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Reducing opioid abuse by knowing the right patient

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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.

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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.

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

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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.

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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.

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

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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.

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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

How Opioid Abuse Exposes Hospitals

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How Opioid Abuse Exposes Hospitals

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.