Patient experience, personalized medicine, big data analytics garner top digital health investment in record-setting first half of 2016

Patient experience, personalized medicine, big data analytics garner top digital health investment in record-setting first half of 2016Funding for digital health companies reached new heights in the first half of 2016, totaling $3.9 billion invested in 155 deals for seed and Series A rounds, according to a report by StartUp Health.

The five leading investment groupings were patient experience with $958 million, wellness with $854 million, personalized medicine with $524 million, big data analytics with $406 million and workflow with $328 million.

“Personalized health has become an explosive winner while patient experience maintains strong and steady growth,” the report said.

In 2015 early stage investments for the first half of the year amounted to 3.5 billion, reaching $7 billion for the year. In 2015, $2.9 was invested in the first half, and the year-end total was $6 billion.

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The Big Data Difference: Predictive Analytics

The Big Data Difference: Predictive AnalyticsWhat if you could know in advance which patients would benefit from certain therapies? Or identify patients approaching a medical crisis and intervene before it’s too late? While doctors have traditionally had to rely on instinct to make these calls, predictive analytics could be a game changer for hospitals, healthcare providers and patients.

The Power of Prediction

Medical sensors and data analytics can be used to power medical devices that can predict adverse outcomes before they occur. By analyzing very large data sets, researchers can identify subtle markers, such as small changes in vital signs or patient behaviors that can be correlated to development of serious conditions like heart failure or kidney failure. If we can learn to look for the right signs, we can develop an early warning system for imminent medical crises.

Combining data analytics with body-worn or implantable medical sensors will allow us to better monitor patient health. These sensors can pick up subtle changes in biometrics, biomarkers and other patient data over time. Using predictive analytics, smart sensors could use these readings to detect early warning signs of kidney failure, stroke, heart failure and other medical crises, alerting healthcare providers before adverse events occur. Data analytics could also be used to power smart apps or devices that provide ongoing guidance to patients in response to sensor data in order to help them better manage chronic conditions.

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The Difference Between Big Data and Smart Data in Healthcare

The Difference Between Big Data and Smart Data in Healthcare“Big data” is one of those terms that gets thrown about the healthcare industry – and plenty of other industries – without much of a consensus as to what it means. Technically, big data is any pool of information that is compiled from more than a single source.

For healthcare organizations, this could mean creating a database that takes patient names and addresses from one system and matching it up with scheduled appointments from another, or integrating claims data with clinical notes from the EHR.

Stitching multiple sources of information together into a centralized databank accessed by reporting or a query system can provide a more in-depth and actionable snapshot of a patient’s history, diagnoses, treatments, socioeconomic challenges, and risk profiles.

But leveraging these disparate data sources requires the right tools and competencies, which aren’t always easy to develop.

Electronic health records are starting to take big data analytics seriously by offering healthcare organizations new population health management and risk stratification options, but many providers still turn to specialized analytics packages to find, aggregate, standardize, analyze, and deliver data to the point of care in an intuitive and meaningful format.

These tools may include quality benchmarking and performance measurement systems, clinical analytics algorithms that monitor patients in real-time, revenue cycle and claims analytics, and population health management packages that foster engagement, deliver alerts and reminders, stratify beneficiaries, or gauge risk of a certain disease.

In addition to the right technologies, providers must invest time and manpower into acquiring the competencies to make analytics work for them.  This includes crafting a dedicated team of experts to oversee big data projects, implement and optimize software, and convince clinicians that these new strategies are worth their while.


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EHRs and digital health tools ‘dramatically transforming’ care experience, patients say

EHRs and digital health tools 'dramatically transforming' care experience, patients sayNearly 75 percent of patients expressed a high level of interest in accessing their electronic medical records, according to new research, and 33 percent indicated that EHRs have already changed their experience for the better.

“The patient experience is dramatically transforming,” CareCloud CEO Ken Comee said in a statement. “Patients of all ages are actually embracing digital online patient engagement tools from scheduling appointments to accessing their medical records and making online payments.”

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Top 4 Emerging Tech Trends in Healthcare Big Data Analytics

Top 4 Emerging Tech Trends in Healthcare Big Data Analytics

For most segments of the healthcare industry, big data analytics is still an evolving concept that hasn’t quite reached the mainstream of clinical care.

The prospect of integrating disparate sources of information into a multifaceted canvas of patient experiences is a tantalizing one, yet basic concerns with the usability of electronic health records, the availability of health information exchange, and a chronic lack of time, knowhow, and funding have all contributed to keeping big data on the back bench.

However, a new wave of commitment to health data interoperability, paired with advances in data standardization and a growing recognition that the EHR is no longer enough for cost-effective, high quality care, have started to make big data analytics a great deal more accessible to providers across the care continuum.

In turn, these accomplishments have raised stakeholders’ hopes that big data is about to produce a seismic shift in the way providers make decisions, interact with their patients, and power through their daily workflows.

Analytics is becoming a priority area for health IT investment as healthcare organizations get on board with the data-driven mentality of systemic reform – forty percent of respondents to a recent IDC Health Insights poll said budgets are loosening up for health IT projects – and big data is becoming a major criteria for financial and clinical success.

The majority of providers are still looking for tools and technologies that will enable them to participate in basic big data activities, like risk stratification, population health management, and reducing operational costs.  However, cutting-edge developers are already reaching past this first stage of adoption towards a future in which big data is no big deal.


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Predicting and Preventing Health Issues with Big Data

Predicting and Preventing Health Issues with Big DataFor eons, healthcare, in whatever form it came in, was fairly straightforward. Someone would get sick or injured, and a physician would prescribe a cure or treatment using the prevailing medical knowledge of the day. This has held true even in modern times, where many people only think about their health during those times when it fails them.

While this approach is a practical one, especially when many have other concerns on their mind, it doesn’t make for long term healthy outcomes. That’s why many medical institutions are trying to use the latest technology to improve patient care and help people live healthier lives by trying to predict health problems before they happen.

Some of this advanced technology is obvious and visible, while much of it happens behind the scenes. This is where big data is being used. On the surface, big data may seem like an odd choice for the healthcare industry, but it’s actually a perfect fit that can help health professionals predict and prevent health issues.

When broken down into its most basic parts, healthcare is really all about data. Doctors are often seen carrying clipboards filled out with patient data such as age, height, weight, blood pressure, blood type, and more. Even such rudimentary data can be challenging to collect and analyze.

Now factor in more advanced information like CAT scans, X-rays, and the biggest goldmine of health data — genetic data. It quickly becomes obvious that the amount of health data that is currently available or will one day become available is absolutely massive. All of this information needs to be taken into account by big data analytics in order to come up with the best, most accurate predictions. Better data sets essentially equal better results.

That’s really what the move to institute predictive analytics is all about — determining the biggest at-risk factors in patients and preventing health problems from developing. In a sense, it’s similar to how companies use business analytics to predict who is most likely to buy their products; instead, in the case of healthcare, it’s an analysis of factors taken from many different sources over the course of one person’s life. A checkup as an eight-year-old would be considered, much like an emergency room visit would twenty years late



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5 Best Practices to Avoid Data Breaches in the Healthcare Industry

5 Best Practices to Avoid Data Breaches in the Healthcare IndustryData breaches are common and can occur at almost every type of organization or company, but they are particularly troublesome and widespread in the healthcare industry. Patients’ sensitive medical records are constantly at risk, whether the organization is large or small, affecting individuals at every level of data breach.

The U.S. Department of Health and Human Services maintains an online database of healthcare breaches affecting over 500 individuals, but many smaller breaches occur each year as well. According to Forbes, over 112 million records were compromised by data breaches in 2015 alone—and 90% of the top ten breaches were related to hacking or IT incidents.

The average cost of a breach continues to rise, and in 2014, that average stood at $5.9 million. With the high prevalence of cybercrime still rising, the healthcare industry must take steps to reduce the number and impact of data breaches, which lead to the compromise of sensitive data and financial consequences. Healthcare organizations should follow cyber security best practices to minimize the risk of a breach. These steps include:

Educating Employees on Security Risks

Healthcare organizations may have stellar employees, but human error can always lead to security issues. Proper training on regulations, security protocols—and support for employees using mobile devices—can help reduce these errors and improve overall security. Employees should only have the data necessary to perform the functions of their job—the fewer places data is stored, the more secure it is.

Choosing Vendors Carefully

Many healthcare organizations use offsite data storage systems that work with third party vendors who are responsible for the organization’s records. Choosing partners who follow best practices are essential to keeping data safe. When an organization does not have direct control over the data, the security precautions must be just as strict as if the data was stored in-house.

Best Practices are the Best Defense

Unfortunately, it’s not always possible to prevent a data breach. By following best practices, however, healthcare organizations can minimize the risk of a breach and be better equipped to handle a one in the future. Preventing a breach may require quite a bit of preparation, but it can save money in the long run and prevent patients’ sensitive data from falling into the wrong hands.

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MIT’s answer to global health issues: Democratizing big data analytics

MIT's answer to global health issues: Democratizing big data analytics Discover how medical professionals and MIT researchers are using data-enabled systems to help doctors around the world make the best healthcare decisions.

“While wonderful new medical discoveries and innovations are in the news every day, doctors struggle with using information and techniques available right now,” writes Leo Anthony Celi, assistant professor of medicine, Harvard Medical School, in the Conversation commentary Improving patient care by bridging the divide between doctors and data scientists. “As a practicing doctor, I deal with uncertainties and unanswered clinical questions all the time.”

Enter big data

Celi feels there are opportunities for big data and information analytics in the healthcare field. “A digital system would collect and store as much clinical data as possible from as many patients as possible,” writes Celi. “It could then use information from the past—such as blood pressure, blood sugar levels, heart rate, and other measurements of patients’ body functions—to guide future doctors to the best diagnosis and treatment of similar patients.”

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Big Data Analytics Redefining the Healthcare Industry

blogpicBig Data Analytics has enormous potential to influence healthcare industry positively by meliorating the quality of care, reducing costs and avoiding preventable deaths.

Big Data is still in its early stage and is already changing the course of business sector all across the globe. Big Data Analytics has enormous potential to influence healthcare industry positively by meliorating the quality of care, reducing costs and avoiding preventable deaths. On a fundamental level, implementing Big Data in an organization makes it more productive, efficient and cost-friendly.

Understanding Big Data

Big Data can be described as a set of electronic data so complex and large in volume that it is very difficult to manage it using traditional software or hardware. It can neither be easily managed with basic data management tools.

The Indian healthcare industry has already started using Hospital Information Systems (HIS) and Electronic Health Records (EHR) to make their organization more profitable and productive. The industry is engaged in producing zettabytes of data every day by capturing exuberant amount of patient care records, diagnostic tests, prescriptions, insurance claims, monitoring vital signs, and most importantly the medical research information. The growth of these records will be explosive in the coming couple of years, which makes Big Data intervention in the healthcare industry utterly crucial.

Big Data Analytics in healthcare segment amalgamates clinical innovation and technology. This promising technology supports an array of healthcare functions to improve services and handle problems of the healthcare sector. It has introduced new ways for organizations to formulate actionable insights, boost up outcomes, reduce time to value and organize their future vision. The evaluated results can be very fruitful to enhance decision making capacity of the management.

By Aditya Kandoi

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The Critical Role of Data in Precision Medicine

Nastel Comments: Data analytics not only can do research and treatment to large groups on a broader scale but it also provides focus on the specific patient by improving accountability as all transactions are tracked and monitored. When claims are adjudicated, the processor has the necessary information to make the best decision for the patient without incurring additional costs to research the necessary supporting data. This reduces administrative costs and errors, provides accurate patient information and enables faster processing of claims.
The Critical Role of Data in Precision Medicine

Precision medicine promises to change that paradigm, to pioneer a new model of personalized, individual, patient-powered research and treatment. The idea is that, thanks to big data, researchers can tap into a wider range of digital sources and conduct deeper analysis into a greater variety of characteristics to better understand which types of treatments work best for more specific subsets of the population based on factors including other health conditions, environment, and lifestyle. Ultimately, this could mean personalized treatments designed specifically for each individual, offering a much greater likelihood of effectiveness.

Ingesting and effectively using the massive amounts of data available today currently requires various applications. Concentrating on integrating applications results in data silos, where each system’s data exists only within that system. To realize the potential for precision medicine, we must find ways to aggregate data from all sources – IoT, patient surveys, clinical lab data, etc. – in a centralized repository, giving clinicians access to this wealth knowledge with virtually any analytics tool or user interface. In other words, instead of focusing on building precision medicine applications, we first must focus on integrating and harmonizing the data to make it accessible by any application. – See more at:


Read the source article at Data Informed
Original Author: Gary Palgon