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Data-based medicine: The end of evidence-based medicine?

As all kinds of information are being collected about every aspect of our lives, the data generated at this exorbitant rate can lead to advancements in research and health care.  That is the idea behind big data” and it’s disruptive benefits for the health care industry.  The term encompasses a searchable vast data collection for relative information in order to quickly identify trends.  Like all other disruptive innovations, the focus is speed.  However, medicine, unlike most industries, has never been quick to adapt to trends.


The history of medicine started out based on the knowledge of religious or spiritual theories.  The process for medical decision-making was highly subjective, and a few thousand years later the advancements in clinical judgments were based on individual preference.  Today, we would consider this an example of clinical-based medicine, practice based on individual or group observations.  It wasn’t until the later in the 20th century that doctors and health care researchers began to use the limited data that had been collected and evaluate the effectiveness of individual patient treatments.  Epidemiological methods were then devised to track explicit evidence of the effectiveness clinical practice guidelines and policies.  This disruption in medicine would lead to policies and practice guidelines being anchored on experimental evidence gathered from data rather than expert opinions.

Big data is a huge collection of data that is unmanageable by traditional evidence-based means and is a seismic disruption in the field of medicine.  One of the first published incidents of using big data to affect doctor decision-making was in 2011 at Stanford Lucile Packard Pediatric Hospital, where Dr. Frankovich searched through her medical records of pediatric lupus patients to determine whether or not to prescribe anticoagulant medication.  Because there were not any published guidelines and scant literature on the subject, she resorted to analyzing the patterns revealed in her collection of medical charts.

Lloyd Marino, CEO of Avetta Inc., a global strategy company, says big data is not a quick fix for immediate answers, especially in health care.  Unlocking the value of big data requires an ongoing process of the three A’s: automation, analytics, and action.

Automation sorts through and cleanses the data from numerous sources.  By normalizing the collected data, it can be integrated with current health care models on a continuous basis in order to produce real-time outcomes.  For example, medical records are filled with dozens, if not hundreds, of data points per patient and can be routinely updated inside an electronic medical record.  Beyond just collecting information, medical records can be combed through by robust learning machines for patterns and filtered based on disease, risk factors, or outcomes.

However, machine-learning algorithms from auto-generated data needs to be built and mastered.  Big data analytics explores deeper into the stream of healthcare information and finds solutions undiscoverable by traditional search means through moving beyond just managing data to mastering it.  Analytics does not just offer insight but can help create efficient better hospital infrastructure and streamline drug testing.

Most importantly, the action taken must be deployed wisely and rapidly to achieve a high return on investment (ROI), and this would speed the pharmaceutical industry’s notoriously slow pace.  Success also depends on how these solutions are aligned with key health care objectives, how easy for practitioners and invested health care workers to make use of solutions, and how well it integrates with existing protocols and procedures.

Evidence-based medicine is facing a disruptive force. However, it will never be fully uprooted; much like clinical-based medicine continues to exist today.  Big data has the advantages of size and speed compared to evidence-based medicine.  However, big data alone will not solve any issues for health care problems that exist for individual patients and communities.  Proper implementation of automation, analytics, and action, can help properly leverage big data for new solutions to health care models.

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jean marc mosselmans's curator insight, March 22, 2015 8:02 AM

the major danger is to forget the difference between observational studies and intervention studies. Modern medicine is full of very promising observational studies and hypothesis, unfortunately not confirmed by interventional dubble blind studies

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Ingredients for streamlining care management | Healthcare IT News

Ingredients for streamlining care management | Healthcare IT News | Healthcare and Technology news | Scoop.it

In an era where medicine is highly specialized and different specialties are involved in the care of a patient, intelligent use of information technology is essential to help providers, payers and patients achieve better care management outcomes while simultaneously improving cost and quality of care.

While some entities, such as the Department of Veterans Affairs, have implemented solutions where patients have the ability to view their personal health records online and offline, the majority of the healthcare industry continues to face multiple challenges while implementing care management processes. Care management for large, diverse populations is highly complex and subjective, largely because needs vary for each patient and encounters may span across multiple care settings and plans.

Although a large proportion of health information today is captured electronically, integrated data around patients and their underlying disorders is often not available to providers at the point of care. However, efforts to code clinical content with standard terminology has, to some extent, helped streamline information across applications. There is also a lack of alignment between payers and providers in regards to cost of care management services and shared risk arrangements, leading to sub-optimal care quality.

How organizations manage their healthcare data, and what they use this data for, therefore becomes extremely critical to the success of these programs. While technology plays a very important role in areas like decision support, care coordination and population health management, providers and payers are still faced with the challenge of managing both complex people and process challenges.

Effective use of patient data

Patient data adds value across multiple areas such as decision support, planned interventions and medical reconciliation. Such examples include:

  • Using CPOE Based Order Sets: Effective clinical decision support tools contained within an order set can help enforce the use of quality measures or meaningful use criteria by providers. An example would be the use of a venous thromboembolism (VTE) risk assessment and subsequent prophylaxis for high risk patients embedded within an order set. Monitoring the prophylaxis regimen based on the VTE risk score can help reduce incidence of venous thrombosis.
  • Clinical Information Exchange: Effective care coordination requires healthcare data to flow seamlessly across all parts of the healthcare ecosystem, including providers, payers and consumers. By aligning incentives, all parties can reduce costs and improve quality of care. By leveraging health information exchanges across radiology, laboratory, perioperative, inpatient and outpatient applications, healthcare organizations have the ability to access patient data in a timely and secure fashion.
  • Medical Reconciliation: This feature is commonly available in electronic health records (EHRs) and can play a very important role in preventing adverse drug reactions. For example, the use of over-the-counter (OTC) medications like acetaminophen may not get recorded in an EHR, but can be retrieved from the pharmacy or the medication management application. This is extremely critical information for a physician, given the hepatotoxic profile of the drug.
  • Patient Registries: A patient registry fed with data from EHR applications can show the treatment prescribed to patients and identify care gaps, based on evidence-based guidelines. Care management programs can use this kind of analysis to highlight areas of improvement, thus positively impacting cost and quality of care.

Promoting patient engagement

Patient education plays a very important role in effective care management. Patients who are actively focused on learning more about their conditions are more likely to participate in initiatives that promote preventive steps and healthy behaviour. The use of patient portals, for instance, allow patients to have anytime, anywhere access to their medical records, and the ability to schedule appointments, request medication reconciliation, etc.

Processes such as discharge management and preventive care can also provide strong opportunities to increase patient participation. Such processes play a crucial role in keeping readmissions and acute care costs to a minimum. Automated alerts informing patients to make appointments or follow up on lab visits can help prevent potential acute and chronic conditions.

Patients today are increasingly using consumer devices and mobile apps to store and monitor their health parameters. Wearable devices have the ability to change the way health data is collected and managed, and care management processes will soon need to incorporate consumer technology to enhance patient engagement and self management.

Managing Stakeholder Expectations

To drive a sustainable care management program, it is important to demonstrate value to key stakeholders including providers, payers and patients. However, the definition of value differs from one entity to another. For instance, providers and payers often do not see eye to eye on issues such as risk sharing and care management goals. It is essential to build consensus on many of these issues and agree on clearly defined goals around care objectives, processes and costs.

Addressing issues around provider and payer expectations could lead to significant advantages for the healthcare industry as a whole. According to the Center for Disease Control and Prevention, the government spends nearly three-fourths of its total healthcare expenditure on chronic disease, an area where care management programs can make a large impact. A concerted effort from all major stakeholders to streamline care management objectives and processes would have a very large impact on healthcare cost and quality.

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Why 2014 was a groundbreaking year in digital health

Why 2014 was a groundbreaking year in digital health | Healthcare and Technology news | Scoop.it

2014 was the most exciting year in digital health since 2000, when the human genome was cloned. In February 2001, The Human Genome Project and Craig Venter’s Celera Genomics published the hallmark event. What followed was over a decade of glimmers of the potential for personalized medicine and new insights into disease, but also realistic mitigations in expectations, as is wont to happen in health care.


There is every indication that the next decade will be different — there will be an acceleration in innovation and development of devices to assess our healthy and ailing selves. What happened in 2014? A huge increase in funding and corporate investment in digital health technology (e.g., mobile, social media, genetics and big data), and massive growth in the accessible population, and the amount of open data:

  • Funding in digital health startups, tracked by an accelerator Rock Health since 2011, has grown steadily at double digit growth until this past year, when records were shattered with $4.1B in funding, more than double the 2013 amount.
  • Almost every major consumer technology company announced a large health initiative, notably Google, Apple, and Samsung.
  • Electronic health record and sensors were positioned to join or actually entered the “Internet of Things”. The partnership between Apple and Epic alone could reach 20 percent of patients entering a health care system in the U.S. An estimated 10M activity monitor units were sold in 2014 and phones became personal health monitors with the release of Apple’s HealthKit and Google’s Google Fit.
  • Lastly, the number of large data sets that opened in health care and the tools to analyze them came of age in 2014. For example, the FDA launched openFDA in June 2014, which made it easier to analyze data about adverse events, drug and medical device recalls, prescription and over the counter product labeling, and to access open source code for analyzing this data.

What does this mean for our health? Funding will help drive innovation, and greater connectivity between patients and the people and systems that deliver their care will help drive efficiencies. Both of these will enable developers to more easily amass huge data sets to advance personalized diagnosis and treatment, and support efforts to prevent disease.

Innovation. The last five years have been a “wild west” of digital health startups with greatly varying business rationales and user adoption. We are now beginning to see some sound inventions that make economic sense and will be used by patients, providers, and systems. Activity monitors will go stealth, such as the contact lens being developed by Google and Novartis that detects blood glucose levels. Quantification of conditions will advance so that we have a better understanding of the level and type of disease we are dealing with, such as Oculogica’s brain injury detection system for concussion and other brain afflictions. (Disclosure: The author is a consultant to Oculogica.)

Efficiencies. One of the most needed, but most difficult to realize, implications of health care systems partnering with technology companies, are changes that reduce time and costs for health care systems and patients — from the simple check in at a clinic or hospital, to the number and nature of tests ordered, to smarter follow-up. For example, the first Epic Apple integration at Ochsner Health System in Louisiana estimated a 40 percent decrease in readmissions based on a pilot study with 100 heart failure patients.

Personalization and outcomes. 2014 won’t be the end of guidelines and recommendations based on the general population, but we are at a turning point to eventually achieve ubiquitous genomic assessments of individuals and prediction for optimal treatment. The interim step will be larger data sets — ideally from clinical records and recorded from sensors — which will allow segmentation of patients by age, gender, stage of disease, and other factors. This enables providers to tailor care based on individuals or small segments, rather than large swaths of the population.

Prevention. Prevention is the holy grail in health care with significant impact on health and cost. Programs have been difficult to implement because adoption has been too onerous. Return on investment is difficult to quantify and is often not realized by one hospital or payer. Less obtrusive sensors and more connected systems lower or even remove barriers to adoption. Return on investment will be more quantifiable as individuals, rather than health systems, are followed and quantified.

What do we need in health care? Fewer people who get ill in the first place. When they do, they should receive better care, tailored to who they are and the specifics of their disease, delivered at a lower cost. The challenges notwithstanding, we moved a step closer to this fantastic vision for health care in 2014.


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Cerner big data platform gets new client

Cerner big data platform gets new client | Healthcare and Technology news | Scoop.it

Truman Medical Centers and the University of Missouri-Kansas City's Center for Health Insights have teamed up on a new initiative that will harness data from electronic medical records, de-identifying it and digesting it into a database that can help inform better care decisions.

Both organizations will partner with EMR giant Cerner to leverage its Health Facts data warehouse platform to drive the analytics initiative. Health Facts extracts data from both clinical and financial IT systems, de-identifies the data, standardizes terms through mapping to common nomenclature and has the ability to create adverse drug events and outcomes reports.

The platform, as Cerner officials described, will allow the two-hospital TMC to use its current clinical and financial data and transform it into a usable form that can be leveraged to improve patient safety and care outcomes. What's more, TMC officials anticipate the data analysis can also be used to reduce specific health disparities and reduce costs for certain procedures.

Specifically, with the platform TMC officials will be able to use the generated reports and compare one's organization's performance with other clients who use the warehouse. The warehouse already includes millions of EMR records from inpatient, ED and outpatient visits from patients nationwide.

"The centerpiece of this partnership provides tools to accelerate clinical and translational research and ultimately provide better health outcomes," said Lawrence Dreyfus, UMKC vice chancellor for Research and Economic Development, in a Feb. 18 press statement. "We couldn't be more excited about the prospects that this partnership holds for healthcare decisions that ultimately improve care and reduce costs."

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Digital health in 2015: What's hot and what's not?

Digital health in 2015: What's hot and what's not? | Healthcare and Technology news | Scoop.it

I think it’s fair to say that digital health is warming up. And not just in one area. The sheer number and variety of trends are almost as impressive as the heat trajectory itself. The scientist in me can’t help but make the connection to water molecules in a glass — there may be many of them, but not all have enough kinetic energy to ascend beyond their liquid state. The majority are doomed to sit tight and get consumed by a thirsty guy with little regard for subtle temperature changes.


With this in mind, let’s take a look at which digital health trends seem poised to break out in 2015, and which may be fated to stay cold in the glass. As you read, keep in mind that this assessment is filtered through my perspective of science, medicine, and innovation. In other words, a “cold” idea could still be hot in other ways.

Collaboration is hot, silos are not. Empowerment for patients and consumers is at the heart of digital health. As a result, the role of the doctor will shift from control to collaboration. The good news for physicians is that the new and evolved clinician role that emerges will be hot as heck. The same applies to the nature of innovation in digital health and pharma. The lone wolf is doomed to fail, and eclectic thinking from mixed and varied sources will be the basis for innovation and superior care.

Scanners are hot, trackers are not. Yes, the tricorder will help redefine the hand-held tool for care. From ultrasound to spectrometry, the rapid and comprehensive assimilation of data will create a new “tool of trade” that will change the way people think about diagnosis and treatment. Trackers are yesterday’s news stories (and they’ll continue to be written) but scanners are tomorrow headlines.

Rapid and bold innovation is hot, slow and cautious approaches are not. Innovators are often found in basements and garages where they tinker with the brilliance of what might be possible. Traditionally, pharmaceutical companies have worked off of a different model, one that offers access and validation with less of the freewheeling spirit that thrives in places like Silicon Valley. Looking ahead, these two styles need to come together. The result, I predict, will be a digital health collaboration in which varied and conflicting voices build a new health reality.

Tiny is hot, small is not. Nanotechnology is a game-changer in digital health. Nanobots, among other micro-innovations, can now be used to continuously survey our bodies to detect (and even treat) disease. The profound ability for this technology to impact care will drive patients to a new generation of wearables (scanners) that will offer more of a clinical imperative to keep using them.

Early is hot, on-time is not. Tomorrow’s technology will fuel both rapid detection and the notion of “stage zero disease.” Health care is no longer about the early recognition of overt signs and symptoms, but rather about microscopic markers that may preempt disease at the very earliest cellular and biochemical stages.

Genomics are hot, empirics are not. Specificity — from genomics to antimicrobial therapy — will help improve outcomes and drive costs down. Therapy will be guided less and less by statistical means and population-based data and more and more by individualized insights and agents.

AI is hot, data is not. Data, data, data. The tsunami of information has often done more to paralyze us than provide solutions to big and complex problems. From wearables to genomics, that part isn’t slowing down, so to help us manage it, we’ll increasingly rely on artificial intelligence systems. Keeping in mind some of the inherent problems with artificial intelligence, perhaps the solution is less about AI in the purest sense and more around IA — intelligence augmented. Either way, it’s inevitable and essential.

Cybersecurity is hot, passwords are not. As intimate and specific data sets increasingly define our reality, protection becomes an inexorable part of the equation. Biometric and other more personalized and protected solutions can offer something that passwords just can’t.

Staying connected is hot, one-time consults are not. Medicine at a distance will empower patients, caregivers, and clinicians to provide outstanding care and will create significant cost reductions. Telemedicine and other online engagement tools will emerge as a tool for everything from peer-to-peer consultation in the ICU to first-line interventions.

In-home care is hot, hospital stays are not. “Get home and stay home” has always been the driving care plan for the hospitalized patient. Today’s technology will help provide real-time and proactive patient management that can put hospital-quality monitoring and analytics right in the home. Connectivity among stakeholders (family, EMS, and care providers) offers both practical and effective solutions to care.

Cost is hot, deductibles are not. Cost will be part of the “innovation equation” that will be a critical driver for market penetration. Payers will drive trial (if not adoption) by simply nodding yes for reimbursement. And as patients are forced to manage higher insurance deductibles, options to help drive down costs will compete more and more with efficacy and novelty.

Putting it all together: What it will take to break away in 2015?

Beyond speed lies velocity, a vector that has both magnitude and direction. Smart innovators realize that their work must be driven by a range of issues from compatibility to communications. Only then can they harness the speed and establish a market trajectory that moves a great idea in the right direction. Simply put, a great idea that doesn’t get noticed by the right audience at the right time is a bit like winking to someone in the dark. You know what you’re doing, but no one else does.


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How Big Data Will Transform Our Economy And Our Lives In 2015

How Big Data Will Transform Our Economy And Our Lives In 2015 | Healthcare and Technology news | Scoop.it

The great Danish physicist Niels Bohr once observed that “prediction is very difficult, especially if it’s about the future.” Particularly in the ever-changing world of technology, today’s bold prediction is liable to prove tomorrow’s historical artifact. But thinking ahead about wide-ranging technology and market trends is a useful exercise for those of us engaged in the business of partnering with entrepreneurs and executives that are building the next great company.

Moreover, let’s face it: gazing into the crystal ball is a time-honored, end-of-year parlor game. And it’s fun.

So in the spirit of the season, I have identified five big data themes to watch in 2015. As a marketing term or industry description, big data is so omnipresent these days that it doesn’t mean much. But it is pretty clear that we are at a tipping point. The global scale of the Internet, the ubiquity of mobile devices, the ever-declining costs of cloud computing and storage, and an increasingly networked physical word create an explosion of data unlike anything we’ve seen before.

The creation of all of this data isn’t as interesting as the possible uses of it. I think 2015 may well be the year we start to see the true potential (and real risks) of how big data can transform our economy and our lives.

Big Data Terrorism

The recent Sony hacking case is notable because it appears to potentially be the first state-sponsored act of cyber-terrorism where a company has been successfully threatened under the glare of the national media. I’ll leave it to the pundits to argue whether Sony’s decision to postpone releasing an inane farce was prudent or cowardly. What’s interesting is that the cyber terrorists caused real fear to Sony by publicly releasing internal enterprise data — including salaries, email conversations and information about actual movies.

Every Fortune 2000 management team is now thinking: Is my data safe? What could happen if my company’s data is made public and how could my data be used against me? And of course, security software companies are investing in big data analytics to help companies better protect against future attacks.

Big Data Becomes a Civil Liberties Issue

Data-driven decision tools are not only the domain of businesses but are now helping Americans make better decisions about the school, doctor or employer that is best for them. Similarly, companies are using data-driven software to find and hire the best employees or choose which customers to focus on.

But what happens when algorithms encroach on people’s privacy, their lifestyle choices and their health, and get used to make decisions based on their race, gender or age — even inadvertently? Our schools, companies and public institutions all have rules about privacy, fairness and anti-discrimination, with government enforcement as the backstop. Will privacy and consumer protection keep up with the fast-moving world of big data’s reach, especially as people become more aware of the potential encroachment on their privacy and civil liberties?

Open Government Data

Expect the government to continue to make government data more “liquid” and useful – and for companies to put the data to creative use. The public sector is an important source of data that private companies use in their products and services.

Take Climate Corporation, for instance. Open access to weather data powers the company’s insurance products and Internet software, which helps farmers manage risk and optimize their fields. Or take Zillow as another example. The successful real estate media site uses federal and local government data, including satellite photography, tax assessment data and economic statistics to  provide potential buyers a more dynamic and informed view of the housing market.

Personalized Medicine

Even as we engage in a vibrant discussion about the need for personal privacy, “big data” pushes the boundaries of what is possible in health care. Whether we label it “precision medicine” or “personalized medicine,” these two aligned trends — the digitization of the health care system and the introduction of wearable devices — are quietly revolutionizing health and wellness.

In the not-too-distant future, doctors will be able to create customized drugs and treatments tailored for your genome, your activity level, and your actual health. After all, how the average patient reacts to a particular treatment regime generically isn’t that relevant; I want the single best course of treatment (and outcome) for me.

Health IT is already a booming space for investment, but clinical decisions are still mostly based on guidelines, not on hard data. Big data analytics has the potential to disrupt the way we practice health care and change the way we think about our wellness.

Digital Learning, Everywhere

With over $1.2 trillion spent annually on public K-12 and higher education, and with student performance failing to meet the expectations of policy makers, educators and employers are still debating how to fix American education. Some reformers hope to apply market-based models, with an emphasis on testing, accountability and performance; others hope to elevate the teaching profession and trigger a renewed investment in schools and resources.

Both sides recognize that digital learning, inside and outside the classroom, is an unavoidable trend. From Massive Open Online Courses (MOOCs) to adaptive learning technologies that personalize the delivery of instructional material to the individual student, educational technology thrives on data. From names that you grew up with (McGraw Hill, Houghton Mifflin, Pearson) to some you didn’t (Cengage, Amplify), companies are making bold investments in digital products that do more than just push content online; they’re touting products that fundamentally change how and when students learn and how instructors evaluate individual student progress and aid their development. Expect more from this sector in 2015.

Now that we’ve moved past mere adoption to implementation and utilization, 2015 will undoubtedly be big data’s break-out year.


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Irina Donciu's curator insight, January 15, 2015 4:33 AM

The great Danish physicist Niels Bohr once observed that “prediction is very difficult, especially if it's about the future.”

Maryruth Hicks's curator insight, September 8, 2015 11:27 AM

Digital learning and big data in education might lead to educational reform!