Reducing opioid abuse by knowing the right patient


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?


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.

big data

How Big Data is Changing Medicine

big data

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

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.

the rising use of big data in healthcare

What’s Happening With All Our Healthcare Data?

the rising use of big data in healthcare

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

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 will improve healthcare delivery

Big Data and Healthcare – The Present and the Future

big data will improve healthcare delivery

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.

big data will improve healthcare delivery

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.
big data will make us healthier individuals

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


Biometric Patient Identification Implementation Should Be Higher On The Priority List


The following post was submitted by Brad Marshall, Enterprise Development Consultant with RightPatient®

As someone with a long track record of implementing enterprise IT solutions in healthcare, I frequently observe hospitals “shuffling the project deck” as they compare and contrast the merits and return on investment (ROI) of each initiative in order to determine which makes the most sense to allocate budget dollars. Does politics at times play a part in the decision of which technology projects eventually get approved? Yes, at times. Are there often misinterpretations of the value that an enterprise IT project can offer in both the short and long term? Absolutely. Do hospitals often place a high priority on implementing projects that in reality, should be pushed further down the list in lieu of the value that another project brings to the table? Definitely.


Are healthcare organizations evaluating the proper criteria to prioritize enterprise IT Projects?

Case in point: the implementation of biometrics for patient identification in healthcare. Although I am obviously biased towards this technology since I work for a company that has helped many hospitals throughout the world see the benefits of using it to increase patient safety, prevent duplicates and overlays, and protect patients from medical identity theft and fraud, it doesn’t alter the facts about how implementing biometrics before making a commitment to other competing enterprise software projects is something more healthcare organizations should consider. Why? Let’s look at some specific examples of alternate enterprise project implementations that would actually benefit and see performance improvements if a biometric patient identification project deployment took preference:

1. EHR Projects – Although the implementation or “switch” to another EHR vendor is perhaps one of the most complicated, time consuming, and resource-intensive enterprise software projects a healthcare organization will ever undertake, the implementation of a biometric patient identification system prior to embarking on an EHR project is a smart idea. Why?

Our experience has shown that there is always an uptick in duplicate medical records when you are initially implementing or switching to a new EHR system, primarily because staff are adjusting to new workflows and are less likely to catch duplicates the first few months. Since duplicate medical records present a direct threat to patient safety and a serious treatment error risk to healthcare organizations, their prevention should be priority #1 for any modern EHR system. 

I am not necessarily advocating the push for budget dollar allocation to biometric patient identification over an EHR project, however implementing a biometric patient identification solution before an EHR Go Live will make it more successful by immediately eliminating the possibility of creating duplicate medical records and overlays and prevent staff from making registration and patient identification mistakes while learning the new system.

2. Duplicate Medical Record “Clean-Up” –  Often times, I run across hospitals that may be evaluating the implementation of a “duplicate medical record clean-up” project prior to deploying biometrcis for patient identification. Without discounting the importance of purging duplicate medical records from any EHR database, the argument for why hospitals should consider the use of biometrics for patient identification is clear — healthcare organizations will successfully perform a “de-duplication” cleanup but continue creating duplicates until they implement stronger patient ID technology and will most likely have to do another cleanup down the road. 

Keep in mind that it only takes one – ONE – mistreatment at the hands of incorrect, missing, or incomplete medical data due to duplicates or overlays to result in harm, or possibly even death of a patient. Ask yourself, are you willing to assume the risk of medical errors to patients and the repercussions (which often can include hundreds of thousands, even millions of dollars in legal fees and compensation) of these errors for the short term gain of a “clean” master patient index (MPI)? Chances are, you aren’t willing to take that risk which leads to a stronger argument to implement biometrics for patient ID prior to launching a duplicate medical record clean-up initiative.

3. Big Data and Analytics – These are project priorities that perhaps perplex me the most, especially in the context of establishing higher data integrity when preparing to join a health information exchange (HIE), or as part of a merger that joins separate Integrated Delivery Networks (IDNs).  If a healthcare organization is seeking to allocate budget dollars to initiatives that advance data integrity, that’s good news. No one will argue that the healthcare industry simply has to better understand and find wisdom in the terabytes of data their systems possess to help advance the “triple aim” and deliver higher quality care, especially as more disparate networks are attempting to share data . However, the problem is that allocating budget dollars to these deployments is the quintessential “cart before the horse” mentality.

Instead of placing more emphasis on cleaning existing “dirty” data, healthcare organizations are rushing to the HIE table for fear of losing a seat or appearing indifferent to their patients and the industry wide push on sharing and making health data more accessible. What good is joining a HIE (or merging IDN’s) in the absence of technology that ensures that not only is the data you share clean and all medical data is properly attributed to the correct patient, but also guarantees that the data will STAY clean to give you the confidence that clinicians truly have a complete picture of a patient’s health and medical history when administering care. 

I’m reminded of a story that is a perfect illustration of why implementing biometrics for patient ID should take precedence over many other health IT projects, especially those that address data quality.

Years ago I worked for a local YMCA that had a leaky roof over the gymnasium. Each time it would rain heavily, staff would be scrambling to place buckets around the gym floor that would strategically catch the water leaking from the roof. The leaks would cause event and class cancellations, disrupt workout schedules, and generally leave paying members feeling a bit frustrated. YMCA management then made a decision to replace the aging, wooden gym floor with a new model that was built with a soft rubber substance – a radical new technology that was supposed lower the impact and strain of running on a hardwood surface. They then spent tens of thousands of dollars replacing the floor, and as you may have guessed, the next time a powerful storm came through, it leaked water all over the new gym floor. The irony in this situation of course is that management should have allocated the funding to fix the roof before they had the new floor installed.

As we continue to help the healthcare industry understand the advantages of implementing biometrics for patient identification, we understand that many healthcare organizations are not flush with cash to haphazardly allocate to any enterprise project that comes down the road. There are many mission critical projects that simply take precedence in the broader scope of improving the quality of care. Shouldn’t the deployment of biometrics for patient identification be one of them?

Brad Marshall works for RightPatient - the industry's best biometric patient identification solution.

Brad Marshall is an Enterprise Development Consultant with RightPatient®. With several years of experience implementing both large and small scale biometric patient identification projects in healthcare, Brad works closely with key hospital executives and front line staff to ensure project success.