Seven strategies ACOs use for better patient outcomes and lower costs

August 22, 2019/7117 views / 0 Comments/in BLOG, EHR, healthcare, Medical Record Mix-ups, Patient Engagement, Patient Experience, Patient Outcomes /by Matt Gibson

According to recent studies, it is expected that Medicare’s projected spending will be well over $1.5 trillion by the year 2028 – that is more than double what the value was just two years ago! All Medicare asks from ACOs are better patient outcomes.

Many ACOs have already reduced costs and thus saved Medicare approximately $1 billion during 2013-2015. Not only did they reduce costs, but they also improved quality across the majority of the metrics required by Medicare. These exemplary ACOs depended on primary care visits, which they used to reduce ER visits and in turn, cut costs by around $700 per patient.

RightPatient enhances patient outcomes.

Some of the strategies which ACOs can follow to improve their healthcare spending patterns and generate better patient outcomes are:

Collaborate with the physicians they work with

ACOs highlighted the fact that one of the ways to enhance the quality of healthcare as well as reduce the costs was to work closely with the assigned physicians. They also stated that these physicians are usually ordering services like lab tests for the patients or referring to other specialists without keeping the costs in check, and may inadvertently end up incurring more costs than necessary. However, if the physicians and ACOs collaborated frequently, the former can make informed decisions regarding the costs which will be beneficial for both the patients as well as the ACOs by reducing costs while keeping quality in check. Other than that, the physicians have to be busy with administrative issues, which can be quite hectic for them, which causes them to focus more on these tedious tasks rather than focusing on the patients. ACOs can collaborate with the physicians regarding these issues, as well, to reduce the time spent on such matters and focus more on the patients instead.

Encourage the patients to take initiatives regarding their health

A common yet effective strategy used not only by ACOs but by any health system is to encourage their patients to take charge of their health and adopt a better, more active lifestyle. However, ACOs are reporting that this can be quite challenging, especially if there are multiple physicians which is common in ACOs. What ACOs can do is adopt the strategy used by conventional health systems – use patient engagement apps like CircleCare. It has all the necessary features required for active patient engagement. Patients can track not only their steps but also keep track of their blood pressure, blood glucose level, schedule medicine reminders, and so on. It helps patients to maintain even the most complex medication routines as well as encourages them to lead a healthier lifestyle. However, these are not the only features of such apps, as will be explored further down the line.

Emphasize on patients requiring extra care

Care coordinators are professionals who are entrusted to make sure that the patients requiring extra care receive it, especially when they are discharged along with their proper medication as well as necessary materials. Nearly all the ACOs utilize such personnel who even help schedule follow-ups. However, ACOs can also use CircleCare in this context for better care, since these apps help patients and these caregivers to stay connected and exchange health information easily, perhaps about minor complications and so on.

Reduce ER visits and readmission rates

Most ACOs face the problems of ER (emergency room) visits which in turn generate hospital readmissions, many of which are preventable. However, it is notably more of a concern for ACOs since they are fined based on the readmission rates. One strategy ACOs can use is providing digital solutions to patients such as patient engagement apps like CircleCare. Since these apps push the patients to be physically active, these can create better patient outcomes – the more active the patient, the healthier they will be. Also, since these apps have two-way communication facilities, they can contact their physicians regarding any minor health issues and resolve them outside the ACO premises, thus, reducing ER visits.

Enhance patient identification and data sharing

Patient identification is one of the major problems of the US healthcare system, and it is a massive concern for ACOs as well – they need to share patient data among themselves, and the data needs to be as immaculate and consistent as possible. Thus, ACOs can overcome the issues with conventional EHRs by using biometric patient identification solutions like RightPatient. It uses iris scanning to accurately identify the patients and match them with their appropriate records within seconds. This will improve the match rates as well as enhance the patient experience along with data sharing, which are all must-have features for any ACO as these lead to better patient outcomes.

Make sure medication adherence among patients is present

According to statistics, two-thirds of the prescribed patients are non-adherent regarding their medications. This generates 50% of treatment failures, causing up to 125,000 preventable deaths per year in the US. These could have been prevented if the patients were adherent to their medications, and for that, CircleCare is the perfect solution. Its medicine reminder makes medication adherence as easy as it gets – the patients using the app can set the type, color, look, frequency, dosage, starting/ending date, and duration through an intuitive yet simple interface. Even the most complex regimens become manageable due to CircleCare, ensuring medication adherence and thus fewer ER visits for ACOs.

Ensure patient education is provided

Patient education is another problem which generates frequent ER visits as well as hospital readmissions. Most patients have minimal knowledge regarding their health – 50% of them experience difficulty in understanding as well as using health information and 40% of them do not remember most of the information in the first place. CircleCare provides meaningful and easy to understand information for patients, customized according to their health conditions so that they can receive the latest knowledge regarding their health and make informed decisions if required. Moreover, it also provides general health tips regarding food and physical activities, which can help patients follow those tips for a better lifestyle and better patient outcomes.

Unique identifiers will lead to a reduction of patient matching challenges

August 18, 2019/6262 views / 0 Comments/in BLOG, healthcare, Medical Identity Theft, Medical Record Mix-ups, Patient Experience, Patient Identification, Patient Matching, Patient Safety /by Matt Gibson

If you are a follower of this blog, then you will know how huge a problem patient matching challenges actually are for the whole healthcare industry. As the health systems are brainstorming workarounds to make sure patient matching is increased, they should also keep in mind some other factors. According to a report from Pew Charitable Trusts, if the industry wants to ensure that patient matching errors are eradicated or at least substantially reduced, they should focus on developing robust data standards and patient engagement alongside the search for an effective patient identification system.

But why should it matter? According to the researchers, they have found positive correlations between patient matching errors and adverse effects. To put it simply, if a health system cannot match a patient correctly to his/her existing medical record, then problems like rising costs, medication errors, and adverse patient experiences will take place. Thus, patient matching is not an issue which can be underestimated. Concerns such as data integrity failures, lack of clean records, and patient mix-ups can all lead to patient identification errors and disrupt the patient experience as well as threaten patient safety. For example, if patient A has heart disease and patient B has kidney complications, and their records somehow got mixed up, then both of them will receive improper care, which could be fatal. Such mix-ups usually occur because of common names, demographics, addresses, as well as the format of the data stored within the EHRs of the patients. Formatting refers to how a health system saves the data and how many data fields it uses. For example, one health system may keep email addresses, whereas another one may not.

Another example can be a health system saving the full name of a patient in a single data field, whereas another may use three fields to save first, middle, and last names of the patients. Due to such errors, interoperability is generated as well. Other issues which cause patient matching errors can be incomplete or blank data.

The research said that if common elements used by all the health systems were to be standardized, that is, the data is entered using a standard guideline rather than each health system doing so independently, these patient matching errors would decrease by a considerable amount. However, this may not reduce patient mix-ups between individuals with common characteristics like names and addresses, as these are still bound to happen.

Another suggestion the research made was that active patient participation is needed to ensure that they are correctly identified and matched with their appropriate record. However, patients can sometimes absentmindedly or inadvertently choose a wrong record, while in other cases, the hospital staff may do it on their behalf and create a whole new record for the patient, known as a duplicate ID.

The third and most effective suggestion the research made was to emphasize on using a unique patient identifier, something along the lines of RightPatient, that is, biometric patient identification systems. The study has shown that such a system helps in improving accurate patient identifications. The research further stated that biometric modalities are unique, cannot be counterfeited, and have excellent potential in the healthcare industry. They also found that hundreds of health systems have widely utilized some form of biometric patient identification system, and among them, one health system stated that over 90% of their patients accepted to use their biometrics to be identified since it is easy to use as well as accurate. Both the providers of healthcare as well as the receivers agreed that biometrics are helping to reduce patient matching challenges.

RightPatient falls in line with the research’s suggestion. It is a biometric patient identification system which uses iris scanning to identify patients. Once a patient’s irises are registered into the system, the data is then integrated with the patient’s health record. All the patient needs to do is look at their camera – RightPatient then accurately matches him/her with the proper ID – it is that easy and convenient. Since it does not require any physical contact, there are no risks for contracting new diseases during the identification process. Even the health systems love RightPatient since, with its help, the physicians can focus more on the patient rather than spend time matching the patient with the correct record, enhancing the patient experience along the way. Over one hundred health systems are using it and have reported that it has reduced losses which they incurred due to patient matching challenges, saving millions of dollars in the process.

Patient safety issues occur due to patient misidentifications

July 31, 2019/6195 views / 0 Comments/in BLOG, EHR, healthcare, Iris Recognition, Medical Record Mix-ups, Patient Experience, Patient Identification /by Matt Gibson

When electronic health records (EHRs) were introduced, people lauded it as the next big thing in the technological landscape of the healthcare industry. There were many reasons – it was entirely digital as the name suggests, could be accessed quickly and whenever required, reduced paperwork, among many other reasons. However, once it was widely implemented, the reality was drastically different. Instead of solving these problems, EHRs added additional ones along the way – patient safety issues.

EHRs have created many problems for patients and healthcare providers alike. They have created risks which were unpredicted at the time of their implementation, which can potentially generate the chance to make grave errors in the treatment processes for patients, specifically if the treatment involves medicines. If this seems terrible, it gets even worse. These problems associated with EHRs are much more catastrophic for children and younger patients since their prescribed drugs are age-based. A study has found that EHRs do not take age into account; thus, it does not tackle the problems associated in a pediatric environment. Other than that, patient safety issues like matching errors are synonymous with EHRs. This is where biometric patient identification systems like RightPatient come into play.

The problems healthcare providers face while using EHRs lead to misidentifications mostly. Some of the challenges EHR users face are:

Problems associated with displaying patient information, or incomplete/corrupt patient data
Issues related to patient data entries which cause delays
Problems with EHRs regarding feedback or notifications
Disruption in the workflow if data needs to be shared

So what are the actual problems associated with patient safety issues caused by EHRs?

Restricted information results in wrong medications

EHRs usually provide the hospitals with blank data fields which the latter can fill in, if required, regarding making notes making it easier for colleagues. However, they do not know whether their colleagues have access to those specific fields, which can create many problems. For example, if a doctor had made a note within the EHR regarding the medical condition of a patient, say glucose level, the nurse who will administer the medication may not be able to view this note because her access is restricted, not taking into account the medical condition. Such problems lead to a lot of medical complications. Likewise, if required fields are not available to be viewed by everyone in the hospital, the staff may get confused between patients with common characteristics like name, address, etc., causing patient matching errors.

A patient is provided with excess or wrong medication due to an entry error

This is the primary cause of confusing units – between imperial units and metric units. Thus, as it is common in the US to use pounds, and if the weight is entered in pounds, but the EHR accepts only kilograms, this will hamper with the medication. Medications are sometimes dependant on the weight, especially in the cases of children, and they may, unfortunately, receive larger doses of medicine than required, which can be fatal. Other than that, if a patient is misidentified, then this will cause the patient to receive the wrong medication as well.

Missed doses of medications occur due to problematic information displays

EHRs can usually list all the medicines that have been scheduled for patients, along with the time and dosage required. However, sometimes due to patient matching errors, they may end up with the medicines planned for some other patient, and this can be fatal for both the patients involved if someone is not cautious enough while administering the medications.

Duplicate patient IDs are created

By far, one of the most significant flaws of EHRs is consistent to this very day. News regarding patient matching errors are very common, and at least one person you know has faced it. How does it happen? Very simply – once a patient comes in and a hospital representative does not find the individual’s record in the EHR, the employee tries to save time by creating a new ID instead of searching more in-depth for the correct record. The staff thinks that this is the way to save time and effort but generates another source for losses by the employer. Sometimes even the patients are to be blamed – if they are not attentive enough while verification, the hospital staff may pick the wrong record for them. Its effects can range from being financial losses to even life-threatening. Due to this single issue, everyone involved with healthcare has suffered – patients, healthcare providers, insurance companies, and so on. Healthcare companies are now clamoring for a unique patient ID solution to eliminate these errors.

Medical ID thefts take place

Another consequence of patient safety issues via EHRs – fraudulent activities. Addicts and professional thieves can very easily misuse others’ IDs and gain access to healthcare benefits or drugs which are entitled to the actual patients, resulting in financial losses incurred by the unfortunate patients. All this happens because there was no sure way to identify whether the medical record belonged to the perpetrator, until now.

What can be the solution to patient safety issues?

As seen from the problems, all of them point in one direction – patient matching errors. From all of this, patient identification error is seen as a disease in itself of the healthcare industry. Everyone involved is affected and suffers due to it in various ways and degrees. It is a multibillion-dollar problem in the US, where fixing a single entry costs from $1000-5000.

However, its days are numbered, it seems, as biometric patient identification systems like RightPatient are successfully eliminating it from the healthcare providers who use it. It uses iris scanning, which is easy and convenient for patients – all they need to do is look into the camera, and they are identified accurately. Another benefit of this biometric modality is that it also correctly identifies the irises of the younger patients as irises are formed within ten months of birth and remain unchanged. Patients also love it because there is no chance of getting any contagious diseases as it does not require physical touch. Over a hundred healthcare providers are using it, and they are reiterating the same thing – patient experience has improved along with patient safety due to the reduction of errors and the speed of the process.

Hospitals are Prioritizing Patient Matching Accuracy

June 2, 2019/7914 views / 0 Comments/in BLOG, healthcare, Iris Recognition, Medical Record Mix-ups, Patient Experience, Patient Identification, Patient Matching, Patient Safety /by Matt Gibson

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

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

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

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

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

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

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

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

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

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

May 31, 2019/7565 views / 0 Comments/in BLOG, healthcare, Iris Recognition, Medical Record Mix-ups, Patient Errors, Patient Identification, Patient Safety /by Matt Gibson

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.

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.

CMS cuts payments to 800 hospitals for patient safety incidents – is yours next?

May 15, 2019/5553 views / 0 Comments/in BLOG, Medical Record Mix-ups, Patient Identification, Patient Safety /by Mizan Rahman

Patient safety incidents should be taken seriously by all hospitals. Unfortunately, CMS is penalizing 800 of them for having the highest rates of patient injuries and infections. The agency will trim these hospitals’ Medicare payments in the fiscal year 2019.

The HAC Reduction Program is a Medicare pay-for-performance program that supports CMS’s long-standing effort to link Medicare payments to healthcare quality in the inpatient hospital setting. Put more simply, hospitals are offered a financial incentive for preventing harm to patients! Under the program, a hospital’s total score is based on its performance across six quality measures. Each year, Medicare cuts payments by 1 percent for hospitals that fall in the worst-performing quartile due to patient safety incidents.

It’s alarming that, according to Kaiser Health News, 110 hospitals are being penalized in the fiscal year 2019 for the fifth straight time. Considering the daily news announcements about hospitals that are suffering financially or have gone out of business, we wonder why they aren’t taking more steps to address this issue.

If you think that only small rural hospitals are facing this problem, you will be surprised. CMS recently threatened to terminate Vanderbilt’s Medicare contract after a fatal medication error – Vanderbilt!

Since patients share common names and show up to the hospital many times without proper identification, 8-10% percent of the time their existing medical record is not found or they are potentially treated as a different person. This is a serious incident that can happen in every hospital at any time.

While the industry is going through serious financial pressure, I don’t think any hospital can afford to wait on this issue and get carried away with focusing solely on switching or upgrading EHR systems. Your hospital is just an event away from losing medicare payments due to patient mismatches. Someone can lose their life and the reputation of your organization will forever be tainted. That’s why many prominent healthcare providers have implemented our RightPatient biometric patient identification method to protect their patients and to protect themselves by preventing patient safety incidents caused by identification issues.

The curious case of a mistaken patient identity

May 10, 2019/7221 views / 0 Comments/in BLOG, Medical Record Mix-ups, Patient Errors, Patient Identification, Patient Safety /by Matt Gibson

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.

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.

Reducing opioid abuse by knowing the right patient

July 16, 2018/13577 views / 0 Comments/in AI, Analytics, Artificial Intelligence, Big Data, BLOG, Data Integrity, data mining, Health Care, healthcare, Medical Record Mix-ups, Patient Errors, Patient Identification, Patient Matching, Patient Outcomes, Patient Safety, population health, precision medicine, Predictive Analytics, value-based care /by Michael Trader

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

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

How did we get to this point?

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

There have been many changes regarding the prevalence of various diseases over the last three decades. Slowly and steadily, medicine has become dominated by chronic and painful health conditions. It is estimated that one-third of the U.S. population or 100 million Americans are living with a chronic and acute pain condition. Among them, one-fifth are living with moderate to severe pain. Considering these statistics, it follows that opioids would be widely prescribed. However, 8-12 percent of those prescribed opioids result in patients developing an addiction.

Opioid misuse is not just limited to those living with painful conditions. Many of the prescribed opioids end up in the wrong hands. Many addicted to opiates hide their identity or medical conditions and visit various clinics under different aliases. For doctors, it is challenging to identify the right patient.

How can we reverse the epidemic?

To bend the trend downwards, efforts must be implemented at every level. At the community level, we must educate the public and raise awareness about the health risks of opioid abuse. Policymakers should advance legislation to address the problem. Above all, there is a need to change the way medicine is practiced; healthcare providers must take higher precautions at the clinical level.

Clinicians cannot and should not deprive people in pain from drugs that can bring them needed comfort. However, big data and technology can assist them in differentiating between the right patient and the wrong one. This is where RightPatient can play a vital role. Powered by artificial intelligence, the platform can help clinicians to thwart medical identity fraud and ensure that a patient’s complete and accurate medical history can be retrieved.

By recognizing the correct patient, clinicians can better understand the validity of patient complaints along with a patient’s disease history. When and where was the patient last prescribed an opioid? Did the patient rightly identify himself/herself?

RightPatient can be one way to prevent opioid abuse.

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

February 9, 2018/14095 views / 0 Comments/in AI, Analytics, Artificial Intelligence, Big Data, BLOG, Data Integrity, data mining, Health Care, healthcare, Medical Record Mix-ups, Patient Errors, Patient Identification, Patient Matching, Patient Outcomes, Patient Safety, population health, precision medicine, Predictive Analytics, value-based care /by Mizan Rahman

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

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

Current state of U.S. healthcare

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

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

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

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

Challenges in moving to a population health solution

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

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

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

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

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

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

How RightPatient Prevents Chart Corrections in Epic and Other EHRs

January 29, 2018/12129 views / 0 Comments/in BLOG, Chart Corrections, Data Integrity, Health Care, healthcare, Medical Record Mix-ups, Patient Errors, Patient Identification, Patient Matching, Patient Outcomes, Patient Safety, value-based care /by Michael Trader

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

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

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

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

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

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

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