Technology has always been at the forefront of improving our understanding of diseases, but the rise of big data has taken this to new heights. Big data in healthcare isn’t new, but it is worth discussing over and over again because it has not yet reached its full potential. No one even knows what its full potential looks like yet.
Even still, the application of big data in healthcare has now reached a point where it’s producing meaningful results not only for researchers but also for clinicians and patients.
Big data has provided changes to the way people in healthcare and research work, but what about the changes it’s provided to specific treatments? These changes are already here, and they’re indicative of both what’s to come and what’s possible for both individuals and patient populations.
What is Big Data in the Healthcare Context?
Big data is a broad concept with applications in a wide swath of fields. In the healthcare context, big data refers to the practice of collecting, analyzing, and using data from many different sources, including patient data, clinical data, consumer data, and physical data. In the past, it was possible to collect only a few types of data in smaller volumes because the tools needed to process and apply it were unavailable.
In this way, big data goes hand-in-hand with other technological developments, like machine learning and artificial intelligence (AI). Before machine learning, both clinical studies and applications were massively limited in terms of their scope: you could only handle a certain volume of data or a set variety. Veracity was also a problem with big data sets, which impacted the validity of studies.
Today, big data is a huge part of healthcare. You can find it in the creation of electronic health records (EHRs), pharmaceutical research, medical devices, medical imaging, and genomic sequencing. It differs from previous advances because it encompasses what data scientists call the 3Vs of Big Data: volume, velocity, and variety.
Big Data Reintroduces Old Treatments
Evidence-based medicine is at the core of modern practice. From diagnosis to treatment, physicians and specialists rely on an extensive foundation of research before making decisions. Medical big data has the ability to impact predictive modeling, clinical decisions, research, and public health. But it does so with greater precision: big data uses temporal stability of association. It leaves causal relationships and probability distributions behind.
Hypertension represents an ideal case study of the impact of big data on medicine. Despite the various effective medicines, including beta-blockers, the rates of uncontrolled hypertension in the general population are still very high. Scientists are using big data and machine learning to identify other drugs that may be working against beta-blockers to prevent the patient from gaining control of their blood pressure. One study identified proton pump inhibitors (PPIs) and HMG CO-A reductase inhibitors as drugs that weren’t previously considered to be antihypertensive but that actually improved success rates in hypertension treatment.
Without big data, it would be both time-consuming and expensive to rerun studies on these kinds of drugs. Moreover, there simply wouldn’t be enough data available to do it.
Guest post by Abhinav Shashank, CEO and co-founder, Innovaccer.
Since 1966, Americans have received more Nobel Prizes in Medicine than rest of the world combined with astonishing advancement in medical treatments, but how much of it reflected on ground level is still a troublesome figure. The soaring costs of healthcare; the amount spent on healthcare is approximately 20 percent of the country’s GDP and the amount spent on one person per year is going to be roughly $10,000 in 2017; much higher than any other country. Despite ACA, more than 30 million people in the U.S. are still uninsured. With so many concerns, the healthcare industry needs innovation to change this bleak picture.
Innovative solutions have emerged in these aspects – the delivery of treatments to patients, the technology as well as the business aspects. A few innovations in healthcare are here to stay, resulting in a more convenient and effective treatment for patients today, where time is of the essence and providing patients a better future is a priority.
Big Data. Big Use. Big Outcomes
Data-driven innovations are poised to do wonders in healthcare industry. Big data has been used to predict diseases, find their cure, improve the quality of care and avoid preventable deaths. From increasing awareness in patients to transforming data into information, big data offers healthcare a paradigm shift. Instead of analyzing a single patient’s data, we can now explore entire patient population and predict patients’ health trend.
Some healthcare leaders have already extracted value from big data and are already putting them to good use. Many value-focused healthcare organizations are working to improve healthcare delivery and healthcare delivery and patient outcomes by making an integrated technology system that will allow practices to deliver evidence-based care that is more coordinated and personalized.
The health IT revolution is here and 2016 will be the year that actionable data brings it full circle.
Opportunities to achieve meaningful use with electronic health records (EHRs) are available and many healthcare organizations have already realized elevated care coordination with healthcare IT. However, improved care coordination is only a small piece of HIT’s full potential to produce a higher level synthesis of information that delivers actionable data to clinicians. As the healthcare industry transitions to a value-based model in which organizations are compensated not for services performed but for keeping patients and populations well, achieving a higher level of operational efficiency is what patient care requires and what executives expect to receive from their EHR investment.
This approach emphasizes outcomes and value rather than procedures and fees, incentivizing providers to improve efficiency by better managing their populations. Garnering actionable insights for frontline clinicians through an evolved EHR framework is the unified responsibility of EHR providers, IT professionals and care coordination managers – and a task that will monopolize HIT in 2016.
The data void in historical EHR concepts
Traditionally, care has been based on the “inside the four walls” EHR, which means insights are derived from limited data, and next steps are determined by what the patient’s problem is today or what they choose to communicate to their caregiver. If outside information is available from clinical and claims data, it is sparse and often inaccessible to the caregiver. This presents an unavoidable need to make clinical information actionable by readily transforming operational and care data that’s housed in care management tools into usable insights for care delivery and improvement. Likewise, when care management tools are armed with indicators of care gaps, they can do a better job at highlighting those patients during the care process, and feeding care activities to analytics appropriately tagged with metadata or other enhanced information to enrich further analysis.
Filling the gaps to achieve actionable data
To deliver actionable data in a clinical context, HIT platform advancements must integrate and analyze data from across the community—including medical, behavioral and social information—to provide the big picture of patient and population health. Further, the operational information about moving a patient through the care process (e.g., outreach, education, arranging a ride, etc.) is vital to tuning care delivery as a holistic system rather than just optimizing the points of care alone. This innovative approach consolidates diverse and fragmented data in a single comprehensive care plan, with meaningful insights that empowers the full spectrum of care, from clinical providers (e.g., physicians, nurses, behavioral health professionals, staff) to non-clinical providers (e.g., care managers, case managers, social workers), to patients and their caregivers.
From financial services, to technology, to telecommunications, retail and more, big data has made a meaningful impact across industries. In healthcare specifically, big data is being used to create a more efficient, effective and personal approach to providing care.
The statistics speak for themselves: As the $2.8 trillion industry continues to evolve, big data could add as much as $300 million per year.
But big data for healthcare is about more than revenue growth.
As the healthcare industry shifts towards a world of value-based and proactive patient care, big data offers health systems the ability to improve patient quality of life, increase preventable care and enhance patient engagement. Furthermore, big data has the ability to provide actionable insights in hospital settings while saving time, and ultimately costs, by allowing healthcare systems to operate more efficiently and effectively.
Learn more in the infographic below on how big data potential creates improvements in healthcare.
Dean Stephens is the CEO of Healthline, a media group and a health information technology company. Here Stephens discusses healthcare analytics and how it’s important to providers and patients; the ever-increasing importance of harvesting useable and life-changing information from unstructured big data; analytics in population health; the importance of ACOs and the future of Healthline.
Tell me about your background and your role at Healthline. I grew up in a small, blue-collar town in New England and was fortunate enough to attend an Ivy League college, which was a rare thing for this town. After college and graduate school, I got lucky to land a policy analyst position for the Washington State governor, but in no time, got drafted into management consulting at Deloitte. Much of my consulting time was spent in the healthcare industry learning first-hand how “upside down” the industry was. Thus, I joined other entrepreneurs to re-imagine this muddled industry and joined Healthline as CEO in 2001, not knowing then that I would end up building two companies simultaneously.
What does Healthline do and how has the company evolved?
Healthline’s mission is to make the people of the world healthier through the power of information. And we do this through two business units – our media group, which consists of our consumer health website Healthline.com, and our health information technology group, which includes a range of search and data analytics solutions built on our market-leading medical taxonomy. We are currently working with some of healthcare’s largest brands, including AARP, Aetna, Pfizer, Sanofi, UnitedHealth Group, Microsoft, IBM, GE and Elsevier.
Describe your personal view of analytics and what that means to the rest of us. Why is this important?
Healthcare is the most information-intensive industry on the planet. The number of diseases recognized today and the permutations on the treatment matches to individuals have exploded over the past 20 years. It’s impossible for an individual physician or a large, sophisticated provider or payer institution to deliver effective treatment across all patients without analyzing vast amounts of complicated data. We limped along in the traditional fee-for-service realm. Now as the healthcare market shifts to value-based reimbursement, the value of information and analysis rises dramatically as providers shift from being rewarded for sick care to well care.
Guest post by Anil Jain, MD, FACP, senior vice president and chief medical officer, Explorys, and consulting staff, Department of Internal Medicine, Cleveland Clinic.
Despite advances in medical education, the proliferation of medical journals and the speed of light retrieval of information on the Internet, the lag time between when researchers identify life-saving clinical interventions and when they are put into practice ranges from 10 to 25 years, averaging 17 years. This lag time between the discovery at the “bench” and its practice at the “bedside” is even more startling when you consider the impact of care at the “bedside” to the “bottom-line.” This “bottom-line” has become increasingly important with the formation of accountable care organizations (ACOs) that aim to reward provider organizations and payers that meet the “triple-aim”: high-quality care for the population, high-quality care for the patient, at the most affordable cost. Unfortunately, current practices at the “bedside” reportedly generate approximately $700 billion in care that isn’t necessary and may even be potentially harmful to the “bottom-line.” Moreover, despite healthcare expenditures of 17 percent of our GDP, the U.S. lags behind most industrial nations when looking at composite measures of healthcare quality.
With the increasing use of health information technology and data we should be able to shorten the time between “bench” to “bedside” and improve the “bottom line.”
“Big data” is data that is of high volume, variety and of sufficient velocity that is not amenable to traditional data storage and analysis tools. This “big data” is most typically generated from health systems’ electronic health records (EHRs), laboratory, radiology, financial and billing systems, personal health records, biometrics and smart devices. In addition, patients today are oftentimes utilizing various mobile health and wellness apps and wearable devices which also collect a plethora of data, which only adds to the complexity.
The aggregation of de-identified medical information across millions of health records from varying venues of care facilitating a longitudinal view of a person can be incredibly beneficial for researchers focused on net new knowledge discovery. For data from disparate health systems to be aggregated, it is vital that it is standardized and that subjects across health systems can be matched. This harmonization of disparate data coupled with the appropriate analytics software is critical to identify patterns in the data.
In this setting, the larger the data set, the more likely that a signal can be detected through the noise, even in the rarest of conditions. Fortunately, many hypotheses can be conceived and tested through appropriate analytics within this real-world data set in a much more cost-effective manner than conducting full-scale clinical trials. Furthermore, if a signal is detected or a pattern is found, researchers can then design a more focused explanatory or pragmatic clinical trial to prospectively test the hypotheses. For example, over the past few years within the Explorys network, more than a dozen peer-reviewed abstracts and publications have been generated by leveraging a de-identified data set comprised of nearly 48 million subjects, searchable by a specialized browser-based analysis and query application.
Lance Speck, general manager of Actian cloud and healthcare, speaks here about healthcare big data and how it can be used in healthcare to improve processes from care coordination to coding for ICD-10. In his day job, he is focused on delivering healthcare solutions to help payers and providers address an estimated $450 billion annual opportunity created through data analytics, ranging from fraud analytics to patient re-admission reduction to staff optimization to accountable care reporting and clinical auto-coding. For more than 20 years, Lance has served in a variety of management, sales and product roles in the software industry including a decade focused on SaaS, cloud and healthcare.
How can big data analytics improve patient care?
According to a recent PwC survey, 95 percent of healthcare CEOs are exploring better ways of using and managing big data; however, only 36 percent have made any headway in getting to grips with big data. All agree that big data analytics has the potential to improve the quality and cost of care, but many are still struggling with finding the right ways to infuse analytics into everyday operations. Assuming they realize that they already have access to the data, what do they do with it? What are the areas that will have the biggest impact? Where do they start?
Start with the basics. Organizations should focus in infusing big data analytics where a big impact can be recognized. They should ask themselves:
Is there enough value in solving the problem?
Can the problem can be predicted?
Can the problem be prevented?
Can the predictive action be delivered accurately, and in a timely fashion to make a difference?
Very early in the process, organizations should address how they plan to incorporate big data into the everyday workflow of clinicians, financial staff and other healthcare stakeholders for organizations to:
Use predictive analytics against historical and external data to anticipate patient occupancy needs to adjust staffing levels to have the right care available at the right time.
Use science to determine with accuracy health trends in specific communities and take action to prevent costly
Determine patients’ risk of readmission before they are discharged to improve patient outcomes and reduce costs and penalties by nearly $70 billion.
Realize that for this insight to be effective, you must put this information into the hands of the clinicians and the patients in the format that fits their daily flow.
How can healthcare providers transition to ICD-10 as simply as possible?
Guest post by Alexandra Sewell, executive director, emerging markets, Comcast Business.
As the healthcare industry moves through 2014 and begins planning for 2015, several trends continue to dominate the healthcare IT landscape. Healthcare organizations are grappling with the explosion of Big Data and implementing strategies to achieve varying stages of meaningful use. The industry is working toward interoperability, mobility and improving data security – all while looking to control costs and provide quality care.
New healthcare technologies hold great promise to improve both access to and quality of care, but they are in varying stages of adoption and federal approvals. This is leaving healthcare organizations and their IT directors searching for flexible solutions that can address current and future technologies.
Unfortunately, the industry’s approach to how technology is sourced, implemented and integrated as a business strategy is fractured. Many vendors offer different approaches to today’s healthcare technology challenges, but very few offer total solutions.
With that said, some technology is taking hold, such as digital hospital rooms, virtual medicine kiosks and mobile e-health devices, which allow physicians and other clinicians to monitor, diagnose and treat patients from remote locations. PACS imaging, electronic health records (EHR) and other data can now be shared within the entire healthcare ecosystem – from patients and clinicians to pharmacists and payers, and this is progress. But it’s been slow to take shape and there are still many questions to be answered.