abnormalities and predict health issues while smartphones and “iAnythings” are empowering the patient to monitor his health. As doctors rely on such technology for diagnosis and decision-making, there is a marked improvement in procedure performance, decreased healthcare costs, and improved patient-centric care.

Indeed, the emergence of advanced simulation technologies is proving a boon for better diagnosing osteoporosis and accurately quantifying fracture risk. This provides medical practitioners with a new, comprehensive and non-invasive way to examine individual bones and skeletal structure, and determine the best course of action.

Better understanding
For instance, 3D models simulating the working of the human heart offers medical professionals and scientists a near real-life scenario to improve understanding of the complexities of human heart disorders. The model simulates the heart’s functioning, in particular the deformation of heart tissues due to certain stress conditions.

While this helps medical professionals diagnose heart disorders faster and with a higher level of precision, it enables medical device manufacturers test and validate implants to detect and quickly correct anomalies. This is expected to lead to better-designed medical devices, faster regulatory and compliance approval, and improved time-to-market for those devices.

Promoting new life science technologies would require companies to forge a strong partnership across scientists, researchers and local communities — a collaboration rooted in transparency, clear guidelines for intellectual property and, most importantly, a patient-centric mindset.

Towards collaboration
For effective collaboration, data standardisation, integration and interoperability are vital. And information management, an important area on which pharmaceutical companies need to focus.

For instance, the entire clinical development process generates an enormous amount of data which is not efficiently used. If this information is integrated with that from the discovery phase and other studies, companies can garner insights that could result in new drugs or help avoid costly failures.

Making the clinical trial data available to all can accelerate the innovation trajectory and establish a stronger research foundation for the industry. While it requires an unwavering focus on future-oriented life-saving drugs and tech-enabled innovations to accelerate growth, the pharmaceutical industry must address drug prices and price discrepancies across regions.

Significantly, innovation thrives at the intersection of open collaboration amongst all the stakeholders including industry, academia, regulators, policymakers, government and investors. However, at the core of it remains a focus on patient-centric solutions to create a healthy economy and a pink planet.

(The writer is the CEO of EdgeVerve Systems_

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Population health and consumer centric healthcare are here to stay

Population health and consumer centric healthcare are here to stay | Analytics & Social media impact on Healthcare | Scoop.it
Population health and the increasing retail and consumer-minded approach are arguably the biggest shifts in the healthcare industry today, and they show little sign of letting up anytime soon.

So says healthcare consultancy firm the Advisory Board in a blog post, detailing potential strategies for providers.

“The truth of the matter is, retail medicine and population health are real, and probably around for the long haul,” writes Ben Umansky, noting that hospital leaders can be successful if they adopt the right strategies.

“We identified three strategic objectives in particular that are relevant in both types of markets:convenient access, lean cost structures, and a smart partnership strategy.”

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Convenience is a new concept in healthcare overall, so it’s not hard to understand why consumers, aka patients, won’t put up with the old guard ways of arbitrary, cloaked-in-secrecy pricing and long wait times for seemingly simple procedures. But it also has an indirect benefit for population health.

“When it’s easier for patients to get care, or even just information, when they need it, they’re more likely to follow care plans.That means better outcomes and lower total costs. Patients are also more likely to stay within your network if they can always access it, so you can be confident that the returns on your care management investments will accrue to you, not your competition,” Umansky writes.

Just as the concept of convenience shouldn’t be hard to grasp, the same is true of lean cost structures, but it’s more important than ever to actually achieve it with the turbulent road ahead.

Finally, with all the consolidation that has occurred, a smart partnership strategy is vital for any hospital’s survival.

“No matter the form it takes (and it’s not always M&A), a smart partnership can strengthen any organization’s appeal to retail consumers.”

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Novartis’ mobile health strategy poised to move from tracking to virtual care | mobihealthnews

Novartis’ mobile health strategy poised to move from tracking to virtual care | mobihealthnews | Analytics & Social media impact on Healthcare | Scoop.it

While Novartis’ recent partnership with Google and its longtime relationship with Proteus have indicated that the pharma company has an interest in digital health, a page on the company’s website, added this summer, lays out its broad vision and explicit interest in mobile health specifically. The company even has a mobile health strategy lead, Michele Angelaccio, who holds the title of Associate Director US Mobile Health Strategy at Novartis Pharmaceuticals.

“We have a unique understanding of the challenges doctors and patients are facing, and can help guide startups in building and testing proposed solutions,” Angelaccio says in the piece. “Partnering with these health technologists is the cornerstone of our mobile health strategy. It will continue to propel us forward as an innovator and it is the means by which mHealth will help us to meet our customers’ needs and solve some of the business challenges we’re facing.” 

In the post, Novartis highlights tracking and monitoring of patients as one of the biggest opportunities in mobile health. They mention the now-discontinued VaxTrak, for instance, as well asPodhaler Pro, an inhaler training app for cystic fibrosis patients.

Novartis currently has 13 iPhone apps in the Apple App Store, nine of which are patient or consumer-facing. The list includes two games, “Sickel Cell Iron Invaders” and “Marley’s World” which are designed to teach players about Sickle Cell disease and Multiple Sclerosis, respectively. It also includes MyNetManager and Clinical Trial Seek, two apps that launched last March.

The article also discusses a 2013 digital health challenge sponsored by Novartis, and ultimately won by home monitoring startup Sense.ly. They add that work is continuing to build on Sense.ly’s platform, which is set to come out of beta later this year.

Novartis’s interest in tracking as the primary vehicle for making the most of mobile health opportunities is displayed by the deals the company has been involved with over the last few years. It sponsored some major trials with Proteus Digital Health, a company that aims to track patients with ingestible sensors embedded in pills. This year, Novartis has also partnered with TicTrac to help multiple sclerosis patients engage in self-tracking and, in a high profile deal, signed on tolicense Google’s smart contact lens to help people with diabetes track their blood glucose levels.

The article concludes, however, with the suggestion that the company is getting ready to go beyond just tracking to technologies that “could reach the market in the near future, including some that enable patients to undergo testing, diagnosis and treatment remotely.” Perhaps the company’s interest in Sense.ly, which reaches out to homebound patients with a virtual clinical avatar, points to the sorts of technology Novartis is pursuing.

“Through solutions like these, we intend to make a major change in the way care is delivered, and increase access to health services,” Angelaccio said.

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What is “Social Proof” and Why Should You Care?

What is “Social Proof” and Why Should You Care? | Analytics & Social media impact on Healthcare | Scoop.it
Social Proof is important to your business but many aren’t familiar with the term. It is also known as Social Influence and basically, it boils down to people looking for cues on what to do based on what others are doing. Although we pride ourselves on individuality, we are find comfort in numbers and groups and often look to what others are doing for assurance that we are on the track.

So how does this impact your business? If someone is uncertain if they should make a purchase, they will look to others who have made the purchase for guidance. The more people they can see have purchased and the more people that were happy with the purchase, the more likely they are to buy themselves.

Social proof comes in many different formats, with the most common being:

Storytelling – you use a story to share the experiences of the people that have already used your product or service.Video or written testimonialsReviewsCase StudiesComments on posts that indicate people are interested in what you have to say and offerSocial engagement numbers including your subscribers, followers, fans, tweets, likes, and other social sharesStatistics (while stats can be and often are manipulated, there is still trust built by sharing data – especially if it can be validated by a third party)Imagery (of people using a product or services)

Did You Know:

Over 70% of Americans say they look at product reviews before making a purchase

Nearly 63% of consumers indicate they are more likely to purchase from a site if it has product ratings and reviews.

Pretty powerful stuff, eh? Makes you want to work on building up your reviews and other forms of social proof, doesn’t it?

Some tips for implementing “Social Proof” into your marketing.

Images make testimonials, comments and stories more believable which makes them more beneficial to you.Most people subconsciously like things that “resemble” themselves. When reading reviews, our brains place more weight on those people we deem to be the most like us. In testimonials and case studies, avoid generic “Great service!” quotes. Outline your buyer personas and capture a moment where they described a specific pain that they solved with your product/service. Try to find a customer that represents your ideal customer. If your other customers can relate to them, the testimonial will benefit you more.Use storytelling.

What would persuade you more, a 5-star review or a detailed story of how a certain product/services was able to solve someone’s problem? Both are great forms of social proof, but one is far more powerful than the other.

Stories are persuasive and more trustworthy than stats because individual examples remain in our minds, but statistics don’t.

Stories work because our brains are primed to heed their advice.

Stories are persuasive because they are able to transport us to the tale being told. (Researchers say we tend to imagine ourselves in other people’s shoes during a story.)

Use Influencers to achieve the “Halo Effect”:

Since an “influencer” has already established a reputation, anything they involve themselves with is seen in a better light by association. Connecting yourself to people or brands with credibility, transfer some of that credibility and trust to you by association.

Less is not more:

People look at the social proof you make available to them and determine if it’s “enough” to be compelling. If you don’t have enough, you are better to go with none until you can bulk it up. With none people have nothing to judge, but with “a little” they start to wonder why there isn’t more.

Use Social Widgets:

Display your social networks and engagement numbers with widgets on your site or Blog.

Use Case Studies:

Case studies show what other people who were just like you are now experiencing as a result of making a decision to buy something that you have the option to buy too. If you see enough of these sorts of case studies you start to see the outcome as a forgone conclusion. Make this purchase and the result is yours, because it has happened to so many other people after buying.

Use Blogs and Social Media:

One of the simplest forms of social proof you will find on Blogs are comments. Comments are indicators that enough people are paying attention to what you are writing to reply. The same applies to the facebook “like” and twitter “tweet” buttons and before them, the Digg and Stumble buttons, and more recently Pinterest “Pin” button.

Gather your social proof:

Chances are you already have built up social proof. You may have received positive feedback emails from customers who have benefited from what you sold them – can they be your next case study? Do you have a lot of comments on a particular Blog post, or can you add a comment function to something to start building a social proof resource? Facebook comments? Pinterest followers? Etc etc.

If this isn’t something you have focused on, it’s time to start! You can almost instantly increase conversions when you effectively implement Social Proof.

By the way, you can use Social Proof on your site, landing pages, Blog posts, ad campaigns, social media marketing and more.

 

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Turning big data into better health outcomes

Turning big data into better health outcomes | Analytics & Social media impact on Healthcare | Scoop.it

Population health management is a multifaceted, many-layered endeavor that nevertheless has a common theme: the need for data and the ability to mine it for actionable information.

 

A broad spectrum of health care players -- individual providers, hospital systems, payers, local public health departments and federal agencies -- are all in some way addressing population health management. The approach involves identifying populations, assessing their disease status and developing appropriate responses, such as management programs for chronic diseases. Those activities require access to data -- and plenty of it.

 

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JAMA report looks at what drives healthcare analytics | Vital Signs | The healthcare business blog from Modern Healthcare

JAMA report looks at what drives healthcare analytics | Vital Signs | The healthcare business blog from Modern Healthcare | Analytics & Social media impact on Healthcare | Scoop.it

The big hope for proponents of computer-enabled predictive analytics in healthcare is to one day see it in widespread use, at the point of care, in actionable form, to aid in real-time clinical decisionmaking.

But broad use implementation of eHPA is still in its infancy, say the authors of “Implementing Electronic Health Care Predictive Analytics: Considerations and Challenges. ”

Their piece is one of a series of articles on various permutations of Big Data in the current issue of the healthcare policy journal Health Affairs.

“The term infancy is relative,” says the article's co-author Bin Xie, health services research manager with PCCI, a Dallas-based not-for-profit corporation spun out of the healthcare data analytics work done at Parkland Health & Hospital System. 

The decades-old Framingham risk model for cardiovascular events and the APACHE II scoring systems to gauge the acuity of ICU patients are both well known examples of predictive analytics systems, the authors point out. 

But very few risk prediction models targeting hospital readmissions had been incorporated into an electronic health record system for easy use and reference, according to a 2011 survey report, published in the Journal of the American Medical Association and cited in the Health Affairs article.

“There are already many implementations across many hospitals in the country and across the world,” Xie adds in an interview. “It could grow into a big, giant adult, so, when we compare it to its potential, it's still in its infancy.”

“We think in five to 10 years, it could really become a big thing in healthcare, especially when we address the difficulty of containing costs and improving the quality of care and the challenge of the growth in the number of senior citizens,” he said.

Just as government penalties for hospital readmissions captured the attention of many early implementers of eHPA efforts, “payment reform is one essential piece to drive this growth” in the future, Xie said.

Predictive analytics has four component parts, according to the authors—acquiring data, validating the risk-prediction model, applying it in a real-world setting and scaling up the model for broader use in a healthcare system. Their article focused on the latter two and the challenges of bringing them to fruition.

Among those challenges are setting up an appropriate oversight mechanism with the right balance between enough control to keep the program operating properly and also affording it enough breathing room to grow and respond to daily events, the authors said. Another is stakeholder engagement, which includes patient consent, particularly when the risk models are still in the early stages of development. 

“The first time you go out and experiment, you do need a rigorous framework of the patient's right to know, just as you do in research study,” Xie said. 

Other issues that data analytics program planners must address are data quality assurance, patient privacy protections, interoperability of the technology platform and transparency of the risk model. 

“Whenever possible, clinicians, in particular, need to be able to 'see into' a risk-prediction model and understand how it arrived at a certain prediction,” the authors advise.  Transparency builds needed trust in the model, and it might encourage “crowd sourcing” to improve the model or expand its use to other organizations or settings.

Follow Joseph Conn on Twitter: @MHJConn

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FDA Looks to Urge Companies to Tweet Drug Risks

FDA Looks to Urge Companies to Tweet Drug Risks | Analytics & Social media impact on Healthcare | Scoop.it

e FDA is looking into a new way to regulate drugs and medical devices—by using social media. The agency has drafted social media guidelines that would urge drug companies to use platforms such as Facebook, Twitter, and YouTube, to educate the public about the risks of their prescription drug or medical device.

The draft guidelines, which are currently under review by the agency, propose that companies be required to use the “character space constraints” on social media platforms such as Twitter to tweet the risks, along with the benefits, of a product. The guidelines also recommend that manufacturers include a link that takes readers to more information about the product. In the case of Twitter, that information should all be included in a single tweet.

The document states:

If a firm concludes that adequate benefit and risk information, as well as other required information, cannot all be communicated within the same character-space-limited communication, then the firm should reconsider using that platform for the intended promotional message.

If approved, the guidelines will become the first formal recommendation by the agency regarding manufacturers’ use of social media.

Lara can be reached at lara.salahi@globe.com. Follow Lara on Twitter: @BostonLara
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Preparing for the digital health revolution

Preparing for the digital health revolution | Analytics & Social media impact on Healthcare | Scoop.it

The convergence of life sciences and technology has the potential to reshape consumption of healthcare services, transform government policy and minimise disease risk by enabling health-focused lifestyle choices.

The healthcare industry has long understood that a ‘one-pill-fits-all’ approach is an inadequate way to treat billions of individual patients across a multitude of genetic, cultural and environmental backgrounds. It has therefore attempted to work towards an idealised vision of personalised medicine, whereby the right drug is delivered to the right patient, at the right time, in the right dose. However, the ability to derive and collate the individualised data necessary to deliver on this vision has, to date, been a limiting factor.

The advent of personal genomics provided a significant step towards enabling personalised medicine, but it is innovation in wearable and implantable devices capable of measuring a multitude of personal biochemical and physiological parameters that will deliver on genomics’ promise.

Wearable technologies such as the Samsung Gear and the Apple Watch are marketed largely as lifestyle products, with apps aimed at measuring and improving health and sport-related activities. However, these products are also part of a new category of medical devices capable of monitoring all the vital bodily functions in real time and transmitting this data via wireless networks and mobile phone technology to a cloud storage facility for diagnostic purposes.

Devices are now available that can, in a non-hospital environment, measure and transmit all of the same categories of health markers that would currently be measured in an intensive-care facility. These products include earphones that can check blood oxygenation levels, edible microchips that can capture food intake and rest patterns, and even nanotechnology-enabled sensors, or ‘nanobots’, which can be injected into the bloodstream; there, they wirelessly transmit data regarding the presence of markers that can help to predict diseases such as cancer or heart failure.

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The benefits of harnessing device technology in this way are beginning to influence the collaborative and acquisition strategies of the major pharmaceutical companies, which are looking at complementary diagnostic partners that have the technology to transform their therapeutic innovations. These companies’ ultimate commercial goal is to deliver integrated diagnostic and therapeutic products under a single brand.

The connectivity of wearable devices is relevant not only to the uploading of data for diagnostic purposes, but also to delivering information to the user to improve their use of healthcare services and products. A vast amount of public money is wasted as a result of patient non-compliance with medication regimens. The impact of medication-reminder apps, for example, which prompt patients to take their pills and connect to a pharmacy to refill prescriptions, could have a sizeable impact on reimbursement arrangements and the strategies of pharmaceutical companies.

Among the health apps in the NHS library is Sleepio, a program that delivers personalised cognitive behavioural therapy for insomniacs, which won the Wired Health Bupa Startup competition.

At a recent event in Berlin, where healthcare startups pitched their ideas to the industry and prospective investors, innovations included web applications that allow patients to obtain early-stage medical advice, help them perform exercises tailored by doctors, and support doctors and labs in managing their workflows and data.

In developing countries, the ability of smartphones to capture patient data could also have a powerful impact on national vaccination programmes. Tracking each vaccinated individual via a barcode scan can facilitate an understanding of the stage at which sufficient individuals have been vaccinated, which can help to create effective immunity for the whole population.

Detecting, reporting and collating vast amounts of individuals’ biological and physiological data also provides great opportunities for the biopharmaceutical industry to improve the efficiency of its clinical trial and post-approval surveillance procedures. Many therapeutically effective drugs are not presently available because of serious negative side effects in a relatively small number of patients. The use of devices to constantly monitor for markers of the onset of such side effects will change the entire regulatory environment - and should ensure that many more important drugs reach and remain in the market.

Data privacy

The ability to sync wearable technology to a smartphone will allow people to create a hub of personal healthcare data that they, their GP and even their personal trainer can access.

Here, the issues of data protection and purpose become acute - individuals will want to use the data for their personal benefit, but what are the legal and ethical parameters guiding what employers or insurers can do with it? What happens when the line is crossed between information about wellbeing and information concerning matters of life and death?

This concern - of private medical records becoming a commercial proposition - goes to the heart of the debate over the UK’s NHS data programme to share and link patient data in order to improve research and “transform health services”. In February 2014, the scheme was delayed by six months in order to “ensure stronger safeguards around the uses of the data [and] clarity about the rights people have to opt out”.

Such gathering of data raises fundamental questions about privacy and ethics. The price of genetic information has fallen dramatically - from $10m (£6.27m) per genome in 2001 to close to $1,000 (£627.76) - but the much publicised shutdown of 23andMe’s home DNA-testing service by the US Food and Drug Administration demonstrates the difficulty of regulating consumer-driven medical research.

It is little wonder that legislators do not have the answers. The convergence between technology, life sciences and data is fast-moving, making categorisation and enforcement difficult. The world of wearable technology is not yet regulated. And with very little precedent, legal and commercial departments need experts with degrees in computing and biology. Engineering in the life sciences field is not new, but the internet has brought discovery much closer to the individual - and, with it, the need for doctors (and lawyers) who understand data algorithms.

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Twitter Partners With IBM To Bring Social Data To The Enterprise | TechCrunch

Twitter Partners With IBM To Bring Social Data To The Enterprise | TechCrunch | Analytics & Social media impact on Healthcare | Scoop.it

witter and IBM announced a significant partnership today that will involve Twitter sharing its data with IBM for integration into IBM’s enterprise solutions, including the Watson cloud platform. The deal means IBM will gain access to the Twitter “firehose,” allowing businesses to incorporate insights gained from the social network into their decision-making processes.

Additionally, the two companies will also be teaming up to build “a unique collection of enterprise solutions,” they say, which puts IBM into a different category than some of Twitter’s other data partners, who generally just ingest the data for use in their own systems.

IBM says the companies will collaborate to build enterprise applications to improve business decisions across industries and professions, beginning with applications and services for sales, marketing and customer service. They will also work together on industry-specific solutions, including those for banking, consumer products, transportation and retail.

Developers will also be able to integrate Twitter data into their own cloud applications, thanks to this agreement, using either IBM’s Watson Developer Cloud or its BlueMix development platform.

Going forward, the integrations will allow IBM customers to ask more complex questions about their businesses, like “why are we growing quickly in Brazil?” for example, a Twitter blog post suggests, and then use Twitter data to help them inform those responses.

The announcement, which Twitter says has been “years in the making,” comes at a time when IBM has been ramping up its efforts to become a player in the analytics space. The company has been rolling out a number of products recently, including Watson Analytics, a cloud application designed for working with big data which bears the name of the Watson supercomputer best known for its stint as a Jeopardy contestant years ago.

IBM has since taken that technology and turned it into a cloud platform that they’re building their own solutions on top of, while also inviting others to do the same.

IBM wants to provide business customers with as many data sources as they can, so it makes sense that they would include Twitter’s data stream in their analytics products.

Meanwhile, for Twitter, the company is now able to benefit more from its firehose of data, while also establishing its value to enterprise customers beyond the usual marketing and social media monitoring kinds of use cases. That matters even more these days as Twitter is struggling to find user growth, even attempting to invent new metrics that can track Twitter’s true reach to its “logged-out” and “syndicated” audiences, and beyond. (During the company’s earnings this week, it reported that user growth slowed to 4.8 percent and timeline views per user fell 7 percent.)

Twitter’s user growth may be slowing, but its move into the enterprise space here is surely not without some financial impact to the company’s bottom line.

Twitter also notes today that its new relationship with IBM was made possible by its acquisition of Gnip earlier this year, as it provided the enterprise-grade platform capable of delivering its 15 billion “social activities” created per day to Twitter partners, now including IBM.

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Where Will Healthcare's Data Scientists Find The Rich Phenotypic Data They Need?

Where Will Healthcare's Data Scientists Find The Rich Phenotypic Data They Need? | Analytics & Social media impact on Healthcare | Scoop.it

The big hairy audacious goal of most every data scientist I know in healthcare is what you might call the Integrated Medical Record, or IMR, a dataset that combines detailed genetic data and rich phenotypic information, including both clinical and “real-world” (or, perhaps, “dynamic”) phenotypic data (the sort you might get from wearables).

In my last post, I noted that, as Craig Venter told Congress this summer, the combination of next-generation sequencing (to generate raw data) and cloud computing (to efficiently process the information) begins to address the genomic data component of an IMR (disclosure: I work at DNAnexus, a genomics data company).  The question is: where will the dense phenotypic data come from?

The gold standard for clinical phenotyping are academic clinical studies (like ALLHAT and the Dallas Heart Study).  These studies are typically focused on a disease category (e.g. cardiovascular), and the clinical phenotyping on these subjects – at least around the areas of scientific interest — is generally superb.  The studies themselves can be enormous, are often multi-institutional, and typically create a database that’s independent of the hospital’s medical record.

Inevitably, large, prospective studies can take many years to complete.  In addition, there’s generally not much real world/dynamic measurement.

The other obvious source for phenotypic data is the electronic medical record (EMR).  The logic is simple: every patient has a medical record, and increasingly, especially in hospital systems, this is electronic – i.e. an EMR.  EMRs (examples include Epic and CernerCerner) generally contain lab values, test reports, provider notes, and medication and problem lists.  In theory, this should offer a broad, rich, and immediately available source of data for medical discovery.

I discussed some of the problems with EMRs in my last post – the recorded information is of variable quality, often incomplete, generally doesn’t include real-world phenotype (though this may be changing), and is typically extremely difficult to extract.  Interoperability remains a huge problem, meaning that even two hospitals that might be running the same brand of EMR can have trouble exchanging information.  Consequently, pooling EMR information at scale from multiple hospitals remains a significant problem, despite the obvious utility of being able to do this for cancer and other diseases.  (It’s also fair to say that many of the central challenges around data sharing by academic physicians are not attributable to problems with the EMR.)

DIY (enabled by companies such as PatientsLikeMe) represents another approach to phenotyping, and allows patients to share data with other members of the community.  The obvious advantages here include the breadth and richness of data associated with what can be an unfiltered patient perspective – to say nothing of the benefit of patient empowerment.  An important limitation is that the quality and consistency of the data is obviously highly dependent upon the individuals posting the information.

Pharma clinical trials would seem to represent another useful opportunity for phenotyping, given the focus on specific conditions and the rigorous attention to process and detail characteristic of pharmaceutical studies.  However, pharma studies tend to be extremely focused, and companies are typically reluctant to expand protocols to pursue exploratory endpoints if there’s any chance this will diminish recruitment or adversely impact the development of the drug.

Given these complexities, it’s not surprising that some researchers have decided the best path forward is to start from scratch.  This is essentially the premise of Google’s Baseline Study, which aims to track a cohort of healthy patients, obtaining rich clinical phenotypic data at the outset (like a traditional academic clinical study), but then supplementing this with dynamic information from a range of wearable devices.  (Disclosure: GoogleGoogle Ventures is an investor in DNAnexus.)

Like prospective academic studies, Baseline will presumably take a while to yield results.  In addition, they will ultimately need to enroll a very large number of volunteers — far more than the 175 or so they’re starting out with.   Finally, while the reliance on objective measurement is understandable, it’s not clear to me whether there’s a component of traditional clinical assessment as well.

Over time, I suspect, the distinction between some of these approaches may disappear as wearable data increasingly become part of the EMR, and are obtained more regularly in prospective academic studies, and eventually, in pharma studies, like this recently announced Boehringer Ingelheim/Propeller Health pilot.  There also seems to be an increased focus on patient experience, as I discussed here.

At the same time, I’m especially sympathetic to the idea of creating a more perfect data union from the ground up.  I think the real question to ask is whether the right place to start is a novel clinical study, like Baseline — or a novel clinical system?

Addendum:

Readers might enjoy previous post, “Next Hurdle For Medical Research: Capture and Integration Of Phenotype At Scale“

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It's time for the next wave of healthcare analytics startups

It's time for the next wave of healthcare analytics startups | Analytics & Social media impact on Healthcare | Scoop.it

“Big data” and analytics applied to healthcare is a hot area of investment. This year alone, roughly $200 million of venture capital dollars has been allocated to the space (according to Rock Health). I’m here to tell you that these glory days are gone.

New startups are finding it difficult to differentiate from the swath of other companies unless they have something extremely novel.

While I still believe in the transformative power of data in healthcare (I’ve had two successful exits with analytics companies: RxAnte and Humedica), it’s increasingly clear to me that the current space is beginning to commoditize:

A lot of companies are chasing the same targets. Large providers and insurers are increasingly finding it hard to choose amongst vendors — and as a result, pricing for pure analytics is decreasing.Most platforms provide extremely useful clinical insights, but rely on an end user that they don’t control to act. Hence these companies have no accountability for reducing the cost of healthcare and improving outcomes.Providers and payers are bombarded by different vendors every day. Some offer very elegant data visualization but not necessarily “new” or “better” data, and the underlying issues of accountability and pricing pressure remain.

That’s the bad news. Here’s the good news: There is still plenty of opportunity for startups in this space!

To hear more about the opportunities for startups and investors in healthcare, join VentureBeat at HealthBeat 2014, Oct. 27-28 in San Francisco,

where Mo Kaushal will be diving deeper into the topic.

Evolving policies around payment model reform (in which providers will be paid based on outcomes, not just services rendered) and meaningful use bonus payments for electronic medical record (EMR) adoption demonstrate that the healthcare industry is in the early days of transformation. Ongoing technology innovation, macroeconomics, and policy reactions will continue to accelerate the shift.

This creates a world of opportunity of the healthcare analytics entrepreneur. Despite my concerns over traditional healthcare data analytics companies, there are a few areas I believe we need to accelerate.

Here are some of the criteria I believe will be required in the next generation of healthcare analytics companies:

1. New business models.  For example, companies that are building out a service around a core piece of technology and that can deliver this service much more cost-effectively than any other incumbent competitor.

Navihealth is a great example of this. Navihealth’s core analytics uses patient function to predict the ideal setting upon discharge, coupled with a service model that helps optimize individual care in each post-acute facility. In other words, people in conjunction with the right technology targeting an at-risk business model has helped create a very unique value proposition to end customers.

2. Advanced, proprietary technologies. Beyond data analytics, next-generation artificial intelligence platforms will drive the next wave of innovation. New platforms must be able to ingest multi-source data and reveal novel insights that are actionable and not commoditized. These solutions will displace many current platforms as the data output becomes more valuable.

Vicarious is attacking the market for artificial intelligence by building a unified algorithmic architecture. Along the way, Vicarious has also secured investment from many of the biggest names in tech, including Mark Zuckerberg, Peter Thiel, Jeff Bezos, Jerry Yang, and Marc Benioff (to name a few). I’m also aware of some interesting examples of companies doing this in healthcare, but to my knowledge none of them has emerged from stealth — so watch this space!

3. High-value data sets that can’t be replicated. Companies that can provide proprietary data sets that can’t otherwise be easily obtained are increasingly setting a high bar for entry for new healthcare analytics startups.

This is one of the key reasons Optum acquired Humedica. Humedica is able to extract, standardize, and analyze millions of fully integrated clinical data versus just claims information.

As an investor, my dollars for healthcare analytics companies have already been invested, and it’s time for the next wave of innovation. For my money, healthcare entrepreneurs must focus on unique niches where little competition exists, and they must address those markets using differentiated technologies, data sets, and business models that target large problems.

Where do you see healthcare investment potential?

Mohit (Mo) Kaushal is a partner at Aberdare Ventures. He’s an MD MBA with extensive experience within clinical medicine, venture capital, and health policy. Prior to Aberdare, he was Chief Strategy Officer and EVP of Business Development at West Health, where he developed the West Health Investment Fund strategy and sourced and led investments. Prior to that he was the Director of Connected Health with the FCC, where he established the agency’s first dedicated health care team. He was also a member of the White House Health IT task force, a cross agency team focusing on implementing the technology aspects of Health Reform.

HealthBeat — VentureBeat’s breakthrough health tech event — is returning on Oct 27-28 in San Francisco. This year’s theme is “The connected age: Integrating data, big & small.” We’re putting long-established giants of the health care world on stage with CEOs of the nation's most disruptive health tech companies to share insights, analyze trends, and showcase breakthrough products. Purchase one of the first 50 tickets and save $400!
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Now comes Moneyball for health IT | Healthcare IT News

Now comes Moneyball for health IT | Healthcare IT News | Analytics & Social media impact on Healthcare | Scoop.it

An overarching theme from a vendor's first analytics conference is similar to something that that EHR vendors have been saying for years: workflow and organizational culture are at least as important as the technology itself when it comes to healing healthcare through IT.
"It's a whole cultural change from a clinician's point of view," Bryan Bohman, MD, associate CMO of Stanford Hospital and Clinics, as well as CMO of the Stanford-affiliated University HealthCare Alliance, said at analytics company Health Catalyst's inaugural Healthcare Analytics Summit last week.
"You have to understand that it's a process, that medicine is a team sport," Bohman continued. "That's a bridge too far for many physicians today," especially those trained to believe that their judgment should be the last word.
Physicians, of course, will fight any perceived incursion on their turf, but Bohman suggested framing workflow changes as a move to improve their quality of life. "It's nice to know that you're Superman, but it's very stressful," the practicing anesthesiologist said.
With this in mind, Health Catalyst invited Oakland Athletics General Manager Billy Beane — he of "Moneyball" fame — to keynote the opening session. (Beane's speakers' bureau effectively gagged the media on his speech, but other presenters referred often to the 2011 movie starring Brad Pitt.)
To get buy-in for analytics — or any project requiring cultural change — "invite people who aren't normally there,"  said Gene Thomas, CIO of Gulfport (Miss.) Memorial Hospital.
In the Hollywood movie, Pitt's Beane did this by having Jonah Hill's "Peter Brand" character loosely based on ex-Athletics Assistant GM Paul DePodesta, sit in on a meeting with skeptical, old-school baseball scouts to explain how Beane was employing analytics to assess the value of players. Thomas did this with clinical leaders prior to deploying a Health Catalyst enterprise data warehouse in conjunction with the rollout of a new Cerner EHR earlier this year.
In return, Thomas knew he had an important job to do for the clinical staff: Make analytics information relevant and useful. "Data has to be high-integrity, projectable and real-time," he said.
At the event, Health Catalyst released a document called the "Accountable Care Transformation Framework." It mentioned "workflow no less than 30 times.
And Health Catalyst CEO Dan Burton stated what many of the more than 500 people in attendance were thinking: Analytics can become the foundation for health transformation. "Let us resolve to act, to be true catalysts," he said.

The message seemed to resonate. In an informal, real-time poll of about 150 people in a breakout session about making analytics a strategic imperative, a slim majority said that their organization's analytics vendor is more important than its EHR vendor.
A possible reason? "Availability of data has never been a problem at Geisinger," said Geisinger Health System CEO Glenn D. Steele Jr., MD. The big issue there, and at so many other provider organizations, is usability of data.
Crystal Run Healthcare, a physician-owned multispecialty practice based in Middletown, N.Y., has had a NextGen Healthcare Information Systems EHR since 1999. Late last year, the 300-physician group installed its first enterprise data warehouse, bringing business intelligence not only to the billing department, but also to human resources and materials management. EHR data warehouses do not always do that, noted CMO and CMIO Gregory Spencer, MD.
"BI is moving more from IT to analysis," Spencer said, "and from financial reporting to clinical guidance." It is his job to figure out how and what to measure and to display the results in way that will be useful to his fellow physicians. And the job will only get more difficult, as Spencer expects to start integrating data from social media and genomics in the next 3-5 years.

[See also: Health Catalyst shows Midas touch.]
Mission Health System in Asheville, N.C., is in a similar position. "We are shifting our culture to use information to drive change, even without an EDW," said CIO Jon Brown. "The organization is finally ready for an EDW."

It took a while to get to this point, despite strong clinician and administrator demand for data in recent years. "Data became cool at Mission Health," said CIO Jon Brown. Everyone asked for it but nobody did much with it, so nothing changed.
As part of a nascent three-year plan, Mission Health launched an accountable care organization in July, and Brown hopes to have it operational in the next couple of months. The goal is to begin actively managing population health by January 2016 and to break even on Medicare patients in a rapidly aging community that is seeing an influx of retirees moving in from overcrowded Florida, Brown said.

[See also: Partners and Health Catalyst join forces.]
"Stay focused on the vision," Brown said, explaining that with data and analytics, more is not always better, and that fellow CIOs would do well not to get bogged down in producing reports for the sake of producing reports. "Data has got to drive change," he said

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NHS could save billions a year through data analytics - report | Information Age

NHS could save billions a year through data analytics - report | Information Age | Analytics & Social media impact on Healthcare | Scoop.it

A new report –  'Sustaining Universal Healthcare: Making Better Use of Information' – released today by Volterra Partners and EMC has outlined how data analytics and better use of information can improve the efficiency of healthcare delivery in the UK by up to 60%, with the potential to save the NHS between £16.5 billion and £66 billion per year.

The NHS is currently facing a £34 billion funding gap by 2020 and radical action is needed to preserve its core values of 'providing free healthcare for everyone'. The report recommends the use of information sharing and collaboration to deliver a proactive, personalised Wellness Model structure, focused on prevention.

'There has been considerable speculation that the NHS, as a universal healthcare institution, cannot continue in its current form,'  Nick Bosanquet, Emeritus Professor of Health Policy at Imperial College and economist at Volterra Partners, commented.

'We need drastic change to cope with the increasing demands from an ageing population, chronic health conditions and emergency readmissions. The report estimates that with better informatics, cancer admissions could be reduced by 30%. This example and other predicted savings calculated in the study offer a clear path to delivering the Wellness Model.'

> See also: The NHS is sick- big data could be the cure

The report exposes the gap between the NHS and other industries in its use of data analytics and technology. The lack of electronic records, predictive analytics, collaboration and effective monitoring of patient and treatment outcomes, in addition to personalised care, is leading to failures and financial inefficiencies that are unsustainable in the long-term.

There are pockets of excellence across the UK where data analytics has been effectively employed to deliver better quality of care for patients. If these examples were implemented nationally this would result in savings of £840 million per year due to a reduction in A&E attendances; £200 million per year through reduced complications due to diabetes; £126 million per year through better care management for patients with Chronic Obstructive Pulmonary Disease (COPD); Up to £32 million per year through the reduction of readmission rates and £5 billion of savings in staff time through more efficient working practices.

> See also: Medway Hospital seeks big savings as software connects it to NHS spine

Scotland has used informatics technology to provide an integrated care model for the treatment of diabetes. This collaborative data-driven project has yielded impressive results with the incidence of lower extremity amputation decreasing by 30% over four years and major amputations falling by 40.7%. The report estimates that if the same system was implemented in England, it could result in 1,775 fewer amputations, saving the NHS £37 million per year.

> See also: Monitoring the health of NHS IT

The report identifies a number of recommendations to enhance patient care, including speeding up the accessibility of data and communicating the benefits to patients and GPs ahead of time to build trust and buy in, collaboration at a local level with health institutions and academia, investment in appropriate skills in the health workforce to handle and use data effectively, and a change in culture within the Department of Health to drive a real shift to the Wellness Model, rather than just using data to improve performance management

- See more at: http://www.information-age.com/it-management/strategy-and-innovation/123458475/nhs-could-save-billions-year-through-data-analytics-report#sthash.N9UV79XX.dpuf

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Analyst: Apple's financial impact on mHealth worth billions

Analyst: Apple's financial impact on mHealth worth billions | Analytics & Social media impact on Healthcare | Scoop.it

Apple's foray into mHealth, given its reported upcoming iWatch device, its moves into electronic health record technology and the development of its HealthKit platform, will have a dramatic impact on healthcare and advance mHealth like few other initiatives, according to a report at Product Design & Development.


Via Alex Butler
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IBM Sees Broader Role for Watson in Aiding Research

IBM Sees Broader Role for Watson in Aiding Research | Analytics & Social media impact on Healthcare | Scoop.it

Watson, the IBM system that won “Jeopardy,” has shown promise in answering some kinds of questions. Now the company sees a broader role, a bit like the deductions that helped its namesake’s famous partner solve fictional crimes.

The company on Thursday is announcing advances in the technology and the availability of what it calls IBM’s Watson Discovery Advisor, a cloud service that it says can help research teams analyze vast troves of data to come up with new research ideas.

IBM is also pointing to a peer-reviewed case study to back up its claims. It describes how a tool based on Watson–developed at Baylor College of Medicine in Houston–was able to sort through about 70,000 scientific papers for relevant data about a particular protein and generate hypotheses that could be tested by scientists.

 

Watson is a collection of algorithms and software that runs on IBM’s Power line of servers, available for customers to use from its own data centers. Its components are designed to derive meaning more human language, and learn from data and other observations as opposed to being explicitly programmed to carry out instructions.

The company hasn’t generated a lot of measurable revenue from Watson so far, but it is betting that the extension of the technology from answering questions to generating hypotheses should help.

“Discovery is a lot more subtle,” said John Gordon, vice president in IBM’s Watson group. “You are trying to find connections.”

In the study, biologists and data scientists using the technology were able to identify proteins that modify p53, a protein related to many cancers. But the broader point was to show the potential of letting computers analyze data and make useful suggestions about it, amid a flood of research being generated by companies and other institutions.

“The literature is immense,” said Olivier Lichtarge, a professor of molecular and human genetics at Baylor who was the principal investigator on the study. “It is very difficult for any researcher to thoroughly master.”

Gordon said a related motivation is that many research dead-ends generate data that ends up on the cutting room floor. With the aid of Watson, companies could better mine that private information and combine it with scientific data in the public domain.

One company studying such possibilities to evaluate medications and treatments is Johnson & Johnson, IBM said.

But the company sees applications beyond the health realm, including making automated suggestions based on financial, legal, energy and intelligence-related information, IBM said.

 

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How transparency of medical records can lead to enhanced outcomes

How transparency of medical records can lead to enhanced outcomes | Analytics & Social media impact on Healthcare | Scoop.it

The woman was sitting on a gurney in the emergency room, and I was facing her, typing. I had just written about her abdominal pain when she posed a question I'd never been asked before: "May I take a look at what you're writing?"

At the time, I was a fourth-year medical resident in Boston. In our ER, doctors routinely typed visit notes, placed orders and checked past records while we were in patients' rooms. To maintain at least some eye contact, we faced our patients, with the computer between us.

But there was no reason why we couldn't be on the same side of the computer screen. I sat down next to her and showed her what I was typing. She began pointing out changes. She'd said that her pain had started three weeks ago, not last week. Her chart mentioned alcohol abuse in the past; she admitted that she was under a lot of stress and had returned to heavy drinking a couple of months ago.

As we talked, her diagnosis — inflammation of the pancreas from alcohol use — became clear, and I wondered why I'd never shown patients their records before. In medical school, we learn that medical records exist so that doctors can communicate with other doctors. No one told us about the benefits they could bring when shared with patients.

In fact, before the Health Insurance Portability and Accountability Act, a federal law enacted in 1996, patients generally had to sue to see their records. HIPAA, as that mouthful is abbreviated, affirmed that patients have a right to their medical information. But the process for obtaining records was often so cumbersome that few patients tried to access them.

In 2010, Tom Delbanco, an internist, and Jan Walker, a nurse and researcher, started anexperiment called OpenNotes that let patients read what their primary care providers write about them. They hypothesized that giving patients access to notes would allow them to become more engaged in their care.

Many doctors resisted the idea. Wouldn't open medical records inhibit what they wrote about sensitive issues, such as substance abuse? What if patients misunderstood the notes? Would that lead to more lawsuits? And what would patients do with all the information anyway?

After the first year, the results were striking: 80 percent of patients who saw their records reported better understanding of their medical condition and said they were in better control of their health. Two-thirds reported that they were better at sticking with their prescriptions. Ninety-nine percent of the patients wanted OpenNotes to continue, and no doctor withdrew from the pilot. Instead, they shared anecdotes like mine. When patients see their records, there's more trust and more accuracy.

That day in the Boston ER was a turning point for me. Since I started sharing notes with my patients, they have made dozens of valuable corrections and changes, such as adding medication allergies and telling me when a previous medical problem has been resolved. We come up with treatment plans together. And when patients leave, they receive a copy of my detailed instructions. The medical record becomes a collaborative tool for patients, not just a record of what we doctors do to patients.

The OpenNotes experiment has become something of a movement, spreading to hospitals, health systems and doctors' offices across the country. The Mayo Clinic, Geisinger Health System and Veterans Affairs are among the adopters so far. (The OpenNotes project has received funding from the Robert Wood Johnson Foundation, which also provides financial support to NPR.)

But there are new controversies arising. Should patients receiving mental health servicesobtain full access to therapy records, or should there be limits to open records? What happens if patients want to share their records on social media? Will such "crowdsourcing" harm the doctor-patient relationship? What if patients want to develop their own record andvideotape their medical encounter? Are doctors obligated to comply?

Delbanco tells me that he considers OpenNotes to be "like a new medication." Just like any new treatment, it will come with unexpected side effects. In the meantime, patients and doctors don't need to wait for the formal OpenNotes program to come to town. Patients can ask their doctors directly to look at their records. Doctors can try sharing them with patients, in real time, as I do now. It's changed my practice, and fundamentally transformed my understanding of whom the medical record ultimately belongs to: the patient.

Wen is an attending physician and director of patient-centered care research in the Department of Emergency Medicine at George Washington University. She is the author of"When Doctors Don't Listen: How to Avoid Misdiagnoses and Unnecessary Care," and founder of Who's My Doctor, a project to encourage transparency in medicine. On Twitter:

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Why caregivers and their loved ones should not be hesitant but straightforward about what they want to know from doctors at  medical appointments . . .

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Laura Kolodjeski on how Sanofi US created a patient-centered blog in a highly regulated industry

Laura Kolodjeski on how Sanofi US created a patient-centered blog in a highly regulated industry | Analytics & Social media impact on Healthcare | Scoop.it

This post features a case study by Director of Patient Insights, Laura Kolodjeski at our Member Meeting in Boston where she shared insights from Sanofi US’ content hub created for the diabetes community. Laura’s been a SocialMedia.org member since 2010.

You don’t need us to tell you that leading social media in a heavily regulated industry is difficult.

You can see it in Laura Kolodjeski’s opening slide in her presentation: A legal disclaimer saying the views of the presenter do not necessarily represent those of Sanofi US.

“I did say I worked in a regulated industry,” Laura laughs.

But despite the restrictions on social in the pharma industry, Sanofi US has a thriving diabetes community online.

In her case study presentation, Laura describes how their content hub, The Diabetes Experience, or “The DX,” has helped write the rules for pharma in social media. She says it all started when Sanofi US decided to make the center of their social strategy their patients — not their products.

“We’re publishing content by the people, for the people. It’s not to market our products, but to enhance our overall value to our customers,” Laura says.

What does that look like? A blog full of original and curated content on anything from nutrition and fitness to relationships and lifestyle.

“It had never been done before for pharma, because anything we publish has a very stringent review process and content has to be vetted,” Laura explains. “It took a lot of conversations with our stakeholders to make sure they understood the vision, the purpose, and why we were asking to do something like this.”

Laura says they’re extremely proactive about finding the right content.

“We created The DX to offer a place for the community to engage around life and the aspect of that life with diabetes. We did not want it to be about diabetes, and we certainly did not want it to be about diabetes treatments,” she says.

To curate that content, they take a creative approach to listening. Laura says search data and unmet queries tell them a lot about what information patients are looking for. Laura’s team also keeps in close touch with patient advocates and online communities to understand more about caregivers and healthcare providers.

“And what we don’t hear through social listening, we ask.”

For The DX’s original content, Sanofi US looks to big influencers in the diabetes community like journalists, bloggers, and dieticians. They help create expert content from several areas of diabetes as well as connect Sanofi US to their own social followers.

Laura says, “It’s an amazing way to build relationships with your key influencers. As they’re writing for you, you’re building a very personal connection over time.”

Laura says her team’s favorite question is “Why?”

She explains how they’ve been able to push into new territory in social media and do groundbreaking work because they’re willing to ask questions when they’re told “no.”

“We respect our other stakeholders. They’re there to assess risk and ensure that we’re compliant,” Laura says, “But if you keep asking why, ultimately, you might get to a point where you can compromise or shape the conversation.”

Say hi to Laura on Twitter or check out the video of her full Member Meeting presentation here. 

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Lessons Learned: Bringing Big Data Analytics To Health Care

Lessons Learned: Bringing Big Data Analytics To Health Care | Analytics & Social media impact on Healthcare | Scoop.it

Big data offers breakthrough possibilities for new research and discoveries, better patient care, and greater efficiency in health and health care, as detailed in the July issue of Health Affairs. As with any new tool or technique, there is a learning curve.

Over the last few years, we, along with our colleagues at Booz Allen, have worked on over 30 big data projects with federal health agencies and other departments, including the National Institutes of Health (NIH), Centers for Disease Control (CDC), Federal Drug Administration (FDA), and the Veterans Administration (VA), along with private sector health organizations such as hospitals and delivery systems and pharmaceutical manufacturers.

While many of the lessons learned from these projects may be obvious, such as the need for disciplined project management, we also have seen organizations struggle with pitfalls and roadblocks that were unexpected in taking full advantage of big data’s potential.

Based on these experiences, here are some guidelines:

Acquire the “right” data for the project, even if it might be difficult to obtain.

We’ve found that many organizations, eager to get started on a big data project, often quickly gather and use the data that is the easiest to obtain, without considering whether it really goes to the heart of the specific health care problem they’re investigating. While this can speed up a project, the analytic results are likely to have only limited value.

For example, we worked with a federal agency experimenting with big data analytics to identify cases of perceived fraud, waste, or abuse. The program’s analysts focused on data they already had on hand and currently used to direct audit and investigation activity. We encouraged project staff to identify alternative data sources that might reveal important information about compliance history or “hotspots” for illegitimate activity.

We learned that historical case reports and online provider marketing materials were available and were a potentially valuable source for information to aid in fraud detection. However, the project analysts had decided it would take too long to incorporate that information and so had excluded it.

Many organizations – both inside and outside of health care – tend to stick with the data that’s easily accessible and that they’re comfortable with, even if it provides only a partial picture and doesn’t successfully unlock the value big data analytics may offer. But we have found that when organizations develop a “weighted data wish list” and allocate their resources towards acquiring high-impact data sources as well as easy-to-acquire sources, they discover greater returns on their big data investment.

Ensure that initial pilots have wide applicability.

Health organizations will get the most from big data when everyone sees the value and participates. Too often, though, initial analytics projects may be so self-contained that it is hard to see how any of the results might apply elsewhere in the organization.

We ran into this challenge when we helped a federal health agency experiment with big data analytics. The agency’s initial set of pilots focused on specific, computationally complex and storage-intensive challenges, such as reconfiguring a bioinformatics algorithm to run across a large cluster of processors and developing a data-capture approach to access and store data in real time from a laboratory instrument.

While each pilot solved a big data analytics challenge, the resulting capabilities did not provide examples that would be powerful enough to push transformational change across the organization, as the organizational leaders had hoped.

In subsequent pilots, we advised the agency to focus on less rigorous but more far-reaching pilots. In one project, the agency piloted an unstructured natural language processing and text search utility across a number of disparate data archives. In another project, we deployed a data platform that could rapidly generate millions of records of synthetic data for algorithm testing.

In each case, organizational decision-makers could more easily see the applicability and potential of big data analytics and more clearly understand the potential of big data to transform their organization.

Before using new data, make sure you know its provenance (where it came from) and its lineage (what’s been done to it).

Often in the excitement of big data, decision-makers and project staff forget this basic advice. They are often in a hurry to immediately start data mining efforts to search for unknown patterns and anomalies. We’ve seen many cases where such new data wasn’t properly scrutinized – and where supposed patterns and anomalies later turned out to be irrelevant or grossly misleading.

In one such case at a federal health agency, information contained in a data source suggested that there was a significant uptick in the number of less-experienced clinical investigators associated with a set of therapeutic areas. Project staff identified this as an important trend to aid in risk analysis for the agency and prepared to brief senior decision-makers.

However, when the findings were presented first to the administrator for the data source, he suspected that the trends might coincide with the roll-out of new address fields.

As a result of a data-field change, when new address information was added for an investigator, it didn’t append to the original file, but created an entirely new file – making it appear that there were many new investigators, when in fact the number of investigators had slightly decreased over time.

This scenario could have been avoided through an investigation and annotation of candidate data sources with provenance and lineage information prior to operational use. With big data analytic techniques, such details can be prospectively or retrospectively annotated to data records, indicating the prevailing process and data standard at the time of collection.

Then, data miners can leverage this factor in data mining efforts and predictive models to test whether the data-collection process is causing a significant effect in the outcome variable of interest.

Don’t start with a solution; introduce a problem and consult with a data scientist.

Unlike conventional analytics platforms, big data platforms can easily allow subject-matter experts direct access to the data, without the need for database administrators or others to serve as intermediaries in making queries. This provides health researchers with an unprecedented ability to explore the data – to pursue promising leads, search for patterns and follow hunches, all in real time. We have found, however, that many organizations don’t take advantage of this capability.

One federal health agency we worked with, for example, invested in big data analytics to enable network analysis of nodes in a supply chain. Instead of giving its subject-matter experts free rein to look for new and unexpected patterns, the agency stayed with the conventional approach, and simply provided canned business-intelligence reports and visualizations to the end-users.

Not surprisingly, the outputs of this approach disappointed organizational decision-makers in terms of generating new insights and value. We strongly encouraged the agency to make sure subject matter experts could have direct access to the data to develop their own queries and analytics.

Once this was provided, the user community rapidly grew, and there was an associated increase in new capability, training requests, and overall value for the organization.

Conclusions

Health organizations often build a big data platform, but fail to take full advantage of it. They continue to use the small-data approaches they’re accustomed to, or they rush headlong into big data, forgetting best practices in analytics.

It’s important to aim for initial pilots with wide applicability, a clear understanding of where one’s data comes from, and an approach that starts with a problem, not a solution. Perhaps the hardest task is finding the right balance.

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Big Hitters in Health: Social, HCP Comms and Big Data

Big Hitters in Health: Social, HCP Comms and Big Data | Analytics & Social media impact on Healthcare | Scoop.it

Digitally Sick are back and have taken the opportunity to look at the three big hitters for digital health in 2014: Social. HCP communications and big data (with the exception of mobile health which needs a pod of it's own).

 

Social media is now almost passé but over the last decade has revolutionised all aspects of pharma communications from patient support to clinical trials. In 2014 we are still struggling with how we should communicate with HCP's and finally big data, or data, is now the most exciting frontier in healthcare, what are the issues and how can this be leveraged by pharma?

 


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Health Data's Future: 6 Paths to Health Data Maturity | HL7 Standards

Health Data's Future: 6 Paths to Health Data Maturity | HL7 Standards | Analytics & Social media impact on Healthcare | Scoop.it

Every year around the time of the health and government data extravaganza in Washington, Health DataPalooza, it’s reason to do an accounting of how far we’ve come in terms of accessing health data and using it as a foundation for value-based medicine. NPR says we have reached our “Awkward adolescence” (echoing Susannah Fox) with health data—lots of amazing things happening, but not a lot of impact.

Of course there’s plenty more work to do to make health data more accessible, more liquid and more private, but the progress since Health DataPalooza started less than 5 years ago is amazing, and we should take note, then come back to the paths forward.

This year, the big news was the FDA announced “OpenFDA”, available via an open API, with information on adverse drug events. Time will tell what the release will mean in terms of delivering health, but with over a thousand datasets now release by HHS alone, we are seeing a wave of new capability, even if data stories take time to tell.

Meanwhile, 80% of healthcare is now digitized, more than doubling from just a few years ago.

Samsung and Apple see the potential for accessing and harvesting health data and are moving into the fray to create personal health tracking hubs. There are many more examples showing that health data has, indeed, reached the limelight. Big health data, is now often called “the new oil”, and it’s already serving as a key resource in driving economics and powering countless new companies.

But that’s not what this post is about.

All this data is great, but it doesn’t take big data or rocket science to figure out what’s killing us, and how it might be prevented.

If you’d like to see what is killing us, check out this (small) data tool: http://vizhub.healthdata.org/gbd-cause-patterns/

Stop the presses: It’s us.

You take smoking, diabetes, obesity, cardiovascular disease and alcohol out of the mix, and the vast majority of those in the developed world would live to 90+. For data to really have an impact on health, it’ll have to have an impact on us. Many of these disease are diseases of behavior. We can debate how difficult it is to change behavior, or what biochemistry, genetics or other factors drive behavior, but most of our health problems could be prevented by making different choices. Consumers are going to need to care about it and use it.

There are bright spots that this is possible. Engagement rates reach 70% among institutions who do it well, but it takes leadership.

The reorganization of the ONC without a consumer office doesn’t show a lot of confidence that they are going to lead the way.

How do we fix this?

We’re nearing the point where we’ll be able to capture someone’s vital signs every minute of every day via Samsung, Apple, and many others. Will all this measurement save us from ourselves? Can we truly get prevention, or do will we just get better at heading off problems at the last minute? While preventing heart attacks is great, as a new iWatch is rumored to do, it would be even better if we could fix the unhealthy state that makes them possible before a last-minute intervention is necessary.

With that in mind, here are my wishes and a few predictions for the next phase of health care and health technology (now forever linked) and the road to solving health care with health data:

1. We need to create tools that can actually measure and impact behavior on what goes into us, not just stats on where we are and how we’re moving.

At the end of the day, we’re going to need to measure and provide feedback on input on intake as much as output. We’ll need to not only sense motion and vital signs but also what we’re putting into our bodies in terms of food, drink and chemicals, and start to change it. There has been work on tooth sensors to measure intake and Apple and others appear to be working on hydration sensors. It’ll be exciting to see developments in these areas in the coming years.

2. We need to better understand what drives metabolic disease. Metabolism-related killers are becoming our primary killers, but many normal weight people, in addition to obese people, die of metabolic disease. There’s still a lot we don’t understand about prevention and the disease. Yet metabolic disorders such as diabetes are taking an ever-greater toll and half the country will be at risk for diabetes by 2020. That’s a lot of suffering, a lot of death, and an enormous cost.

3. We need to prepare for the fight of a generation. Metabolic diseases are killing us in ever-larger numbers. The more we measure what’s driving costs, as we collect more and more Health Data, we’re going to run straight into a very big wall of conclusion: sugar is killing us.

With the release of FedUp, the idea of sugar as a culprit for our health care woes is starting to hit the mainstream.  If the fight against control of tobacco was tough (and by no means won), the fight against sugar will be 10x harder.

4. We need to correlate outcomes and environment. That means we need to understand what networks behavior of the health care system.  We’ll learn a lot from the 125,000 people who die per year from not adhering to their medications. Why aren’t they taking them on time? What’s preventing people from treating themselves?

For that we need to understand things at a systems level and better correlate with the social determinants of health. As Atul Gawande pointed out, yet again, at health DataPalooza, the overall vulnerability of a population is what’s drives our biggest health costs. The intersection of socio-economic/social determinants and network behavior will help us solve major hotspots, major sources of cost and suffering.

5. This one might be obvious, but we need to be better at predicting with data. EHRs like their name implies, are records, focused on the past. We need electronic health systems that are predictive. Apple and Samsung or others will do it, and they appear to be correctly focused on a new kind of technology for the new business model of health care, focused on risk spread among all players (and value place on prediction).

Dave Chase, CEO of Avado,  now part of WebMD, issued a stern warning to healthcare providers and their approach to healthIT on Susannah Fox’s blog:

“Just as it was easy to dismiss Google, craiglist, ebay, groupon, foursquare, facebook, etc. so too are the Iora Healths, Caremores, HealthCare Partners, Edison Health, One Medical, Surgery Center OK, Paladina Health, etc. ,but their value proposition is compelling. All of those players are deploying health IT in a radically different way than incumbents. Those orgs and their supporting technology take it for granted that patients are a core member of the care team, have access to their data and generally are using IT for competitive advantage.”

6. We need better rules on ownership and rights around health data, we could start with a Health Data Bill of Rights. In the consumer space, the rise of Snapchat and Whatsapp are indicative of a rise in the awareness and need for privacy. In health care, it will take time, but as health data gets “consumerized” with Apple and Samsung entering the fray, I predict the needs will become more and more apparent.

We need to work on rules and awareness to make health data more private and at the same time more easily exchanged. I don’t know exactly what that will look like but I, like many others, get the sense the answer may come through the blockchain. Fred Wilson at Union Square Ventures sees it as driving the next big investment cycle, after social and now mobile. He says, “our 2014 fund will be built during the blockchain cycle”. More on that in an upcoming post.

What do you think? What do we need to solve health care with health data?

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