biometrics-helping-with-patient-identification

Another one added to the endless list of patient misidentifications stories

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

biometrics-helping-with-patient-identification

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).

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 misidentifications. These have also led to numerous patient misidentifications 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 misidentifications can be, thanks to the endless pile of patient misidentifications 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. 

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Patient misidentification cases are persistent as no industry-wide solution exists

Synchronizing medical information with the appropriate patients accordingly- how hard can it be, right? Well, it is precisely the opposite. It is easier said than done- patient misidentification cases are abundant. Since the introduction of electronic health records (EHRs), people thought that it would make the lives of everyone involved easier, but the reverse has happened. EHRs are filled with issues like misspellings, incomplete data, common names, outdated addresses, and so on, which overall leads to duplication of records. One of the consequences of all of this, according to a reputed organization, is the match rates being an average of 80%, sometimes even lower. It doesn’t sound all that bad, but it is the result obtained from the same healthcare provider the records have been created in! 

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One out of every five patients is likely to suffer from patient identification errors and become one of the many patient misidentification cases in the US. This is not the only problem, however. Patient matching errors are like a web created by spiders- trapping all the parties who are connected to patient identification- patients, healthcare providers, physicians, insurance companies, and so on. 

Patient misidentification cases are also synonymous with rising costs as patient misidentification is famous for being a persistent multibillion-dollar problem in the healthcare industry. A patient matching error costs a hospital around $2000, and if we look at the bigger picture, there are $1.5 million of denied claims for a single hospital in a given year. That is an outrageous amount of money which is completely wasted due to these errors.

As per the definition, patient matching refers to obtaining the accurate medical record for a given patient whenever necessary, to make informed decisions regarding the health of the patient. Healthcare professionals are frustrated that this is not what they experience whenever they are matching the patients with their records and are clamoring for something which will change the industry and generate accurate patient identification- something RightPatient is very good at doing. 

Let’s explore more in-depth into why patient matching errors occur. Patient matching is also reliant on the hospital employees who come into contact with it- they need to fill in the gaps for the new data, or else they might need to update changes in data like a surname, address, etc. to ensure accuracy. However, errors, in this case, maybe made by either the patient, the staff, or both. A patient might not be attentive and may not check whether accurate data has been entered; likewise, an employee may not check with the patient to ensure he has put in the correct data or not. They might think that it is not a big deal, but there lies the problem. Such inconsistencies which they believe are inconsequential lead to increased waiting times, worse patient outcomes, financial losses, wrong treatments, and sometimes even result in deaths. 

A reputed individual in the industry stated that to combat these errors, a lot of countries have switched to unique IDs for patients. Sadly, the US is not doing that yet, as it has no nationwide standardized patient identifier nor any effective strategy to do so. Thus, the responsibility is pushed onto the shoulders of the healthcare providers, each coming up with their own approach for identifying patients. 

A lot of suggestions have been made by experts to solve these errors, like software for patient identification, improvements in data standards, and ID cards, among many other options. However, the only one which is being pursued by many and used by early adopters are biometric patient identification systems. RightPatient is the most appropriate choice to eliminate patient matching errors. It uses iris scanning to ensure that the correct patient is identified, and it does so with ease, as reported by over a hundred healthcare providers who are using it. As it is using iris scanning, it is also hygienic and safe, as it requires no physical contact, and is convenient for the patients, as all they need to do is look into the scanner to match with their records. Since it is also less time consuming than traditional patient matching, it is lauded by many for improving the patient experience as well as patient safety. Patient matching has never been easier and more accurate, according to the users of RightPatient

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Patient identification error causes yet another grave mistake

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.

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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.

Scanning a patient for identification

Hospitals Need a Better Patient Matching System to Identify “John Does”

Patient identification or lack thereof is a topic which we hear about every day. We always read news about mistaken patient identities due to mix-ups, frauds, insufficient patient matching system, etc. What about those who arrive at the hospitals and are never identified? Let’s look at these John Does but from a different angle- from the perspective of the emergency hospital staff who receive and treat them rather than from the outside viewer.

Scanning a patient for identification

 

Imagine this: A man in his 50’s arrived in the emergency room, wheeled in by paramedics, shaven head, brown eyes, unconscious. To make matters worse, he had no materials on him that could have helped the staff with his identity for crosschecking with their patient matching system– no wallet, cellphone, papers, or anything at all. To top it all off, he did not have any distinguishing features such as a tattoo or scar. This incident was back in 2017- a car hit him in January, and he was rushed in with a fatal brain injury to Los Angeles County+USC Medical Center. He did not have any visitors, nor was he ever reported missing. Sadly, he passed away being a John Doe, no one ever knowing who he was.

This is just one example of how serious and pressurizing it is for the hospital staff to deal with such emergencies regarding patient matching systems, primarily when they consist of a John Doe. In these cases, they are required to become a form of detective in order to determine the identities of these unknown patients when they arrive at the hospitals. This is done for several reasons: firstly, finding the identity helps with the treatment- the staff can then determine the patient’s medical history and whether he/she has any complications or not. Also, it allows them to find and contact a next of kin or close one to make any critical decisions if it becomes necessary. The identity also helps the hospital to contact the insurance company or government health programs, whichever the patient is associated with, regarding payment of their services.

However, there is a catch- federal laws concerning privacy make it difficult for the hospital staff to determine the unknown patients’ identities. In the previously mentioned example as well as in many similar cases, the team along with the social workers frantically rummage through whatever a John Doe brings with him- bag, clothing, phones without passwords, receipts, or whatever piece of document or device which can help them identify the individual and proceed to their patient matching system. Their efforts don’t stop there- they also question the paramedics and dispatchers. Tattoos, piercings, and scars are duly noted, and when all else fails, dental records are checked against the individual. However, because the police can only access fingerprints, it is often left unchecked, mainly because the police only involve themselves only when a criminal element is present in the situation.

These John Does are usually the ones hit by vehicles and had unfortunately left their IDs back at home, and can also be poor people with cognitive diseases such as Alzheimer’s. Other times, they are overdosed individuals. Unsurprisingly, socially isolated individuals like homeless people are the ones who are the most difficult to identify, and sadly, they are the ones who are the most common John Does in recent years.

The Health Insurance Portability and Accountability Act (HIPAA) was made to ensure the privacy of an individual’s medical data. However, in cases of these John Does, it can make patient matching increasingly difficult as the hospitals cannot release any information to those searching for missing family members regarding these patients. For instance, a patient with Alzheimer’s was admitted to a NY hospital with the name “Trauma XXX.” The police and his family members went in search for him several times at the very same hospital, but they were told nothing. Weeks later, a doctor while watching television saw that man in the news and identified him as the patient “Trauma XXX.” Afterward, when charged with why the hospital hid the patient, the staff said that they did not ask about “Trauma XXX” specifically.

Due to this incident, a lot of rules were set up and changed regarding information requests about missing persons. It consisted of following over twenty steps for hospitals, starting from notifying the reception, to taking DNA samples.

All of this could have been avoided if a fast, accurate, and reliable patient matching system was used. RightPatient is one such patient identification system that utilizes biometrics and AI. Through this, it uses iris scanning to quickly match the patients with their EHRs so that the whole patient experience can be enhanced. It also helps the physicians focus on more critical tasks such as the patients themselves instead of going through matching patients. Thus, not only is it beneficial for the patients, but it is also beneficial for the hospitals as well, creating a win-win situation for all and ensuring patient safety through the enhancement of the whole patient experience.

Piece of medical record over other documents

The curious case of a mistaken patient identity

Mistaken patient identities in the healthcare industry are nothing new- a lot of people have faced it, and it occurs almost every day in the US. However, this time, it was not news of someone who suffered from it, but rather a couple who got saved from just being another mistaken patient identity. This mishap was properly detected and the victims were fortunately saved from a huge financial loss.

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The actual story

A Florida-based couple would have been the victims of mistaken patient identity and almost lost a lot of money. Mrs. Barding detected the error when she identified that Cigna, her insurance company, was processing a whopping $2.2 million in medical bills.

How did she figure it out? With the help of Mr. Barding, the couple identified that the bills were actually associated with his identical twin, Vance Barding, who was burned in a work accident and sadly passed away six weeks later from complications.

Mrs. Barding told that Cigna billed them for her brother-in-law’s care and had deducted $3000 from her health reimbursement account. However, after notifying the insurance company, they verified the claim and stopped billing the couple, as well as returning the money to Mrs. Barding’s account. This was all due to the mistaken patient identity. 

Cigna also stated that there are always a large number of claims which are made in error by different healthcare providers, and they have thus discussed with the latter in order to be more vigilant about such erroneous bills.

The healthcare provider in question is Orlando Health and it was provided incorrect information, due to which this whole situation arose. However, as it was made aware, they worked with necessary parties in order to rectify the mistake. This was a fortunate case where the would-be victims were saved due to the vigilance of the wife. Unfortunately, not every victim has a Mrs. Barding beside them.

Some statistics regarding mistaken patient identity errors 

A survey conducted by Patient Safety & Quality Healthcare (PSQH) revealed that 54% of the respondents are unhappy with the current patient identification methods. Another research shows that 10% of the overall patients are misidentified during entry. This mostly happens due to the large healthcare systems, which have a lot of patients to cover, and thus they make mistakes due to human errors, miscommunication, and sometimes in order to save time. The PSQH survey also shows that 89% of the respondents believe that proper patient identification is a vital part and is of paramount importance to their organizations. On the other hand, only 4% believe that the current patient identification process is completely accurate.

How to avoid patient identification errors? 

Patient identification using biometrics is the only way to eliminate this problem. It not only is error-free, but it is also instantaneous, speeding up the process for patient care, as well as safe.

RightPatient AI is used by a number of notable hospitals as well as thousands of outpatient sites, transforming the experience of the patients as well as the healthcare professionals. It not only eliminates the errors, but it also saves time in order to focus on patient care. It is also fully compatible with any EHR system as well as third-party apps, thus creating a seamless experience for the end user. It uses iris scanning to identify the patients and then pull the relevant data from their EHR. Take Terrebonne General Medical Center (TGMC) as an example. It is located in an area where a lot of people share common names, either first or last. Thus, it posed risks of incorrect record documentation, patient record mix-ups, and providing wrong prescriptions. RightPatient has helped TGMC in eliminating this issue entirely using Photo Biometrics along with iris scanning. It has an advantage over most other biometric modalities- iris scanning does not require any physical contact on the patient’s end, thus, no risks of infections or diseases via contact. Duplication and errors are all things of the past with RightPatient. 17 years of experience in AI and human recognition is proof of it.

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Reducing opioid abuse by knowing the right patient

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

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

How did we get to this point?

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

There have been many changes regarding the prevalence of various diseases over the last three decades. Slowly and steadily, medicine has become dominated by chronic and painful health conditions. It is estimated that one-third or 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.

RightPatient augments population health investments

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

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

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

Current state of U.S. healthcare

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

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

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

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

Challenges in moving to a population health solution

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

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

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

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

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

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

chart corrections impact healthcare data integrity

How RightPatient Prevents Chart Corrections in Epic and Other EHRs

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-right-patient

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

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.

patient mix-ups

How Big is the Patient Mix-up Problem in the U.S.?

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.