Patient matching issues won’t go away with interoperability. Here’s why
Featured in Fierce Healthcare
Many are heralding implementation of the 21st Century Cures Act as the key to unlocking value in healthcare, ensuring that patient data can be easily shared among health systems, health information exchanges and payers to drive more personalized care. But targeted clinical interventions run the risk of not being optimized unless patients can be accurately matched to their medical records—and that’s an issue that demands a solution beyond the Cures Act.
Patient matching ensures that all of a patient’s medical information can be tied correctly to the individual. It relies on the data that are inputted at the point of registration—a person’s name, address, date of birth and phone number, for example—to uniquely identify the patient no matter where the individual seeks care.
But the data used to match patients to their medical errors are highly prone to error, whether due to inputting errors or because demographic information such as addresses or phone numbers can change over time. Issues also occur when individuals withhold or fail to correct personal information, like an error on their state-issued ID card.
When healthcare organizations have patient identity errors, they are limited in their ability to match the right record to the right patient, a scenario that heightens medical risk. It also leads to breakdowns in data sharing that prevent providers from fully understanding an individual’s health history. The result: missed opportunities to direct targeted care that improves health outcomes.
We saw this scenario occur repeatedly during the first months of COVID-19, when lack of data around social determinants of health such as food insecurity and unstable housing worsened COVID-19 outcomes in poorer communities. The impact will stretch beyond the pandemic, affecting mortality and morbidity in vulnerable populations for years to come. It’s an instance where access to data and data validity are crucial to supporting on-the-spot analyses that predict patient risk and support targeted interventions that meaningfully support health, both on-site and upon discharge.
How can healthcare providers work to eliminate patient matching challenges before the Cures Act is implemented, providing a stronger basis for value-driven care? Here are three approaches to consider.
Leverage referential matching
Referential matching quickly spots errors in patient demographic information by comparing the data with records retrieved from a continually updated, highly curated reference database of identities spanning the entire U.S. population. With this solution, EHRs can make the correct match immediately, or—when data in the patient record is different from what is expected—prompt patient registration staff to verify the data with the patient at the point of registration. Because referential matching supplements existing systems rather than replacing them, there is no danger of disrupting operations through use of this tool—critical when minutes count. By augmenting EHRs and data warehouses with referential matching, both providers and payers can bridge gaps in data from disparate sources, strengthening the ability to provide personalized care.
During COVID-19, referential matching helped identify homeless patients in New York City who tested positive for the virus and paired them with the resources needed for safe recovery. Homeless patients often provide proxy addresses—usually, the address of the shelter where they are staying. Unless healthcare providers know the addresses of shelters across the city, it becomes difficult to identify patients who lack stable housing and connect them with resources that address social determinants of health.
Moreover, patients who lack stable housing often use a different proxy address at each healthcare facility where they seek care, creating significant patient matching challenges even when interoperability is present. Healthix, a public health information exchange based in New York City and Long Island, identified proxy addresses for the homeless population and reconciled millions of patient records. It’s an approach that set the foundation for built-in clinical alerts that notify clinicians when a patient is likely homeless, strengthening care coordination for these patients as well as public safety.
Focus on data integrity in telehealth platforms
Demand for telehealth visits exploded during the initial months of the COVID-19 pandemic, when access to in-person care for nonemergent conditions was scarce. Early research suggests virtual visits have staying power, with more than 1 billion visits projected this year. However, the speed with which health systems cobbled together technology to advance telehealth during the pandemic means some telehealth platforms are not well integrated with existing data systems, such as the EHR. This often limits the ability of healthcare stakeholders to aggregate and centralize patient data, raising the potential for breakdowns in care.
At one large, integrated healthcare delivery system, demand for telehealth doubled immediately after the coronavirus outbreak, then jumped from 7,000 to 63,000 virtual visits between March and April. As the health system quickly ramped up its telehealth capabilities, the organization deployed a universal master patient index solution to strengthen the integrity of patient demographic information and support access to data around social determinants of health that could better inform care. With these data in hand, the health system’s clinical team has paired vulnerable patients with stable housing and other resources to help patients stay healthy, eliminate disparities in care and more tightly integrate care.
Reengineer the patient data collection process
Eighty percent of health information management directors say lack of consistent data collection standards often leads to incorrect or incomplete patient contact information. It’s a challenge that is exacerbated by the frequency with which patient demographic information can change—and it prevents providers from contacting patients most in need of care, including those who test positive for COVID-19.
One way to reengineer the patient data collection process is to look for areas where patient record discrepancies often occur due to a blank entry or a default entry in a key identifying field. Then, share the findings with team members who are in charge of inputting this information and invite their input on ways to adjust processes to ensure records are complete.
In Kentucky, efforts by the Kentucky Health Information Exchange to overcome data inconsistencies resulted in a 96% match rate for Medicaid members, laying the foundation for timely event notification and effective COVID-19 response.
Setting the stage for high-value interoperability
One of the biggest drivers for data interoperability is the ability to provide value-based care, 51% of healthcare technology executives agree. However, the ability to achieve value through interoperability depends on the ability to match the right patient to the right record, every time.
By working to eliminate patient matching challenges prior to implementation of the Cures Act, healthcare leaders can better position the industry to achieve the vision of interoperability shared by providers, payers and consumers—one that strengthens quality of care, reduces costs and results in a more tightly coordinated experience.