One of the most significant issues facing healthcare provider organizations today regards the accuracy of patient records. Healthcare facilities often fail to link records for the same patient. Such challenges can be costly to providers and patients.
Add to that the emergence of testing and vaccination centers during the pandemic. These centers are anywhere and everywhere. How does matching patient records function in this makeshift environment?
Healthcare IT News sat down with Clay Ritchey, CEO of Verato, a vendor of patient-matching technology, to talk about how patient matching technologies can help healthcare providers overcome the clinical and financial challenges posed by inaccurate patient records, particularly during the pandemic, as well as about their potential to improve healthcare delivery by tracking social determinants of health.
Q: How can providers address challenges around patient-record duplication and matching at testing and vaccination centers?
A. There is no question that patient identity and record matching are critical to fighting the COVID-19 pandemic, especially as they relate to contact tracing and vaccine deployment. Public health experts rely on patient data contained in disparate hospital, clinic and laboratory electronic health record systems to help determine testing, vaccination and care status of the U.S. population.
Unfortunately, EHRs are only as helpful as their ability to talk to each other. The fact is that healthcare facilities fail to link records for the same patient as often as half the time. A recent Duke-Margolis study also found that as much as 50% of COVID-19 laboratory reports prepared early on in the pandemic were missing addresses or ZIP codes, preventing many patients and their physicians from receiving their test results.
Identity resolution, or patient matching, solutions can without doubt help improve patient-matching rates. However, many such solutions are based solely on algorithms and probability, and research shows that relying only on these matching methods achieves accuracy rates of only about 65%. This means any given provider's patient records are inaccurate for about one out of every three patients.
Fortunately, new technology based on referential databases of records on virtually every U.S. citizen is becoming available, with automated patient-matching accuracy rates as high as 98%.
This level of patient matching performance coupled with modern cloud-based services to ease the interoperability challenges has the potential to dramatically improve the country's ability to track COVID-19 testing and vaccination, as well as increase patient data accuracy and reduce duplicate records across healthcare facilities.
It also can enable healthcare providers to match a patient's medical records to additional data about them, like social determinants of health, to provide a complete picture of each person and ultimately better care.
With complete data on patients, providers also can aggregate better population data and run analytics that show, for instance, whether the organization is disproportionately providing care to certain segments of the population and give clues as to how to address health inequities.
Q. You've said that poor patient matching is not only a healthcare risk, but that it also raises costs. How so? And what can provider organizations do to prevent this?
A. Poor patient-matching capabilities are costly. According to one survey, patient-matching issues cost an average of $1,950 per patient per inpatient stay, and more than $800 per ED visit. The survey also found an estimated 33% of all denied claims resulted from inaccurate patient identification or incorrect patient information.
Claims denied for these reasons cost the average hospital $1.5 million in 2017 and the U.S. healthcare system more than $6 billion annually. There is even a report that the care for an 11-month-old twin was documented in her sister's healthcare record, costing the health system $43,000 in unreimbursed payments.
Clearly, the ability to accurately match patients with their data is critical to every provider and payer's business success, especially considering that fee-for-service payment models have steadily declined as value-based models rise in popularity.
Succeeding in this environment, where organizations are paid based on patient outcomes and quality of their care, requires not only a thorough understanding of any given patient, but also any given patient population.
Providers must be able to identify those at risk for chronic conditions and proactively manage them. But they cannot do that, at least not efficiently or cost-effectively, if patients cannot be accurately matched to their data.
Another thing to keep in mind is that the healthcare system is becoming increasingly consumer-focused. People now expect to interact with their healthcare providers the same way they interact with their banks, utilities and e-commerce companies.
They are tired of filling out the same forms at every encounter and they want seamless, easy, virtual access to their data and services, no matter where they receive care – their family doctor's office, their specialist's office, a telehealth visit, an urgent care center in their town or a hospital in another state while on vacation.
Patients are no longer hesitant to jump ship for another provider who can give them what they want. And one of the best ways to acquire, engage and keep them is to show patients that you know them. Accurate identity-matching tools give providers the ability to do that – curate and cost-effectively deliver the specific information and services required by each patient across their care journey.
Q. What power can clean, properly matched health data bring to social determinants of health?
A. High-quality healthcare delivery is no longer just a matter of knowing what chronic diseases or conditions a patient has, treating them accordingly and sending them on their way. It's also important to know what issues they deal with outside of their health – their level of education, access to transportation, whether they are experiencing food insecurity or unemployment, their income, and more.
All of these play into their provider's ability to treat them, as well as their ability to comply with treatment and other aspects of their care.
Beyond building a complete and trusted picture of patients, enriching accurate EHR patient data with SDOH and identity information management can support patients by giving healthcare organizations the right context to reach at-risk individuals, anticipate outbreaks and ensure equitable outcomes.
As I alluded to previously, this is vital to contact tracing, analytics and outreach efforts in the pandemic, and will continue to play a critical role in healthcare delivery far into the future.
Moreover, incorporating that information into the EHR can help to ensure that no matter where a patient may be at any given time, all their information can be holistically available to the patient or their clinician to help them make better choices, whether they be reactive in the moment or proactive to help provide for good, long-term care coordination to patients with or at risk for chronic diseases.
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