Reducing opioid abuse by knowing the right patient

July 16, 2018/13575 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, 2018/14093 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.

How RightPatient Prevents Chart Corrections in Epic and Other EHRs

January 29, 2018/12128 views / 0 Comments/in BLOG, Chart Corrections, Data Integrity, Health Care, healthcare, Medical Record Mix-ups, Patient Errors, Patient Identification, Patient Matching, Patient Outcomes, Patient Safety, value-based care /by Michael Trader

I’ve visited enough of our customers to know that hospital emergency rooms and free-standing EDs can sometimes be chaotic environments. Unlike most outpatient registration areas, patients who arrive to the ED do not have scheduled appointments and often go through a triage process with a nurse where they are “arrived” within the electronic health record (EHR) system. This is essentially a quick registration that begins the documentation of a patient’s visit information on his/her medical record. Unfortunately, this process often results in what are known as chart corrections.

As one might imagine, a clinician’s primary focus is on the health and safety of the patient. Nurses that triage patients are trying to enter patients into the EHR system so they can receive the appropriate care as quickly as possible. Unfortunately, data entry errors during this process are commonplace. For example, EHR system users may create a “John Doe” or “Jane Doe” medical record if they cannot properly identify the patient. Or, users may mistakenly select the wrong record because it shares a similar name with the patient in need of care.

When EHR users select the wrong patient medical record, all subsequent information pertaining to that visit is entered into that record (sometimes referred to as a medical record “overlay”). This is a data integrity failure and results in data entry errors that need to be resolved with a chart correction. So, a chart correction in the Epic EHR or other EHR systems is the process of fixing a “wrong chart entry” or overlay record that was caused by a patient identification error.

Wrong patient, wrong record data integrity failures within the EHR system can have disastrous consequences. At best, the healthcare provider must spend internal Health Information Management (HIM) resources to perform chart corrections and resolve medical record overlays, costing $60-$100 per hour for an average of 200 hours per overlay record. At worst, wrong patient errors can affect clinical decision making, patient safety, quality of care, and patient lives. This is why organizations like AHIMA have strongly advocated safeguards that healthcare providers can use to prevent medical record mix-ups, improve data integrity, and reduce the risk of adverse events.

RightPatient is the ideal safeguard to prevent wrong patient medical record errors and chart corrections within Epic and other EHR systems. The AI platform uses cognitive vision to instantly recognize patients when their photo is captured and automatically retrieve the correct medical record. This becomes a seamless module within EHR system workflows so there is no disruption to users.

Customers like University Health Care System in Augusta, GA are effectively using RightPatient to reduce chart corrections in Epic. In fact, UH saw a 30% reduction in Epic chart corrections within months after implementing RightPatient.

Healthcare providers using RightPatient to capture patient photos significantly reduce their risk of data integrity failures. This enhances patient safety and health outcomes while reducing costs – important goals in the age of population health and value-based care.

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

January 15, 2018/12191 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 Big is the Patient Mix-up Problem in the U.S.?

January 11, 2018/12437 views / 0 Comments/in AI, Artificial Intelligence, BLOG, Health Care, healthcare, Medical Record Mix-ups, Patient Errors, Patient Identification, Patient Matching, Patient Outcomes, Patient Safety, Privacy /by Patrick May

Hollywood has created several films featuring a person that was wrongly informed about cancer or another fatal disease with the patient being told that they only have a few months/days left to live. Upon hearing this news, the patient goes on a spending spree and adventure only to discover in the end that things have been mixed up. This might make for a great movie but in the real world, if such a patient mix-up happens, the outcomes may be far worse.

But just how frequently does this medical record mix-up problem happen in real life?

It seems that the problem of so-called mistaken patient identity is big enough to cause serious problems – something that is very evident from the article published in the Boston Globe, reporting 14 cases of mistaken identity.

Reports indicate that medical errors due to patient mix-ups are a recurrent problem. Consequently, a wrong person may be operated on, the wrong leg may be amputated, the wrong organ may be removed, etc. In fact, CNN reported that in 6.5 years, in Colorado alone, more than 25 cases of surgery on the wrong patient have been reported, apart from more than 100 instances of the wrong body parts being operated on.

It would be challenging to estimate the true total number of patient mix-ups simply because the vast majority of them go unreported until something untoward happens. Even in cases where complications do occur, most medical organizations would not be eager to publicize them.

Today, it is widely accepted that medical errors are the third largest killer in the U.S.; that is, far more people die of medical errors compared to diseases like pneumonia or emphysema. It is now estimated that more than 700 patients are dying each day due to medical mistakes in U.S. hospitals. This figure clearly indicates that medical errors often occur even though a fraction of them will have fatal outcomes. It also tells us that cases of patient mix-ups may be shockingly high and indeed underreported.

Though several thousand cases of mistaken patient identity have been recorded, it remains the most misunderstood health risk, something that hospitals barely report, and an outcome that patients do not expect to happen.

The U.S. healthcare system is extremely complex, making it challenging for a single solution to resolve this issue. There have been lots of efforts to implement a unique identity number for each patient (a national identifier) but political roadblocks have proven difficult to navigate. The chances are bleak that any such national system would be created, as patients remain profoundly worried about the privacy of their data.

At present, perhaps the best option is that each hospital finds its own way to solve this problem by developing some internal system to make sure that patient mix-ups don’t happen. Or, a better idea is to leave this task to the professional organizations that specialize in the business of improving patient identification. The RightPatient® Smart App is a perfect example of an innovative solution that is powered by deep learning and artificial intelligence to turn any device like a tablet or smartphone into a powerful tool to completely eliminate the problem of mistaken patient identity.

Technological solutions are often meant to augment human efforts, not to replace them. Here are some of the ways to avoid patient mix-ups:

Always confirm two unique patient identifiers within the EHR (Electronic Health Record), like patient name and identity number. Though this is a standard practice, many mistakes still occur due to similar first or last names. Thus, an app like RightPatient can help to eliminate the chances of such an error.
Two identifications should be used for all critical processes.
There must be a system to alert staff if two patients have a similar first or last name.
Avoid placing patients with similar names in the same room.

Although patient misidentification and medical record mix-ups continue to plague the U.S. healthcare system, there is hope to address this serious issue with solutions like RightPatient. Now, we just need healthcare providers to make this a priority and take action.

How Opioid Abuse Exposes Hospitals

December 20, 2017/10529 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, 2017/8050 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.

The Top 6 Reasons to Visit us in Booth #1252 at 2017 HFMA ANI

June 21, 2017/10574 views / 0 Comments/in BLOG, Events/Conferences, healthcare, Mobile, Patient Identification, Patient Outcomes, Patient Safety, Revenue Cycle Management /by John Trader

Visit us in Booth #1252 at the 2017 HFMA ANI Conference and learn more about the benefits of biometric patient identification.

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

If you are heading to Orlando this weekend for the HFMA show, please take a moment while you are walking the Exhibit Hall to visit RightPatient in Booth #1252. We will be on hand offering demos of our biometric patient identification solution and Smart App that converts any smartphone or tablet into a powerful patient recognition device. Not convinced? Here are the top 5 reasons why RightPatient is a must stop while navigating the exhibit floor:

See the RightPatient Smart App – As we just recently discussed on this blog, the RightPatient Smart App provides healthcare organizations the ability to extend the power and reach of our patient identification solution to any smart device equipped with a camera. Using augmented reality and deep learning to identify patients, the Smart App is an ideal tool for clinicians to quickly and accurately identify unconscious patients, verify patients prior to medical procedures, improve patient safety, and securely capture and share clinical images.
Learn How to Humanize Health IT – As you may already know, the RightPatient patient ID solution captures a high resolution image of the patient’s face and then links that image to their medical record. Several of our existing customers have provided feedback on these patient photos via their clinicians who say that these pictures serve an important clinical purpose and are helping to improve the patient experience. These patient photos are helping clinicians feel more comfortable and better connected to the patient often serve as reminders about the clinical context of the patient. Learn how RightPatient is helping healthcare organizations across the country become more patient-centered than ever before.
RightPatient is a Finalist in CHIME National Patient ID Challenge – We think this is kind of a big deal. Being named a finalist in this important initiative that could very well change the face of patient identification in healthcare as we know it, we recognize that being named a finalist is a true testament to the validity of our solution. It’s worth mentioning that of the finalists named, RightPatient is the only vendor that has a solution already being used in healthcare by many hospitals throughout the country. Stop by and learn why our solution was named a finalist in the Challenge. Go Team RightPatient!
Safeguarding PHI – Cybersecurity has become quite a hot topic in healthcare as we have witnessed one hospital system after another falling victim to attacks and rushing to bolster their security defenses. We are elated to see the increased emphasis placed on protecting networks but what about taking the same steps to protect data? Network security and data security are two separate areas and any upgrade to cybersecurity defenses simply must be met with commensurate upgrades to safeguard protected health information (PHI). After all, the digitization of healthcare has opened many new doors to access PHI (e.g. – patient portals, mHealth apps, telemedicine), shouldn’t healthcare organizations be proactively taking measures to implement patient ID solutions that can secure it?
Learn about our SaaS model – With our software-as-a-service (SaaS) model, healthcare organizations can implement the patient ID platform for a small monthly fee that includes software, hosting, and support. And don’t worry. Our artificial intelligence system runs in the secure, HIPAA-compliant RightPatient Cloud to minimize deployment and IT costs. What could be better? Stop by the Booth to learn more.
Accurate Patient ID Helps Revenue Cycle Management – Since revenue is a dominating theme of the conference, we thought we would mention that it is a proven fact that increasing patient identification accuracy will help prevent duplicate medical records, reduce denied claims, and improve the revenue stream. Stop by Booth #1252 and learn how you can improve revenue cycle management through an investment in RightPatient.

Skimming the agenda, the HFMA staff has again assembled a very compelling, highly informative conference packed with outstanding curriculum. Don’t forget that the Exhibit Hall is a treasure trove of education too, and we encourage you to take time and stop by to see why our patient identification solution is dominating the healthcare industry.

Take a moment to watch this short testimonial video from George Ann Phillips, Administrative Director of Revenue Cycle for University Health System in Augusta, GA:

Drop us an email at: sales@rightpatient.com to pre-schedule an appointment or drop in and see us at your convenience. We can’t wait to see everyone in Orlando!

Brad Marshall is a Senior 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.

What You Need to Know About Your Patient & What They Need to Know About You

January 25, 2017/5257 views / 0 Comments/in BLOG, Guest Blog Posts, Patient Identification, Patient Outcomes /by John Trader

The following guest post on patient relations in healthcare was submitted by Hannah Whittenly.

Understanding your patients and making sure they know you helps establish a relationship of trust. This should decrease or eliminate misunderstandings and errors. The following are just a few things you need to know about your patients and what they need to know about you.

Establishing an environment of trust helps decrease or eliminate misunderstandings and medical errors in healthcare.

What Doctors Need to Know

Patient Identification

Patient identification is of the utmost importance for a doctor. It helps ensure that the patient’s identity matches the treatment or service. Keep in mind that the proper procedure for patient identification is for your staff to ask patients to state their name and date of birth. Identification issues might occur when patients have hearing issues, so it is wise to be on top of this problem.

Patient Data

Recording patient data can help your practice and the medical industry as a whole. Understanding a patient’s medical history and tracking a patient’s health, medications, and lifestyle helps doctors come up with a customized plan to reduce the chance of errors. This type of data helps establish patterns for clients and may even help you reevaluate a plan to provide what your patients need the most.

What Clients Need to Know About You

Track Record

Patients should be aware of a doctor’s track record so that they can make an informed decision. This can be done by searching for the doctor’s name on the state’s medical licensing board website. There may be some information on the doctor and his or her track record. It might be a good idea to type in the doctor’s full name and location in a search engine to find out more information about the doctor. A patient can rest easy knowing that a doctor’s track record is clean, and it should make it easy for the client to trust you.

Education

Most state medical licensing boards have information regarding a doctor’s education, but this is not always the case. There are some instances when the state’s licensing board may not be updated. It might be a good idea to check out sites like UCompareHealthcare to learn about a doctor’s education. You can even check out the rest of the staff’s education. If you wanted to look up a nurse who may have participated in an online msn program, for example, you could look him or her up to see what their record and education history looks like and be more assured that they are the right nurse for you. Patients should be able to find out about the medical professionals that that they see pretty easily. This helps them feel more comfortable, and it makes it easier to make a decision about making you the doctor or nurse that they request on a regular basis.

All this information matters, and it is vital in building the right relationship. It also makes a difference in the type of care you provide to your patients.

Hannah Whittenly is a freelance writer and mother of two from Sacramento, CA. She enjoys kayaking and reading books by the lake.

Why Patient Photos Should Be Linked to Medical Records

November 9, 2016/10631 views / 2 Comments/in Biometric Hardware, BLOG, Facial Reciognition, Future of Biometrics, Health Care, Patient Identification, Patient Matching, Patient Outcomes, Patient Safety /by John Trader

The following post on why patient photos should be added to medical records to improve patient safety was submitted by Michael Trader, President and Co-Founder of RightPatient®

The Push to Increase Patient ID Accuracy and Safety

Achieving accurate patient identification in healthcare is an important catalyst to ensure safe, cost-effective care delivery. Although we believe that accurate patient ID should have received more attention and scrutiny parallel to the rapid digitization of the healthcare industry, the issue has finally been thrust into the spotlight by powerful organizations such as AHIMA, the ONC, and CHIME as something that must be solved in order for other mandates (e.g. interoperability, health information exchange, population health, etc.) to materialize.

Why aren’t more healthcare providers capturing patient photos during registration?

Many healthcare organizations have proactively addressed the lingering issue of accurate patient identification by implementing new technologies that supplement existing methods of obtaining demographic information, insurance cards, and proof of ID. The idea is to add biometrics as an added layer of identity protection, security, and identification accuracy by asking patients to provide a physiological token prior to accessing health data and/or medical services. Biometrics for patient ID has rapidly caught on as a proven method to prevent fraud and medical ID theft, improve data integrity, prevent duplicate medical records, and safeguard protected health information (PHI).

Patient Photos Should be Captured During Registration

Despite the rising demand for biometric patient identification to improve patient identification and increase safety, not all solutions are created equal. Healthcare organizations that invest in unilateral biometric patient identification solutions quickly discover that they do not have the ability to easily and automatically capture the patient photo during registration and subsequent visits. This is unfortunate as the photo plays an important role in patient safety and in driving additional value throughout the ecosystem.

In addition, capturing the patient photo with a web camera during initial registration is not enough. This method often produces very poor quality photos, adds an extra step to the process, and the photos cannot be relied upon for other potential uses, such as facial recognition to verify patient identities during remote encounters.

One important differentiator that should be considered when researching a biometric ID solution is whether or not it offers the ability to capture a high-quality patient photo and recognize the patient in a single step. Why?

Patient photos are proven to reduce medical errors.
Respected, influential healthcare organizations recommend including patient photos with their medical record.
Patient photos increase patient safety.
Photos can be used as a second credential for multi-factor patient authentication.
The photo serves as a visual reminder to the provider, thereby enhancing caregiver communication with the patient.
High-quality patient photos allow healthcare providers to leverage facial recognition for accurate patient ID when patient’s access PHI or services in non-traditional settings such as mHealth apps, patient portals, and telemedicine. This enables a holistic approach to establishing accurate patient ID because it addresses all points along the care continuum instead of a narrow approach that only covers patient ID at the point of service in a brick and mortar setting.
In areas like the ED where time is critical, utilizing a web camera and adding an extra step in the workflow is impractical and inefficient.

Criteria that Defines an Effective Biometric Patient ID Solution

In addition to the points mentioned above and the standard questions that should be asked when researching the adoption of a biometric patient ID solution, we recommend that healthcare providers seriously consider the unique value of a platform like RightPatient® that seamlessly captures patient photos and identifies patients in a single step during registration, subsequent visits to a medical facility, and other touchpoints along the care continuum. This establishes a concrete, two-factor audit trail of patient visit activity and identity assurance.

Verify that the biometric patient ID solution offers the following patient photo capture features:

Convenience – Is the patient photo capture process easy and convenient for patients and staff? Photo capture should happen simultaneously with capturing their biometric credentials and should be fast. Otherwise, you run into delays and registration roadblocks in areas like the emergency room where time is of the essence.
Seamless integration and functionality – Patient identification and photo capture should be a seamless part of EHR workflow and not require staff to sign in and out of applications or constantly toggle between applications.
Affordability – Biometric patient ID platforms that offer simultaneous photo capture should be flexible and affordable and offer a software-as-a-service (SaaS) model option.

Conclusion

Capturing patient photos to increase safety and reduce medical costs is not a new concept in healthcare, yet it has failed to become mainstream compared to other industries. In fact, according to a recent report from the ECRI, despite the proven research that photos increase safety and engagement, only 20% of existing providers currently use patient photos. 20%! Think about that in the context of other industries that have used customer photos as part of their routine identification security protocols for years: membership management (e.g. gyms, fitness clubs), banking and finance, retail, education, government — the list is long.

If other industries have relied on the use of photos to augment identification accuracy, why is healthcare so far behind the curve? It seems as if healthcare market conditions and current and future initiatives to improve delivery, achieve better outcomes, perfect individual and population health, and reduce the cost of care are setting the stage for technology that can quickly and seamlessly capture patient photos as part of the identification process. The question is, are you investing in the right solution to harness this power?

Michael Trader is President and Co-Founder of RightPatient®. Michael is responsible for overseeing business development and marketing activities, government outreach, and for providing senior leadership on business and policy issues.