Reducing opioid abuse by knowing the right patient

July 16, 2018/12723 views / 0 Comments/in AI, Analytics, Artificial Intelligence, Big Data, BLOG, Data Integrity, data mining, Health Care, healthcare, Medical Record Mix-ups, Patient Errors, Patient Identification, Patient Matching, Patient Outcomes, Patient Safety, population health, precision medicine, Predictive Analytics, value-based care /by Michael Trader

The US is enduring a massive opioid abuse epidemic. Not only are they widely prescribed, but prescription opioids are now more widely abused than street drugs. If we look at the anatomy of the opioid crisis, it is genuinely frightening. In 2016, 116 people died each day due to opioid overdose, resulting in more than 42,000 fatalities in a single year.

The question is, why is this happening? How are 11.5 million individuals misusing prescription opioids? How is it that each year, 2.1 million people misuse opioids for the first time? It seems that, at present, there is no clear path to stunting this epidemic. Opioid abuse is already costing the US economy more than half a billion dollars annually.

How did we get to this point?

Since the 1990s, the pharmaceutical industry started pushing opioids and assured doctors that these drugs were safe. Consequently, doctors began widespread prescription of these drugs. However, blaming the pharmaceuticals industry and doctors alone ignores many other pertinent factors.

There have been many changes regarding the prevalence of various diseases over the last three decades. Slowly and steadily, medicine has become dominated by chronic and painful health conditions. It is estimated that one-third of the U.S. population or 100 million Americans are living with a chronic and acute pain condition. Among them, one-fifth are living with moderate to severe pain. Considering these statistics, it follows that opioids would be widely prescribed. However, 8-12 percent of those prescribed opioids result in patients developing an addiction.

Opioid misuse is not just limited to those living with painful conditions. Many of the prescribed opioids end up in the wrong hands. Many addicted to opiates hide their identity or medical conditions and visit various clinics under different aliases. For doctors, it is challenging to identify the right patient.

How can we reverse the epidemic?

To bend the trend downwards, efforts must be implemented at every level. At the community level, we must educate the public and raise awareness about the health risks of opioid abuse. Policymakers should advance legislation to address the problem. Above all, there is a need to change the way medicine is practiced; healthcare providers must take higher precautions at the clinical level.

Clinicians cannot and should not deprive people in pain from drugs that can bring them needed comfort. However, big data and technology can assist them in differentiating between the right patient and the wrong one. This is where RightPatient can play a vital role. Powered by artificial intelligence, the platform can help clinicians to thwart medical identity fraud and ensure that a patient’s complete and accurate medical history can be retrieved.

By recognizing the correct patient, clinicians can better understand the validity of patient complaints along with a patient’s disease history. When and where was the patient last prescribed an opioid? Did the patient rightly identify himself/herself?

RightPatient can be one way to prevent opioid abuse.

How Can You Protect Your Investment in a Population Health Solution?

February 9, 2018/13233 views / 0 Comments/in AI, Analytics, Artificial Intelligence, Big Data, BLOG, Data Integrity, data mining, Health Care, healthcare, Medical Record Mix-ups, Patient Errors, Patient Identification, Patient Matching, Patient Outcomes, Patient Safety, population health, precision medicine, Predictive Analytics, value-based care /by Mizan Rahman

Healthcare in the U.S. is going to see a paradigm shift in the next five years that will move it from a fee-for-service (FFS) payment model towards a value-based model. Simply said, those who produce better results and improve patient quality of care at lower costs will reap higher dividends. This shift will require better use of technology and significant changes to many platforms and their capabilities, including more investment in big data, analytics, and patient matching systems. These investments in population health management technologies will provide the real-time information needed to make more informed decisions.

Population health solutions play a critical role in moving healthcare from a treatment-based to a prevention-based model. These platforms enable providers to better prepare for patient-reported outcomes, provide data regarding social determinants of health and activity-based costing, and match extracted data outcomes with the right patient.

Current state of U.S. healthcare

The U.S. spends more on healthcare per capita than any other nation in the world but fails to produce better results for life expectancy and other health outcomes. Moreover, U.S. taxpayers fund more per capita on healthcare (64%) than those in other countries, including those with universal health programs.

These facts suggest that encounter-based medicine might be contributing to sub-optimal results in the U.S. and there is a need for change. That change is prompting the rise of population health management and data analytics technologies.

The population-based model is based on aggregating patient data across various health information resources, forming a comprehensive, longitudinal health record for each patient, and leveraging analytics to produce insights that clinical teams can use to improve care and lower costs. In addition to health and financial data derived from electronic health records (EHRs) and medical claims, information such as a patient’s socio-economic status, personal support network, and habitat conditions can be useful in building preventative care strategies.

For example, a patient diagnosed as prediabetes would be classified as high-risk in an encounter-based model. However, this does not take into consideration the patient’s lifestyle and behavioral patterns. Many prediabetics can avoid developing diabetes by modifying habits such as diet and exercise. Patients who smoke, abuse drugs, or have a sedentary lifestyle are much more at risk of developing the disease. Identifying these genuinely high-risk patients requires access to accurate data that is linked to the correct record.

Challenges in moving to a population health solution

At present, a tremendous amount of patient data is available but it is not unified – it exists within different institutions and across various platforms. Thus, the available information is very difficult to match with the right patient (if not impossible in some cases) and such data has little practical value. Population health solutions need a system that can match patients with their available data and provide information on the best recommendations for preventative care, helping to improve outcomes and save resources.

Therefore, the most important variable in extracting value from a population health solution is ensuring that a patient’s captured data is matched to the correct record. Better data warehousing and mining capabilities will serve no purpose if healthcare providers lack the ability to match the output with the right patient. At present, not only do patient identification issues exist within a single healthcare institution, but these issues become even worse when patient data is exchanged across multiple systems, with error rates rising to 60%.

Failure to properly identify a patient means loss of historical medical history, social indicators, financial information, medications, allergies, pre-existing conditions, etc. – vital information that puts the patient and healthcare provider at greater risk. These data integrity failures can significantly dilute the efficacy of population health initiatives.

In fact, the transition from fee-for-service to value-based healthcare is only going to work if healthcare entities invest in patient matching technology alongside their investments in big data and analytics platforms. These investments should go hand-in-hand since patient matching errors can have such a substantial impact on data quality.

Population health management is among the top six categories in healthcare that are attracting investments from venture capital firms. Other segments include genomics and sequencing, analytics and big data, wearables and biosensing, telemedicine, and digital medical devices.

Thus, the industry is investing in technologies that will play a significant role in value-based care and population health management. However, the success of any population health initiative depends on the right patient being identified every time so that medical records and the corresponding patient data are not mixed-up. Considering the data fragmentation that exists in healthcare and lack of standards around patient identifiers, AI-based systems like RightPatient are the only way to ensure reliable identification of patients across various data platforms and maximized investment in population health management.

Value-Based Care: A Patient-Centered Approach Requires Knowing Your Patient

January 15, 2018/11365 views / 0 Comments/in Analytics, Big Data, BLOG, Data Integrity, data mining, Health Care, healthcare, Medical Record Mix-ups, Patient Errors, Patient Identification, Patient Matching, Patient Outcomes, Patient Safety, precision medicine, Predictive Analytics, value-based care /by Mizan Rahman

Aspirin, penicillin, monoclonal antibodies, interventional cardiology, and genome editing have undoubtedly revolutionized medicine. However, while all of these have been breakthroughs in the field of medicine, not much has changed in the way that doctors do their jobs. Patients visit their doctors, the doctors diagnose, they recommend tests, they prescribe drugs, and they are compensated according to the volume of work done or the number of procedures performed.

If medicine is to progress in the 21st century, things have to change at every level, including the way that doctors work and receive compensation, the way they identify the right patient, and the way that patients are treated.

The long-awaited system that is going to change the way doctors work and are compensated will soon become a reality. This new system is called value-based care.

Value-based care is about compensating doctors according to outcomes. This encourages more personal attention to patients and transitions the healthcare system from cure-based to preventive medicine. It is a system in which doctors receive a higher level of compensation for either better outcomes from procedures or enabling patients to avoid health-related problems altogether.

There are several benefits of a healthcare system where the right patient gets the right kind of care.

Value-based care can save patients a lot of money. Putting aside the historical projections of healthcare inflation, the U.S. is also facing major epidemics of chronic, non-communicative diseases like diabetes, high-blood pressure, and cancer. It is no secret that many of these ailments are preventable with timely intervention and/or the correct behavior. Value-based care creates an environment where doctors can help patients to avoid these diseases by intervening at the right time. A doctor would identify the right patient to design a prevention plan before a disease can manifest where things become more complicated and expensive.

Once the right patient, a patient with a high risk of developing a chronic illness, has been identified, the doctor would be encouraged to spend more time with her, teaching her to take better care of herself so that complications can be avoided. There would be a reward system for identifying the right patient and taking timely preventative measures. It would also result in higher patient satisfaction.

A value-based care system would also lower drug costs. Historically, manufacturers decide the price of their medications without taking into consideration the value that a particular drug has in terms of its effectiveness and overall patient wellbeing. A value-based system would also encourage the development of personalized medicine where treatment plans and even pharmaceuticals can be tailored to specific patient needs.

The backbone of the value-based care system would be patient identification and data mining. Many are already demonstrating why medicine should incorporate more data-based modeling to augment physician decision-making. Data mining helps doctors and the healthcare industry as a whole to better understand the outcomes of various therapeutic approaches. Ultimately, it can help to create the right kind of individualized solution for the right patient.

Unfortunately, realizing optimal results from data mining and value-based care has its challenges, especially as healthcare organizations start mining data that has been accumulated over long periods of time. On average, at least 8% of hospital patient records consist of duplicate data. Thus, an intelligent way to sort out these duplications and identify the right patient is desperately needed.

It is stated that value-based care is about the right patient getting the “right care, in the right place and at the right time.” Instead, the maxim should be, “RightPatient® enables the right care, in the right place, at the right time.”

RightPatient® guarantees that a patient medical record is never mixed up with another record and the hospital ecosystem will always recognize the patient with the help of cognitive vision. Mistakes from common patient names, fraud, human error and other issues are always prevented.

As we all know, chains are only as strong as their weakest link. In many hospitals or medical institutions, there is an urgent need to strengthen this weakest link throughout the entire system – overcoming the errors of false identity and data duplication with RightPatient. Only then can the benefits of value-based care and data mining be fully realized.

How Opioid Abuse Exposes Hospitals

December 20, 2017/9722 views / 0 Comments/in AI, Analytics, Artificial Intelligence, Big Data, BLOG, Data Integrity, Health Care, healthcare, Machine Learning, Patient Identification, Patient Outcomes, Patient Safety, Predictive Analytics /by Michael Trader

Whenever I’m talking to a healthcare provider about RightPatient, the topic of “frequent flyers” inevitably arises. For those who might not be aware, frequent fliers are patients that use different aliases to obtain healthcare services. It’s estimated that between 2-10% of patients arriving at the emergency department (ED) provide some kind of false or misleading information about themselves. Typically, these patients are lying about their identity to obtain prescription medications, and most of these are for opioids.

Since these patients lie about their identity or demographic information, hospitals often end up writing off a considerable amount of money for their services – up to $3 million annually on average. Aside from these financial losses, frequent fliers also pose other risks to providers that are associated with patient safety and quality of care. Why? Because they also frequently lie about prescription drug use or addiction.

What’s worse is that this behavior is not limited to frequent fliers. Any patient can lie about their addiction. Many of these patients lie about their addiction to opioids, specifically. As we all know by now, the U.S. has a serious problem with opioid addiction, a crisis that killed over 33,000 Americans last year. This crisis has no rules or boundaries, and does not seem to select for a particular demographic. Anyone is susceptible to getting hooked on opioids because they are so addictive.

The opioid epidemic has far-reaching consequences that extend beyond the health of the patient; however, in the ED, this is the primary concern of a clinical team. Considering the circumstances, this question seems relevant – “how can healthcare providers ensure high quality of care when patients lie about their identity and/or drug use?”

RightPatient can play an important role in helping to answer this question. Our AI platform can accurately recognize the patient and offer key clinical insights by detecting patterns in the patient’s appearance over time. Clinicians won’t need to rely on the ~~words~~ lies coming out of a patient’s mouth, patients with no ID, or expensive tests. RightPatient automatically knows who the patient is and whether or not they are at risk of opioid abuse.

ED nurses who suspect a patient of abusing opioids will typically search the patient’s belongings to make sure they aren’t prescribed something that could cause an adverse event or even kill them. Unfortunately, the human eye, clinical intuition, and patient reliability have many shortcomings. Luckily, RightPatient can augment clinical diagnostics with cognitive vision to help fight the opioid epidemic and save a lot of lives and money in the process.

Data Personalization in Healthcare

December 10, 2017/7335 views / 0 Comments/in Analytics, Big Data, BLOG, Health Care, healthcare, Patient Identification, Patient Outcomes, Patient Safety /by Mizan Rahman

Personalization in healthcare – everyone talks about personalized medicine, how about data personalization?

One size does not fit all. Thus, medicine is seeing a shift from a standard model of care to a personalized model of care. The emergence of cloud computing, wearables, machine learning, and continuous progress in data management has made the delivery of personalized medicine more possible. Personalized data along with predictive analytics would change the way medicine is practiced today. It has the power to create a proactive health system that can help to address diseases at their earliest phase. Personalized data will help to craft medical solutions “especially for you,” rather than a single solution for all. It would also help consultants to free up time so that they can concentrate on developing a lasting and trusting relationship with patients.

When the human genome was first decoded, there was tremendous excitement about the ability to predict diseases and provide personal health solutions; however, soon it became evident that our overall health cannot be determined by analyzing small snippets of our DNA, as valuable as they might be for understanding specific risks. Medical or health-related decisions cannot be made in the absence of better personal data or a more holistic understanding of the person being treated.

With the help of personalized data, it would be possible to shift from the so-called model based on diagnosis and treatment to one of early disease detection and even a predictive model of medicine, along with personalized solutions.

Traditional medicine has depended not only on the phenotype and genotype data but other variables as well – a personal relationship with the patient, understanding patient lifestyle issues, surroundings, life events, social and family conditions, and much more. Data personalization can help to bring back that edge to automated systems through the customization of data. Data personalization is about delivering the right information about the patient, to the right person, at the right place, at the right time, in the right way. More than ever before, this is now conceivable due to better availability of personal data, personal devices, services, and applications.

Data personalization would make it possible to create a reasoning engine that has the ability to predict and make recommendations by using personal data of the patient provided by various resources.

Data personalization can take personal medicine many steps forward by adding the human touch and predictive analytics.

Perhaps in making medicine personal and predictive, personal information is what seems to be missing. If included in the algorithm, it would surely make predictive analytics more accurate and dependable. Personalized data would help to serve patients in the best possible way by shifting focus from merely disease determination to prevention, timely intervention, and better treatment.

Data personalization should not be taken as something new in medicine; in fact, it is a more natural way of providing health services, and closer to the traditional practice of medicine as it is about integrating the psychological, behavioral, and other measures that have become possible due to improvements in technology.

Combining the human biology with existing knowledge of epidemiology and clinical medicine would result in more personalized care. It is more like giving a human touch to the technology – something that has been a characteristic of traditional medicine, supporting the notion that doctors know their patients far better when a closer relationship is established. Thus, personalized data can augment that missing human factor in modern practice.

As more electronic personal data becomes accessible, systems become more intelligent. Having better learning capabilities and better availability of personalized data would revolutionalize the way we provide healthcare.

How Big Data is Changing Medicine

July 24, 2017/5866 views / 0 Comments/in Big Data, BLOG, Guest Blog Posts /by John Trader

The following guest post on big data in healthcare was submitted by Chris Saviano.

Big Data is one of those buzz terms you’ll see all over the internet. Something about it sounds slightly sinister, like Big Tobacco. But Big Data is more innocuous: it’s just a term used to define large amounts of data. It can encompass any sort of data coming in, from marketing and demographics data to stock ticker data. In the terms of healthcare, that will mean electronic medical records data, aggregated research and payer information, to name a few sources. And this Big Data is changing medicine in a big way.

Big Data is more than just a buzzword in healthcare – it is fundamentally changing care delivery as we know it.

Improved technology

Monitors themselves are changing, thanks to Big Data. CNBC reported on a tiny heart monitor patch that can generate 30,000 pages of data on a patient’s heartbeat, and then distill it into a 15-page full report for physicians. The device is made up of a chip and two electrodes.

All of these data points are compiled into a huge database, which grows with each new patient the device monitors. The machine-learned capability gets smarter with each new addition. Then with each new set of data, that helps doctors diagnose faster.

Patient care streamlining

One of the more noteworthy ways Big Data is changing medicine is through better patient care, the heart of any good medical facility. Large amounts of data collected from patients can help doctors educate patients during treatment decisions. Having a wider set of data available helps doctors tailor solutions to each patient.

One of the biggest advantages of Big Data is that it offers a predictive model for patient outcomes. This can result in earlier diagnosis and reduced mortality from conditions like sepsis or congestive heart failure.

According to MapR: “A machine learning example from Georgia Tech demonstrated that machine learning algorithms could look at many more factors in patients’ charts than doctors, and by adding additional features, there was a substantial increase in the ability of the model to distinguish people who have CHF [congestive heart failure] from people who don’t.”

Increased security

MapR also reported on the security features of Big Data in healthcare. Predictive analytics help payers identify inaccurate claims and fraud. Big Data helps with this in that companies can go back into large messes of datasets for past claims and use machine-learning algorithms to detect patterns in fraud.

Key red flags in data include reusing services in short time periods, duplicate charges for healthcare across different hospitals at the same time and prescriptions filled at the same time in different locations. Through this system, companies can assign risk scores based on past behavior and find items of note in large seas of data that would have been impossible to find before.

Faster, more efficient breakthroughs

Big Data is changing medicine behind the front lines of patient care, as well. Researchers looking at gene variants made a search function for the huge sums of data they’ve pulled during gene research. The functionality is called MARRVEL (Model organism Aggregated Resources for Rare Variant Exploration) , but you can think of it as Google for the human genome. Researchers anywhere can also search the database in minutes.

Author Bio:

Chris Saviano is responsible for Business Development and Sales at PGM Billing and leads PGM’s product integration between proprietary cloud-based practice management software and integrated back office service operations.

What’s Happening With All Our Healthcare Data?

November 14, 2016/5249 views / 0 Comments/in Big Data, Guest Blog Posts, Health Care /by John Trader

The following guest post on big data in healthcare was submitted by Keri Lunt Stevens.

Big data — two little words that have monstrous meaning. Big data is a term for a data set that is so large or complex that a traditional data processing application can’t handle it. From every student’s transcript to every hospital’s patient records and even to your own personal social media exchanges, we’re all producing data. All of the time.

The rising use of big data in healthcare promises to fundamentally improve care delivery.

According to IBM, an American multinational technology company, big data is arriving from multiple sources at an alarming velocity, volume, and variety. To extract meaningful value from big data, we need optimal processing power, analytics capabilities, and skills. Most industries struggle with this because the challenges of capturing, storing, sharing, searching, securing and updating this big data are real.

But so are the benefits. With the help of predictive analytics, big data can be used to anticipate future behavior, spot trends, and foresee and even stop potential problems before they spiral out of control. In the healthcare industry, this could have a huge impact on patient care, privacy and more.

Control Epidemics

In a way, predictive analytics isn’t new to the healthcare industry. For years, quantitative data has been used to predict the likelihood of an infectious disease outbreak, including how the disease will spread and how to control it. Some of these formal methodologies include risk factor analysis, risk modeling and dynamic modeling. But while quantitative data has helped us so far, it hasn’t been enough. Currently, there are five U.S.-based outbreaks being investigated by the Centers for Disease Control and Prevention. Additionally, the Zika virus — for which there is no vaccine — continues to spread. According to a U.S. National Library of Medicine National Institutes of Health article written by Mark Woolhouse, we need to develop a more holistic framework that captures the role of the underlying drivers of disease risks, from demography and behavior to land use and climate change. For a complete picture, doctors and scientists need to be able to cipher through quantitative and qualitative data to make predictions and act accordingly.

Develop Personalized Medicine

The majority of the human body is made up of six elements: oxygen, carbon, hydrogen, nitrogen, calcium and phosphorus, which are divided into simple and complex chemical molecules. The most common medical treatment, drug therapy, is the biochemical interactions between those molecules and the ones in pharmaceutical drugs. And that’s great. This type of treatment has helped heal millions of people worldwide. But it isn’t enough. Too many people are suffering because their bodies aren’t responding to treatments — from depression to blood pressure to cancer treatments.

Using a big data predictive analysis approach to personalized medicine could reduce the financial, social and personal burden associated with the current trial-and-error approach, according to Kateryna Babina, a medical scientist based in Australia. In an article on Budget Direct’s healthcare hub, Babina says integrating clinical, laboratory, lifestyle, behavioral and environmental factors into patient care is the key to helping provide personalized, targeted interventions to the right patients.

Keri Lunt Stevens is a freelance writer and editor who has worked in journalism and content marketing. Her experience ranges from healthcare trends and topics to finance and community news. Her work has been featured in a variety of print and online media outlets.

Big Data and Healthcare – The Present and the Future

November 11, 2016/5499 views / 0 Comments/in Big Data, BLOG, Data Integrity, Guest Blog Posts /by John Trader

The following guest post on big data and healthcare was submitted by Emma Lawson.

Healthcare is one of the largest and the most complex ecosystems that humans as a species have brought to life. With healthcare providers, payers, researchers, patients and additional entities that all have their own needs and agendas, it has grown into a world of its own, governed by its own rules and featuring a perpetual tug-of-war between the different interest groups.

One concept that might help make sense of all of this, provide benefits to all the interested parties and lead to a more stable ecosystem is big data. Big data has been around for some time and in certain fields it has found much use, but in healthcare, we are still seeing it take its very first steps.

The growing us of big data in healthcare promises to fundamentally change healthcare delivery.

Still, it has definitely become a part of the healthcare ecosystem and ind the future, it is more likely than not that it will become one of its most prominent parts.

Big Data Essentials

Big data is a relatively simple idea. It denotes sets of data that are extremely large, created very quickly (often in real time) and which are varied when it comes to their sources, classification and any other criteria you can think of.

Big data is, therefore, different from the more “traditional” data sets that are collected in limited amounts, from very specific sources and which are then organized in relational databases which feature a simple hierarchy and are easy to use.

A certain organization or a healthcare corporate entity might collect data from thousands of different medical practitioners, hospitals, government agencies, pharmaceutical companies, research institutions and patients themselves. They would then try to organize and analyze this data, all in order to come up with insights that would allow them to improve their services or save money.

Big Data Potential for Healthcare

In the perfect world, big data would hold almost limitless potential for everyone involved, from the healthcare providers to patients and even the payers.

For example, healthcare providers can gain much more precise and balanced insights by utilizing sets of data that are larger than any previously analyzed. In combination with data provided by patients themselves, healthcare providers would be able to dramatically increase the chances of full recovery and provide the ultimate healthcare for their patients.

When it comes to patients, smart use of big data would allow them to play a much more active role in their recovery. Furthermore, since their healthcare providers would operate with more data, the patients’ chances of recovery would dramatically increase. In short, more data would, in a perfect world, mean more positive outcomes for the patients.

When it comes to governments and health insurance companies, the use of big data can tighten their budgets by clearly indicating which treatments work and what are the minimum-invasive treatments and habits that would reduce healthcare expenditure.

Proper use of big data could also enhance the data security and other security concerns that the healthcare industry has to deal with.

A Few Current Examples

The best way to illustrate how big data can be used in healthcare is to take a look at a few ongoing projects and adopted practices.

For instance, Blue Shield of California has partnered with NantHealth in order to establish an integrated technology system which will allow for much more streamlined evidence-based care in a number of areas.

Kaiser Permanente has also implemented a system, called HealthConnect, which enables data exchange across innumerable medical facilities through the use of electronic records. Among the early results of HealthConnect are improved cardiovascular disease outcomes and more than a $1 billion saved in lab tests and office visits.

The National Institutes of Health and the National Patient-Centered Research Network have both launched certain initiatives that would allow for a more standardized collection, storage and analysis of big data, which will promote its use in healthcare.

What the Future Holds

While certain involved parties are already doing great things with big data in healthcare, the future is where we should look. The main reason for this is that big data applications are still limited by the lack of experts, certain security issues, and the chaotic nature of the data itself, among other things. Once these problems become the past, big data will definitely become one of the most prominent concepts in healthcare and its advancement.

We are already seeing certain steps being made in the right direction, with hybrid data models which combine the volume and the variety of data with the more structured nature of relational databases. Also, there are some companies that have started utilizing dark data in their data analysis, like Panorama for example. Dark data entails data so chaotic and huge that the standard big data models cannot handle it.

With the proliferation of sensors, wearables and other devices that will provide additional data coming from patients themselves, the amount of big data and its usefulness will only grow.

Closing Word

Big data has already begun to influence healthcare. Barring any catastrophic events, it will become an inseparable part of healthcare systems around the world, helping everyone involved attain their goals more easily.

Above everything else, big data has already started saving lives and it is a trend that will continue.

Emma Lawson is a passionate writer, online article editor and a health enthusiast. In her spare time, she likes to do research, and write articles to create awareness regarding healthy lifestyle. She also strives to suggest innovative home remedies that can help you lead a quality and long life.
Twitter @EmmahLawson