The U.S. healthcare system does not seem to catch a break. The coronavirus outbreak is the latest problem added to the already formidable list of issues plaguing the U.S. healthcare system. Lack of price transparency, outrageous costs, and archaic laws are just some of the problems. However, let’s talk about a problem that has been around for many years and still haunts several (if not all) hospitals – lacking an effective patient identification system.
A study regarding patient misidentification
Not so long ago, a study conducted by the Pew Charitable Trusts and Massachusetts eHealth collaborative shed light on a known issue – wrong patient matching is very common in U.S. hospitals. Let’s see what wrong patient matching leads to and what causes the errors, so that we can understand why healthcare providers must ensure accurate patient matching.
Patient matching and duplicate records explained
Firstly, the meaning of patient matching is quite self-explanatory. It refers to matching a patient with his/her health record so that the hospital can proceed on providing healthcare services. Now, it seems quite simple, but patient matching issues exist, according to the aforementioned study. What makes it so tough? The most common reasons are duplicate medical records and patient misidentification, also referred to as mismatched patient records.
Duplicate medical records are created when a patient has multiple patient records at a given healthcare provider’s EHR system. This happens for a variety of reasons – poor communication between the hospital staff and the patient during patient admission or checkup, failure to find the existing patient record within the database, and so on. Duplicate records decentralize the healthcare process that providers initially intended to provide to patients. For instance, due to duplicate records, a patient’s complete medical history could be impossible to find. In essence, different diagnoses are stored in various records, which leads to serious medical errors like mistreatment, repetitive lab tests, wrong medication, unintentional injuries, and in extreme cases, deaths.
Patient misidentification, wrong patient identification, and mismatched patient records are used interchangeably but mean the same thing. Patient misidentification occurs when a healthcare provider mixes up medical records of different patients. This happens when the patients share similar characteristics – name, date of birth, medical history, are just a few examples. This can cause severe issues like mistreatment, financial loss for patients, longer recovery time, and has also proven to take the lives of unfortunate ones.
How are hospitals affected?
Not only patients but healthcare providers are also affected profoundly. Claim denials can lead to losses in millions and occur when bills are sent to the wrong patients as a result of patient misidentification. Patients can also hit hospitals with lawsuits because of mistreatments.
Thus, accurate patient identification is critical for hospitals to operate smoothly and without any unwanted incidents. Fortunately, RightPatient has a proven track record of helping out hospitals with patient identification issues. It is a biometric patient identification platform that locks the medical records of patients with their biometric data. During enrollment, the platform takes a photo of the patient and his/her biometric data, such as a scan of the irises, and attaches it to the medical record. Later on, whenever the patient returns, all he/she needs to do is look at the camera. RightPatient accurately identifies the medical record within seconds, ensuring accurate patient identification as well as preventing the creation of duplicate records.
https://www.rightpatient.com/wp-content/uploads/2020/04/RightPatient-is-used-by-leading-hospitals-to-ensure-positive-patient-identification.jpg500900Matt Gibsonhttps://www.rightpatient.com/wp-content/uploads/2021/04/RightPatient-Logoup.pngMatt Gibson2020-05-01 16:59:202020-08-10 07:27:50Duplicate Medical Records and Patient Misidentification Frequently Affects Hospitals
It’s no secret that medical identity theft is on the rise. Over 2 million Americans each year become victims of medical identity theft, and, unfortunately, that number only continues to grow.
It’s growing for a number of reasons. First of all, there were more healthcare data breaches in 2019 than the previous three years combined. These breaches compromised the medical records of over 40 million Americans.
Let’s consider this in light of rising healthcare costs and a worsening opioid epidemic. These facts create a ripe market for medical identity theft. Patient identity data is readily available on the black market and there is a ton of demand for it.
When medical identity theft is perpetrated, patients and healthcare providers suffer. Victims can face bills for services they never received, incorrect treatment data mixed into their medical record can affect future outcomes and quality of care, and the costs to restore their identity can be prohibitive.
Healthcare providers lose millions of dollars for services that will never be paid for. Increasingly, they also face litigation costs from patient lawsuits for failing to protect their information.
Providers also face another burden. In 2009, the FTC started to enforce the Red Flags Rule, which requires healthcare providers to develop programs that can help to detect and address situations that are “red flag” indicators of medical identity theft. The goal is to ensure vigilance and reduce the potential costs associated with medical identity theft.
However, implementing red flag processes, keeping them current, and ensuring compliance can be expensive and time consuming for healthcare providers. These processes must also be administered by front-line staff members, typically patient access employees that handle registration.
This is an enormous responsibility for these employees when considering the potential consequences of medical identity theft. Compliance with red flag rules also places a substantial burden on registrars who are already buried with additional duties such as verifying insurance, collecting payment, and processing patients as efficiently as possible to reduce wait times and improve margins.
Now, against the backdrop of these market realities, imagine if the risk of medical identity theft could be substantially mitigated, if not eliminated altogether. This is where RightPatient comes into play.
RightPatient validates that patients are who they claim to be when scheduling appointments by comparing a patient’s selfie photo to the photo on her driver’s license or other ID cards. When patients show up for visits, RightPatient accurately identifies them during registration and other points along the care continuum.
RightPatient creates a closed-loop platform to prevent medical identity theft and other errors that can impact patient safety, revenue cycle, and data quality. This saves a lot of time, money, and hassle for patients and healthcare providers.
https://www.rightpatient.com/wp-content/uploads/2020/03/RightPatient-ensures-accurate-patient-identification.jpg600800Michael Traderhttps://www.rightpatient.com/wp-content/uploads/2021/04/RightPatient-Logoup.pngMichael Trader2020-03-14 08:20:182020-08-10 08:42:49How RightPatient Benefits Medical Identity Theft and the Healthcare Red Flags Rule
What is one of the crucial things a company needs to ensure so that it can thrive? Is it the employees, revenue, or size? While many may answer something along the lines of the previously mentioned characteristics, one of the most critical assets a company can have is the trust of its customers. The healthcare system is no different – various health systems and hospitals are successful today only because of their patients’ trust in their services. Since it is healthcare, patients put their lives in the hands of the hospitals – trust plays a huge role here. That trust can be enhanced with medical identity theft prevention.
According to Morning Consult, in terms of trust from consumers, healthcare lies in the middle, while insurance, finance, and real state are underneath it, whereas airlines and technology are above it.
Morning Consult conducted a study which had several respondents about their perception regarding various US brands as well as firms. From these people, a meager 16% responded that they trust health systems a lot, whereas 36% said that they believe these organizations somewhat.
Also, while ranking the most trustworthy companies, people, ideas, among other things, the respondents ranked their physicians even above notable choices such as Google, police, and leaders.
Thankfully, the report went deeper and gave areas of improvement for hospitals and health systems to build up trust among patients. When the sample of the study was asked what the most crucial factor which helps build trust towards an organization is, three-fourth of the respondents said that protecting their sensitive data was extremely important for trust-building.
All of these are straight from the customers themselves, and these are even more applicable to the US healthcare system. The health systems and hospitals need to ensure that the sensitive patient data they keep are safeguarded, especially now. Breaches seem to be very common nowadays, which leads to exposure to the patients’ confidential medical data as well as documents like medical images, medication, and so on. It costs both patients and healthcare providers alike – patients become victims of medical identity theft, whereas healthcare providers’ reputations are dented. People question the security surrounding the medical records since HIPAA requires strict safeguarding of such sensitive information.
These lead to losses for both patients and health systems – patients may sue the hospitals, the culprits may use the identities to avail services illegally, costing the patients a significant amount of money for services that they never used. Medical identity theft may also occur if an individual steals a patient’s medical credentials and uses it for his/her gain. In such cases, the preferences of the culprit may get mixed up with that of the patients. For instance, the patient might be allergic to certain medications, and may still receive that after the culprit uses his/her ID.
Dynamic healthcare providers such as Novant Health, Terrebonne General Medical Center, and University Health Care System are preventing such issues by using RightPatient. It is a biometric patient identification platform that locks the medical records after attaching those with the biometric data of the patients. Once a patient enrolls with the biometric data, for instance, irises or fingerprints, the records can be accessed using only the same data, creating medical identity theft prevention. The hospital can also identify the accurate patient record within seconds after the patient scans his/her biometric data for verification. RightPatient not only aids in medical identity theft prevention, but also eliminates patient matching errors, ensuring accurate patient identification, enhancing patient safety, and improving the revenue cycle as well. It saves lives as well as millions for both patients and health systems, enhancing patient trust.
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.
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.
https://www.rightpatient.com/wp-content/uploads/2018/07/RightPatient-can-help-reduce-the-opioid-abuse.png400800Michael Traderhttps://www.rightpatient.com/wp-content/uploads/2021/04/RightPatient-Logoup.pngMichael Trader2018-07-16 14:22:002023-04-25 08:03:25Reducing opioid abuse by knowing the right patient
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.
https://www.rightpatient.com/wp-content/uploads/2018/02/RightPatient-augments-population-health-investments.jpg445800Mizan Rahmanhttps://www.rightpatient.com/wp-content/uploads/2021/04/RightPatient-Logoup.pngMizan Rahman2018-02-09 22:31:572023-04-25 08:04:05How Can You Protect Your Investment in a Population Health Solution?
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.
https://www.rightpatient.com/wp-content/uploads/2018/01/Reduce-chart-corrections-with-RightPatient.jpg445800Michael Traderhttps://www.rightpatient.com/wp-content/uploads/2021/04/RightPatient-Logoup.pngMichael Trader2018-01-29 16:02:012021-07-27 14:07:28How RightPatient Prevents Chart Corrections in Epic and Other EHRs
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.
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.
https://www.rightpatient.com/wp-content/uploads/2018/01/Learn-how-RightPatient-can-enhance-value-based-care.jpg445800Mizan Rahmanhttps://www.rightpatient.com/wp-content/uploads/2021/04/RightPatient-Logoup.pngMizan Rahman2018-01-15 00:43:482022-08-26 09:57:51Value-Based Care: A Patient-Centered Approach Requires Knowing Your Patient
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?
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.
https://www.rightpatient.com/wp-content/uploads/2018/01/RightPatient-can-prevent-patient-mix-ups.jpg445800Patrick Mayhttps://www.rightpatient.com/wp-content/uploads/2021/04/RightPatient-Logoup.pngPatrick May2018-01-11 16:06:462021-07-27 13:58:28How Big is the Patient Mix-up Problem in the U.S.?
The following guest post was written by David Cuberos, Enterprise Sales Consultant with RightPatient®
Patient Data Integrity and Duplicate Medical Records
It is a well known fact that inaccurate or incomplete data within a patient’s medical record can be a catastrophic risk to patient safety, not to mention a serious hospital liability. As a result, many hospitals and healthcare organizations across the industry are closely examining the integrity of their health data and taking steps to clean it, most by using third party probabalistic and deterministic de-duplication matching algorithms (often directly from their EHR providers) that search and identify possible duplicates for an automatic or manual merge.
Improving patient data integrity in healthcare requires a multi-layered approach that addresses both data matching and more accurate patient identification.
Several key players in the healthcare industry including CHIME, AHIMA, HIMSS, and major EHR providers are beating the drum to improve patient identification and patient data matching, all important catalysts for the push to improve patient data integrity.
If you are a hospital or healthcare organization that is knee deep in the middle of a health IT initiative to help increase patient data integrity (especially in the context of prepping for participation in a local or regional health information exchange), you may want to stop and reassess your strategy. The rush to cleanse “dirty data” from EHR and EMPI databases is often addressed by relying on an EHR vendor’s de-duplication algorithm which is supposed to search and identify these duplicate medical records and either automatically merge them if similarity thresholds are high, or pass them along to the HIM department for further follow up if they are low.
This could be a very effective strategy to cleanse an EMPI to ensure patient data accuracy moving forward, but is it enough? Is relying on an EHR vendor’s de-duplication algorithm sufficient to achieve high levels of patient data integrity to confidently administer care? It actually isn’t. A more effective strategy combines elements of a strong de-duplication algorithm with strong patient identification technology to ensure that patient data maintains its integrity.
Duplicate Medical Record Rates are Often Understated
The industry push for system-wide interoperability to advance the quality and effectiveness of healthcare for both individuals and the general population has been one of the main catalysts motivating healthcare organizations to clean and resolve duplicates but it also has revealed some kinks in the data integrity armor of many different medical record databases. Most hospitals we speak with either underestimate their actual duplicate medical rate, or do not understand how to properly calculate it based on the actual data they can access. An AHIMA report entitled “Ensuring Data Integrity in Health Information Exchange” stated that:
“…on average an 8% duplicate rate existed in the master patient index (MPI) databases studied. The average duplicate record rate increased to 9.4% in the MPI databases with more than 1 million records. Additionally, the report identified that the duplicate record rates of the EMPI databases studied were as high as 39.1%.”
“High duplicate record rates within EMPI databases are commonly the result of loading unresolved duplicate records from contributing MPI files. EMPI systems that leverage advanced matching algorithms are designed to automatically link records from multiple systems if there is only one existing viable matching record. If the EMPI system identifies two or more viable matching records when loading a patient record, as is the case when an EMPI contains unresolved duplicate record sets, it must create a new patient record and flag it as an unresolved duplicate record set to be manually reviewed and resolved. Therefore, if care is not taken to resolve the existing EMPI duplicate records, the duplicate rate in an EMPI can grow significantly as additional MPI files are added.”
Clearly, the importance of cleansing duplicate medical records from a database cannot be understated in the broader scope of improving patient data integrity but relying on an EHR vendor’s probabilistic matching algorithm as the only tool to clean and subsequently maintain accurate records may not always be the most effective strategy. Instead, healthcare organizations should consider a multi-layered approach to improving patient data integrity beyond relying exclusively on an EHR vendor’s de-duplication algorithm. Here’s why.
Why Patient Data Integrity is a Multi-Layered Approach
Often not clearly explained to healthcare organizations, EHR de-duplication algorithms allow end users to set matching thresholds to be more or less strict, which comes with trade-offs. The more strict the threshold is set, the less chance of a false match but the higher chance of a false reject. The less strict the algorithm is set, the lower the chance of a false reject but the higher the chance of false acceptance.
Translation: Often times hospitals who say they have a low duplicate medical record rate might have a strict false acceptance rate (FAR) threshold setting in their de-duplication algorithm. That may mean that there could be a significant amount of unknown duplicate medical records that are being falsely rejected. Obviously, this is a concern because these databases must be able to identify virtually every single duplicate medical record that may exist in order to achieve the highest level of patient data integrity.
So, what can healthcare organizations do to ensure they are not only holistically addressing duplicate medical record rates, but also adopting technology that will maintain high patient data integrity levels moving forward? One answer is to implement a stronger de-duplication algorithm that has the ability to “key” and link medical records across multiple healthcare providers on the back end, and deploying a technology such as biometrics for patient identification on the front end to ensure that not only is care attribution documented to the accurate medical record, but a provider has the ability to view all patient medical data prior to treatment.
For example, many credit bureaus offer big data analytics solutions that can dig deep into a medical record database to better determine what identities are associated with medical records. These agencies are experts in identity management with access to sophisticated and comprehensive databases containing the identification profiles for millions and millions of patients — databases that are reliable, highly accurate, and secure with current and historical demographic data.
Once data is analyzed by these agencies, they are able to assign a “key” to match multiple medical records for the same patient within a single healthcare organization and across unaffiliated healthcare organizations to create a comprehensive EHR for any patient. Offering a unique ability to augment master patient index (MPI) matching capabilities with 3rd party data facilitates more accurate matching of medical records across disparate health systems and circumvents the problem of MPIs assigning their own unique identifiers to individual patients that are different than unaffiliated healthcare organizations that have their own MPI identifiers.
Benefits of using a third party big data analytics solution that has the ability to “key” medical records for more accurate patient data matching at a micro level include:
More accurate identification of unique patient records resulting in a more complete medical record and improved outcomes
Prevention of duplicate medical records and overlays at registration reduces the cost of ongoing MPI cleanups
Medical malpractice risk mitigation
Reduced patient registration times
The ability to more accurately link the most current insurance coverage patient information for more accurate billing
On the marco level, benefits include:
Positive patient identification for eligibility verification, billing, coordination of benefits, and reimbursement
Improved care coordination
Information and record keeping organization
Linkage of lifelong health records across disparate healthcare facilities
Aggregation of health data for analysis and research
Accurately aggregating patient federated data via a HIE
Conclusion
We have long championed the idea that improving patient data integrity can never be achieved in the absence of establishing patient identification accuracy or relying on EHR vendor de-duplication algorithms as the single resource to clean an MPI database. Hospitals and healthcare organizations that are truly committed to cleansing duplicate medical records from their databases and preventing them from reoccurring through more accurate patient identification must consider deploying stronger front and back end solutions that have the ability to more comprehensively identify and resolve these dangers to patient safety. Why not leverage the clout and reach of these big data analytics solutions to more effectively improve patient data integrity instead of putting all of your eggs in an EHR vendor’s de-duplication algorithm?
What other strategies have you seen as effective methods to increase patient data integrity in healthcare?
David Cuberos is an Enterprise Sales Consultant with RightPatient® helping hospitals and healthcare organizations realize the benefits of implementing biometrics for patient identification to; increase patient safety, eliminate duplicate medical records and overlays, and prevent medical identity theft and healthcare fraud.
https://www.rightpatient.com/wp-content/uploads/2016/02/Patient-data-integrity-and-patient-data-matching-in-healthcare-is-possible-with-RightPatient.jpg445800John Traderhttps://www.rightpatient.com/wp-content/uploads/2021/04/RightPatient-Logoup.pngJohn Trader2016-02-15 19:50:462021-07-04 12:13:31Achieving Higher Patient Data Integrity Requires a Multi-Layered Approach
Healthcare Scene’s John Lynn hosts a blab conversation on the topic of patient matching in healthcare with Michael Trader and Beth Just.
Our President Michael Trader was grateful for opportunity to discuss patient matching and patient identification in healthcare with Beth Just from Just Associates during John Lynn’s blab session earlier today. The discussion covered a wide range of topics including:
— How big is the patient identification problem in healthcare? — The continuing problem of duplicate medical records in healthcare and strategies to improve and sustain patient data integrity — Describing the availability and measuring the success of existing patient identification solutions in healthcare — Would a national patient identifier help or would the existing challenges still apply? — Why can’t the current solutions get to 100% patient matching? — How does the CHIME $1 million National Patient ID Challenge work?Is this challenge achievable?
What materialized was an excellent discussion on patient identification in healthcare with both Michael and Beth offering intelligent insight on the problems that exist, solutions built to address the problems, and what it truly means to achieve 100% patient ID accuracy. Take a moment to watch the blab session here:
