Posts

opioid abuse epidemic

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. 

patient data integrity and patient data matching in healthcare

Achieving Higher Patient Data Integrity Requires a Multi-Layered Approach

patient data integrity and patient data matching in healthcare

Improving patient data integrity in healthcare requires a multi-layered approach that addresses both data matching and more accurate patient identification.

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.

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.”

(AHIMA report, “Ensuring Data Integrity in Health Information Exchange”  http://library.ahima.org/xpedio/groups/public/documents/ahima/bok1_049675.pdf)

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?

biometric patient identification prevents duplicate medical recordsDavid 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.

patient matching and patient identification in healthcare

Healthcare Scene Blab Tackles Patient Matching and Patient Identification

patient matching and patient identification in healthcare

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:

 

Special thanks to John Lynn and Healthcare Scene for hosting the discussion! 

What are your top concerns surrounding the issue of achieving 100% patient matching in healthcare? Please share them with us in the comments below.

accurate patient matching in healthcare through reconciling duplicate medical records

AHIMA Survey on Patient Matching Illustrates HIM Burdens, Frustrations

accurate patient matching in healthcare through reconciling duplicate medical records

A recent survey of HIM professionals by AHIMA illustrates the problems that duplicate medical records have on accurate patient matching in healthcare. (Photo courtesy of Wikimedia Commons: http://bit.ly/2hVSvMc)

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

AHIMA Sheds Light on Patient Matching Problems in Healthcare

The American Health Information Management Society (AHIMA) released details of a survey yesterday that revealed over half of Health Information Management (HIM) professionals still spend a significant amount of time reconciling duplicate medical records at their respective healthcare facilities. The survey went on to reveal some very interesting statistics on patient matching and linking patient records, illustrating the burden that duplicate medical records have not only on HIM staff, but the dangers care providers face who increasingly rely on access to accurate, holistic patient data to provide safe, quality care. One particular stat that jumped out at us was:

“…less than half (47 percent) of respondents state they have a quality assurance step in their registration or post registration process, and face a lack of resources to adequately correct duplicates.”

This is an area of particular concern due to the fact that our research has shown that many healthcare facilities spend tens, sometimes hundreds of thousands of dollars per year reconciling duplicate medical records but very few have technology in place to prevent duplicates in the future. It’s encouraging that accurate patient matching in healthcare seems to finally be getting the attention it deserves, due to the digitization of the industry, the shift change from fee-for-service to a value based payment system and a burgeoning healthcare ecosystem laser focused on improving both individual outcomes and population health. AHIMA’s survey supports this assertion by stating:

“Accurate patient matching “underpins and enables the success of all strategic initiatives in healthcare…”

Equally concerning is the fact that less than half of HIM professionals surveyed have any type of patient registration quality assurance policy in place and only slightly over half of survey respondents could accurately say what their duplicate medical rate actually is. Not to mention the fact that HIM professionals spend entirely too much of their time reconciling duplicate medical records, with 73% reporting that they work duplicates “at a minimum of weekly.” 

As more healthcare organizations and facilities begin to understand that accurate patient matching has a major impact on other downstream activities, it is encouraging that the issue is finally getting the attention it deserves helped in part by the efforts of AHIMA, and CHIME’s national patient identification challenge which is scheduled to kick off this month.  It’s clear that the healthcare industry is slowly coming to the realization that many new initiatives borne from the HiTech Act and Meaningful Use (e.g. – population health, ACOs, health information exchanges, interoperability) don’t really have any hope to succeed in the absence of accurate patient identification. 

Duplicate Reconciliation Unnecessary Burden on HIM?

Early last year, we wrote a blog post on How Accurate Patient Identification Impacts Health Information Management (HIM) which highlights the exorbitant amount of time HIM spends reconciling duplicates and the opportunity cost this brings. For example, time spent on duplicate clean up and reconciliation could instead be allocated to coding for reimbursement and preparing, indexing, and imaging all paper medical records – a critical component in the effort to capture and transfer as much health data as possible to a patient’s EHR.

The fact of the matter is that as health data integrity stewards and medical record gatekeepers, HIM professionals are better served spending their time ensuring proper and accurate reimbursement and medical record accuracy then reconciling duplicates which should have never been created in the first place. HIM staff perform one of if not the most critical functions in healthcare by ensuring health data integrity, especially in light of the increasing reliance of often disparate healthcare providers need to access a complete medical record that includes as much information as possible.

As we noted in the post last January:

“…many hospitals have expanded responsibilities vis-à-vis Meaningful Use, EHR implementation, and meeting Affordable Care Act requirements, and it has become disadvantageous to continue devoting any time at all to duplicate medical record and overlay reconciliation. Biometric patient identification solutions open the door to re-allocation of HIM FTEs to more critical functions such as coding, reimbursement, and reporting. Simply put, implementing biometrics during patient registration is opening the door for HIM departments across the industry to provide a larger and more productive support role to meet the shifting sands of reimbursement and address the need to move towards quality vs. quantity of care.”

Conclusion

We could not have summed up the issue of duplicate medical record creation and reconciliation and inaccurate patient matching in healthcare more succinctly than this quote from AHIMA in the survey summary:

“Reliable and accurate calculation of the duplicate rate is foundational to developing trusted data, reducing potential patient safety risks and measuring return on investments for strategic healthcare initiatives.” 

Trusted data. Isn’t this the backbone of the new healthcare paradigm? Certainly we can’t expect to achieve many of the purported advances in healthcare in the absence of clean, accurate health data. It’s time to eliminate duplicate medical records forever, and establish cohesive, quality assured patient matching in healthcare.

What are your biggest takeaways from the AHIMA report on accurate patient matching in healthcare?

Brad Marshall works for RightPatient - the industry's best biometric patient identification solution.Brad Marshall is an Enterprise Development Consultant with RightPatient®. With several years of experience implementing both large and small scale biometric patient identification projects in healthcare, Brad works closely with key hospital executives and front line staff to ensure project success.

 

biometric patient identification systems should offer multiple modalities

Why We Offer a Choice of Hardware Modalities for Biometric Patient ID

biometric patient identification systems should offer multiple modalities

Understanding the value of using a biometric patient identification solution that offers a choice of multiple biometric modalities is key to a better understanding of how this technology can truly increase patient safety in healthcare.

The following guest post was written by David Cuberos, Enterprise Sales Consultant with RightPatient®

On many occasions throughout the course of conversations with different hospitals and healthcare systems, the question of which biometric modality to deploy for a biometric patient identification management project always surfaces. After all, choosing which biometric hardware modality to deploy is a critical factor for patient acceptance and efficient system performance – metrics that have a significant impact on the success and return on investment (ROI) of the initiative. More often than not when our customers and community get an in depth look at the variety of biometric hardware modalities we offer compared to other alternatives, they are curious about why we would support multiple devices instead of just one, and what the pros and cons are of each. The answer uncovers an important, but not frequently discussed attribute of biometric patient identification solutions that hospitals and healthcare organizations should be aware of.

Experience in Biometrics and Health System Integration Matters

The origins of RightPatient®’s biometric matching technology trace back 13 years through the experience and global track record of managing both small and large scale biometric identification management projects in many different industries by our parent company M2SYS Technology. As a proven innovator and expert in biometric matching technology, our experience has taught us that the success of these initiatives is largely dependent on ensuring that the biometric modality used matches the unique needs of our end users, offers the flexibility to change or add a modality in the future, has the ability to be customized prior to launch, and is easily scaled up as the deployment grows. 

Experience in managing biometric identification management projects has also taught us the importance of using human factor engineering as part of our system design process based on understanding what makes a task easy for hospital staff and what makes it hard in order to ultimately develop biometric tools that would support healthcare organization goals.  Deployments became more about identifying solutions that would cut down on “human error” and providing biometric hardware and software systems that fit employee need and workflow and less about deploying a solution that used the most popular and well known technology and relied on traditional conventions.

Ergonomics have become more influential in biometric patient ID system design, and training curriculum was refined to reflect the sources of expert performance, and how hospital staff acquire expertise in working with biometric identity systems. And perhaps most importantly, biometric deployments based on human factor engineering are designed to make systems more resilient in the face of shifting demands.   

Hardware diversity, patient identification mobility, and back end databases that use certain biometric matching types are the only way that hospitals will be able to reach pre-deployment defined goals of eliminating duplicate medical records/overlays, preventing medical ID theft/fraud, and increasing patient safety. The biometric hardware chosen for a deployment has a direct effect on achieving these goals which is why it becomes a critical decision in the overall project scope.

The Problem of “Locking In” to One Biometric Modality System Platform

Biometric vendors who only offer a choice of deploying one biometric modality system for a patient ID initiative are hamstrung by the limitations of the device manufacturer. In other words, they “lock” you into using one biometric modality system that inhibits the ability to expand a deployment to meet the new realities of identifying patients in healthcare – biometric patient ID systems must now be able to offer patient identification at new touchpoints along the care continuum (e.g. – smart mobile devices, patient portals, and telehealth) and not just accurate ID at the point of service. Biometric patient ID systems that rely on one biometric modality can’t offer this flexibility because they can’t identify patients in these scenarios and often times, hospitals who deploy these solutions must either make another investment in a system that does have this capability, or risk not addressing how to offer accurate patient identification for the aforementioned new patient touchpoints – a risk that could have extremely negative repercussions should a patient be mis-identified or a clinician misses key patient health data missing from their medical record.

Deploying a biometric patient authentication system that offers a variety of modalities is the only way a hospital can meet the increasing complications of ensuring accurate identification along the care continuum at new touchpoints, using voice or facial recognition biometrics for smart mobile devices as an example. Absent of this flexibility, hospitals and healthcare organizations are running the risk of non-authorized individuals accessing sensitive personal health information (PHI), or medical information not being attributed to the proper records which is a direct threat to patient safety and an extreme liability.

The ability to establish and maintain patient data integrity is also called into question when locking into a single biometric modality system. The holy grail of patient data integrity is to achieve 100% accuracy, cleanse a master patient index (MPI) of any duplicate medical records/overlays, and then have the ability to maintain that level of integrity as the database grows. Since single biometric modality systems do not have the ability to address accurate patient identification at all touchpoints along the care continuum, hospitals and healthcare organizations run the risk that a care event will either be administered to the incorrect patient, or medical data could be accessed and stolen by an unauthorized individual. Deploying multiple biometric modalities such as facial and voice recognition to address accurate patient ID at ALL touchpoints is the only way that true patient data integrity can be established and maintained. 

Conclusion

As the biometric identification management industry continues it’s rapid pace of evolution and expansion parallel to the evolution and expansion of new patient touchpoints to access medical data and services, hospitals and healthcare organizations should be thinking of deploying a solution that leverages multiple modalities that can accurately identify patients no matter where they are. The only way to accomplish this is the use of a biometric patient ID solution that offers a choice of modalities and a high degree of flexibility for deployment to address various patient touchpoints along the care continuum.

Don’t fall into the trap that a one biometric modality system will be sufficient to ensure accurate patient ID and a high level of patient data integrity across the care continuum. Learn more about how a choice of biometric hardware modalities for patient identification in healthcare is a smarter investment that will truly help hospitals and healthcare organizations achieve the goals that measure the success of the initiative.

Partnering with a vendor that has deep experience in biometric identification management technology, a strong track record of healthcare system integration experience, and a history of innovation is the only way to achieve the results you expect.

What patient ID challenges have you experienced that were solved by the use of multiple biometric modalities? Please share your comments below.

biometric patient identification prevents duplicate medical recordsDavid 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.