What are health systems doing to improve the patient experience?

August 8, 2019/5247 views / 0 Comments/in BLOG, healthcare, Patient Experience, Patient Identification, Patient Matching, Patient Safety /by Matt Gibson

Unsurprisingly, there is a lot of competition present in the healthcare industry. To stay ahead, loyalty and patient experience are must-have attributes for any given healthcare provider. In some cases, healthcare can indeed be consumer-driven; for instance, take patient engagement. According to a recent study, patients are five times more likely to choose the health system with whom they have had positive experiences, rather than those who attempt to attract new patients with their marketing strategies. This study demonstrates that patient loyalty and positive patient experience are the characteristics to strive for, which is why healthcare providers are looking for strategies to improve the patient experience.

RightPatient improves the patient experience with accurate patient identification.

Thanks to many tools which are improving patient outcomes, patients today are anticipating effective and seamless patient experiences. However, these tools and strategies have to be patient-centric to be successful, as well as being competent enough to induce loyalty, and the most necessary characteristic is trust. Without trust, no health system can survive, let alone be successful, in the long run. Thus, healthcare providers are increasingly focusing on constantly revamping and helps to improve the patient experience.

Healthcare providers are improving the patient experience by focusing on the following.

Patient engagement technologies

Patient engagement is a buzzword recently, and everyone is pursuing ways to improve it within their health systems. Why is this so? Because it is one of the core characteristics which contributes to not only improving the patient experience but also assisting in patient retention. There are a plethora of solutions available in health systems under the umbrella of patient engagement, like health programs, surveys, participative courses, and so on. However, therein lies the problem – the solutions are many, and only a very few are effective as patients deem most as unnecessary and tedious. These are termed so by the patients because these tools’ functionalities are mostly unclear – many overpromise while delivering minimal benefits. To find patient engagement tools which are useful, health systems are diving deep into the technological side of the healthcare industry. Thus, active patient engagement is powered by technologies which help to connect the patients with their health systems seamlessly and assist the patients in leading healthy lifestyles.

Even though many would go for surveys, participative courses, and similar patient engagement strategies, research has shown that these are not very effective. Most of the time, patients are absentminded while answering, and as long as they do not see the potential benefits of these tools, they will not be interested in participating wholeheartedly.

On the other hand, there have been quite a few advancements in technologies which are helping to effectively engage with patients, such as patient retention apps like CircleCare. It is an app that helps patients stay connected to their health systems and doubles as a patient engagement platform. Once a healthcare provider registers with CircleCare, all it needs to do is refer the patient to download it and register. After that, patients can effectively engage in several ways with their health systems, and not only that, they can also work on leading healthier lifestyles. It can be used to track steps, schedule medicine reminders, record blood pressure, record glucose level, and exchange relevant health information with other individuals. It also helps them to stay connected to the physicians, so that if a problem arises, it can be solved outside the hospital’s premises as long as it is not critical. These functions help in cutting down hospital readmissions, as well.

Improving patient safety

Patient safety is currently one of the biggest concerns of the healthcare industry, especially those linked with patient identification errors. Recent surveys, statistics, and studies have shown that patient misidentification is a multibillion-dollar issue. Since the introduction of EHRs, things have gotten worse. To put things into perspective, the identification and correction of a single duplicate record cost around $1000, whereas if there are multiple records attached, it requires a whopping $5000 for any given health system! So, how are healthcare systems avoiding these patient matching errors? They are adopting technologies which are helping to eliminate these errors – solutions like RightPatient. RightPatient is a biometric patient identification system that utilizes iris scanning, thus making it easy to use for both the patients and hospital staff. It is also hygienic as it does not require physical contact and is also safe and accurate. The health systems using it are reporting that it helped to improve the patient experience, improved patient safety, and reduced denied claims. All of these lead to minimizing the losses incurred due to misidentifications significantly.

Another one added to the endless list of patient misidentification stories

August 6, 2019/6685 views / 0 Comments/in BLOG, EHR, healthcare, Patient Errors, Patient Experience, Patient Identification, Patient Matching /by Matt Gibson

Another day, another one added to the endless patient misidentification stories – makes the need for an effective patient identification solution like RightPatient even more urgent.

However, medical record mix-ups occurred even before EHRs were implemented. Years ago, a woman called Liz Tidyman’s parents decided to spend more time with their daughter and grandchildren, and thus moved close to their home. While they did so, they carried everything important with them, especially the hard copies of their medical records (EHRs did not exist back then).

RightPatient prevents patient misidentification and medical record mix-ups.

Exactly two days after the parents moved in with Liz, the father unfortunately fell. Since this has never happened before, the family was concerned, and they took him to a hospital for a checkup. While sitting in the waiting room, Liz took out her father’s medical record right on time, or else there would have been some grave consequences in store for the family. As Liz went through her father’s health record, she saw some abnormalities – there were numerous pages regarding the medical history of an entirely different person. The only thing Liz could associate with her father was the name of the other patient, everything else regarding the medical history was unrecognizable by Liz – the other patient had much more complex problems than her father did. At that instant, she took out those irrelevant pages about the mistaken patient and decided that she will always double-check such sensitive records, medical or otherwise, in the foreseeable future.

This was just one story, and that too before the introduction of EHRs and the mess they created. There are many such stories and incidents before and after EHRS which have created the billion-dollar problem – patient misidentification. These have also led to numerous patient misidentification stories, due to which everyone is taking it seriously now.

No patient, old or young, is safe from patient matching errors, but since the older ones are less comfortable with technology, they need to be more careful regarding their medical records. According to a healthcare official, one out of ten patients who have seen their records say that there are inconsistencies and demand corrections. This is not really surprising, given the number of problems we read about everyday regarding patient identification. For instance, some patients may see that inaccurate medical test results, treatment plans, or diagnoses have been attached to their medical records, raising doubts regarding what else is mixed up with other patients. However, this actually happened with Liz’s father as well.

When asked about kidney cancer, Liz’s father denied he had it. Afterward, Liz viewed the materials and realized that the mix-up was due to the name, and it was listed kidney cancer instead of skin cancer, the latter which her father actually had. Likewise, omissions, patient mix-ups, and duplicate records create all kinds of ruckus and devastate families, end the lives of individuals and cause losses in millions of dollars. This has been the scenario for years, and EHRs did not fix them, they only amplified the problems further.

Health systems have finally begun to address how serious of an issue patient misidentification can be, thanks to the endless pile of patient misidentification stories. While some hospitals are struggling with using only EHRs for patient identification, others are clamoring for an effective solution to end these patient matching errors. However, other advanced and forward-thinking healthcare providers opted for RightPatient – a biometric patient identification system. It uses the iris scanning modality to identify the patients. Once the hospitals enroll the patients with RightPatient, they can be recognized and matched with their records easily and conveniently, all it needs is a look at the camera – it’s that easy! Patient acceptance to RightPatient has been high as well since they perceive it as safe and hygienic – no physical contact required; thus, no risks of contracting any diseases. RightPatients helps in saving the lives of patients and reducing denied claims and ultimately saving money along the way.

Patient identification error causes yet another grave mistake

July 17, 2019/8205 views / 0 Comments/in BLOG, healthcare, Iris Recognition, Patient Errors, Patient Identification, Patient Matching, Patient Safety /by Matt Gibson

Another day, another mistaken patient identification error. However, it was a bit different in this case. Two sisters were informed that their brother was on life support and that is the premise of the whole fiasco.

The sisters, Rosie Brooks, and Brenda Bennett-Johnson received a call from an official that someone they believed to be their brother Alfonso was breathing with the help of a ventilator at Mercy Hospital and Medical Center of Chicago. However, the sisters stated that they didn’t talk much to their brother. The call started with someone enquiring about relatives of Alfonso, and Brooks replied that she was the sister, and then the official broke the news – that he was fighting for his life in the ICU, explaining that he was beaten quite severely, especially the face.

The Chicago police had found the man beaten to a pulp, and according to reports had neither clothes nor any means of identification on his body. A police spokesperson said that witnesses of the incident identified the injured man as one Elijah Bennett. Later on, he was rushed to the hospital and was on life support.

During his time in the hospital, as nobody came looking for him, the hospital staff had to take the help of the police in identifying him. The spokesperson said that their database had no “Elijah Bennett,” however, they did find “Alfonso Bennett.” The police later on handed over a picture so that the hospital could help identify any family members of the unfortunate patient. All these events led to the call to Brooks, yet another patient identification error.

When the sisters rushed to the hospital, they failed to identify the man as their brother Alfonso. However, CPD kept saying that it was their brother. According to the nurse, police used the help of mugshots to identify him, but due to budgetary issues, a proper ID could not be made.

However, the patient’s situation was worsening, and the sisters faced a challenging situation – whether or not to remove his life support. With immeasurable sadness, they had to sign papers stating that this man was their brother and to remove his life support, and as expected, the man passed away, unfortunately.

After this series of events, the story did not end. After the untimely death of the “brother,” the sisters started making preparations for his funeral, to give him a proper sendoff. Before they could carry out the planning, however, what happened next was a scene out of a dramatic movie – the brother, Alfonso, walked right through the front door of the house of the one sister! She shouted over the phone to her other sister, exclaiming that the brother they had thought was dead is very much alive and healthy and that it almost gave her a heart attack.

However, this newfound relief and happiness quickly turned into sadness, regret, and remorse – they realized that they had given the green light to end the life of a stranger, thinking it was their brother. They shared how they felt with the media and that they were extremely remorseful about deciding everything about someone unknown. However, the police, later on, identified the person with the help of fingerprints and started looking for his family. Everyone involved was deeply disturbed by this incident and thinking that there are no procedures or strategies to ensure such cases do not happen again in the future. However, this is not an isolated incident, as a very similar situation occurred in 2018.

Many people are worried that as these incidents are recurring, there is no way to avoid this. They are wrong. RightPatient would have helped the situation in reducing such errors. It is a biometric patient identification system which, and with the help of iris scanning modality, it reduces not only any kind of patient identification error but is also safe, convenient, and quick. Since no physical contact is required, all it needs is a glance from the patient for registration and matching – thus being hygienic and easy to use for both patients and physicians. All these features help to enhance patient safety as well as improve the patient experience, reducing claims by 35% and saving a lot of costs of healthcare systems in the process.

Hospitals are Prioritizing Patient Matching Accuracy

June 2, 2019/7891 views / 0 Comments/in BLOG, healthcare, Iris Recognition, Medical Record Mix-ups, Patient Experience, Patient Identification, Patient Matching, Patient Safety /by Matt Gibson

Patient identification has been haunting the healthcare industry since its inception. Using the existing practices in the industry, accuracy rates are significantly low and cannot be used to exchange health data effectively, as reported by officials from different healthcare systems such as hospitals and physicians. The industry is in dire need of patient matching improvement.

However, the above report is not the only one – other statements point towards the same conclusion of requiring patient matching improvement, as per the research brief from Pew Charitable Trusts. A study was conducted by Pew researchers along with Massachusetts eHealth Collaborative (MAeHC) that sought to identify the current situation of patient identification in the healthcare industry. They did so by collecting information from different healthcare executives with the use of interviews. Another aspect of the study was to identify how to achieve patient matching improvement. The sample of this study was healthcare experts and influential figures from various practices and sizes who served numerous patients in diverse regions all over the country.

A vast majority of the sample expressed the same view – patient identification and matching were quite inaccurate and desperately needs an overhaul, thanks to the increasing demand for interoperability.

Healthcare providers are now motivated to exchange more health data due to the recent CMS Promoting Interoperability program. That’s not all! CMS is also going to be granting incentives to accountable care organizations (ACOs) who will show savings through activities which support care coordination.

According to the Pew researchers, healthcare systems like hospitals and clinicians eligible for these programs need to exchange information with others so that all of the parties have the latest patient data from other various institutions.

The hospital officials stated that it is quite challenging to measure the match rates, resulting in their efforts being ineffective to examine and improve the patient identification rates. They also had difficulty providing a number when asked for the identification rates within their organizations. This was because many hospitals only keep a record of the duplicates identified through EHRs, whereas others do not know which files are relevant and which are unlinked. Thus, without knowing the actual number of correct matches, these healthcare systems cannot determine their match rates. Therefore, only the amount of misidentifications was provided by them, thus summarizing the research.

It was also identified that healthcare systems could easily match patient identities when asked by organizations they are in constant contact with. Both automated and manual processes are utilized to link records to the correct individual.

However, whenever it is an organization with whom the healthcare system is not in contact with regularly, match rates are inclined to be lower. This is because these unsolicited requests introduce more blockades because the healthcare system may not have a record of that individual, and the healthcare system uses automated processes for such applications. On top of that, the research also showed that urban areas require better identification rates compared to rural areas as not much-sharing activities take place in the latter.

Some healthcare executives also think that improved patient identification matching requires significant costs. However, many believe that biometric patient identification is the solution to improve matching rates and is worth the cost. Some hospitals are even utilizing iris scanning solutions like RightPatient to identify all their patients and pull their relevant data from their EHRs and show a significant change. They report that it is fast, accurate and improves the overall patient experience as well as speeding up the whole process and saving valuable time of the physicians so that they can concentrate on more critical tasks such as the patients themselves.

Reducing opioid abuse by knowing the right patient

July 16, 2018/13548 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, 2018/14065 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, 2018/12105 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, 2018/12164 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, 2018/12417 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.

Patient Identification Deployment Video: Community Medical Centers

June 2, 2017/6843 views / 0 Comments/in BLOG, healthcare, Infection Control, Patient Identification, Patient Matching, Patient Safety, Photo Biometrics /by John Trader

using-RightPatient-for-patient-identification-to-increase-patient-safety

Community Medical Centers implemented RightPatient using photo biometrics for patient identification to increase patient safety.

There are myriad reasons why a healthcare organization seeks to implement a biometric patient identification solution. It could be to prevent duplicate medical records. Or, perhaps to increase revenue cycle management efficiency and returns. Maybe it’s in an effort to better protect patient medical identities. Whatever the reason, there is one recurring theme that is a constant in all biometric patient ID deployments – increasing patient safety.

A desire to improve patient safety by ensuring accurate patient identification was an important underlying goal for Community Medical Centers (CMC) when they made the decision to invest in RightPatient. Staff at CMC knew that RightPatient was an important part of their overall strategy to prevent medical record mix-ups and protect patients from error.

The staff at CMC assembled a video overview of their RightPatient deployment providing insight into the enrollment process and factors that led to their implementation decision:

As explained in the video, RightPatient is an image-based patient identification solution – a non-invasive, hygienic (non-contact), and easy-to-use technology that instantly links a patient’s photo to their unique medical record. In the case of CMC and many other RightPatient customers, the software becomes a seamless module of the Epic EHR workflows through a low-level integration.

Backed by 15 years of experience in biometric technology, RightPatient remains the industry’s most versatile and scalable biometric patient safety system, leveraging a powerful cloud-based intelligence engine to recognize patients by simply capturing their photo.

Our thanks to the staff at CMC for creating this patient identification deployment overview video!

If you want more information or would like to see a demo of RightPatient, please contact us.