The Path Forward for Healthcare’s People Matching Problem

The following is a guest article by Rachel Podczervinski MS, RHIA, Vice President of Professional Services at Harris Data Integrity Solutions.

Efforts to identify the right path forward for healthcare’s patient matching problem are gaining a foothold as stakeholders from across the spectrum come together to remove obstacles and implement effective solutions. Most notably, in 2022, efforts by industry organizations like Patient ID Now led to the temporary removal of Section 510 from the U.S. House and Senate Labor, Health and Human Services, Education, and Related Agencies (Labor-HHS) appropriations bills. While it was ultimately reinserted in the final version, its initial removal by the Senate was the first time that chamber had done so in 20 years – a significant achievement in the fight to eliminate the outdated rider barring funding of research into a national patient identifier.

Meanwhile, the Office for the National Coordinator (ONC) through its Project US@ collaboration with standards development organizations and other stakeholders issued the Project US@ Technical Specification for patient addresses and the ONC-AHIMA Companion Guide containing operational guidance and best practices. The Patient ID Now coalition also issued its Framework for a National Strategy on Patient Identity: A Proposed Blueprint to Improve Patient Identification and Matching to guide creation of a national strategy for patient identification to ensure accurate patient matching and protect patient safety.

Even as progress is made, the patient matching problem is being exacerbated by the rapid growth in the volume of patient data being collected by healthcare organizations and patients themselves. Without standards and an improved patient matching strategy, the impacts of patient misidentification will only worsen, endangering lives and impacting the financial stability of hospitals, health systems, physician practices and clinics nationwide.

Why it Matters

Patient misidentification issues cost the average healthcare facility $17.4 million per year in denied claims and lost revenue. Further, according to Black Book Research, the expense of repeated medical care due to patient misidentification costs an average of $1,950 per inpatient stay and more than $800 per emergency department visit, while denied claims due to patient misidentification costs the U.S. healthcare system over $6 billion annually.

recent survey from HIMSS and Patient ID Now also found that healthcare organizations are spending an average of 109.6 hours per week resolving patient identity issues. Over half are spending 21-80 hours per week and have an average of 10 full time employees dedicated to patient identity resolution. This is despite nearly all participating organizations reporting that they had a unique patient identifier in place already. Further, more than one-third of responding organizations reported spending more than $1 million annually on identification resolution, including the cost of full-time employee salaries and benefits, technology, and software. Only 18% reporting spending less than $250,000 a year.

The cost of poor patient matching goes beyond financial. Lack of a consistent and accurate way to link patients to their health information is a significant contributor to an average duplicate record rate that runs as high as 18% in the typical facility. Further, as many as 20% of all records are incomplete. Thus, clinicians are forced to provide care based on a potentially incomplete picture of their patients.

The result of difficulties managing patient identities, according to a whopping 70% of respondents to the HIMSS/Patient ID Now survey, are duplicative or unnecessary testing or services. Among respondents, 67% also agreed that a lack of clear identities for patients put their organization at a higher risk for fraud, while 71% said it created identity verification and eligibility issues that made member enrollment and patient admission unnecessarily difficult. Seventy-eight percent reported that inconsistent identity data complicates workflows that span organizational boundaries, such as patient matching and eligibility.

Patient matching issues also impact response to public health emergencies, as was seen throughout the COVID-19 pandemic. Missing data, including an estimated 40% of demographic data missing from commercial laboratory test feeds for COVID-19, hindered contact tracing, vaccination, and public health reporting efforts. Patient ID Now also noted reports of vaccination registrations causing thousands of duplicate records within a single system – costing some hospitals and health systems at least $12,000 per day to correct – and of vaccination sites being denied additional vaccines because patient record systems erroneously showed patients as not having received previously administered vaccinations.

At least some of the blame for exacerbated patient matching issues has been laid at the feet of industry trends. For example, 77% of HIMSS/Patient ID Now survey respondents said that electronic health record (EHR) migrations or facility acquisitions have contributed to patient identity or duplication issues, while 71% pointed to portals allowing patients to self-schedule and/or register as contributors to the increase in duplicate record creation or identity issues.

Technology to the Rescue

Solving the patient matching problem requires a muti-stakeholder approach that starts with removal of Section 510 from the Labor-HHS appropriations budget to allow HHS to explore the feasibility of adopting a unique patient identifier. While this would set the stage for adoption of a national strategy for patient matching and identification, it is just the first step. What follows needs to be a collaborative effort between the private and public health sectors – something Patient ID Now did with the work group to develop its framework, which serves as an excellent blueprint for improving identification and matching.

At the organizational level, solving the patient matching problem will get a significant boost from deployment of the right technologies. Consider that while 95% of organizations responding to the HIMSS/Patient ID Now patient matching survey reported use of a unique patient identifier, just 20% reported having an identity management process or solution implemented – although nearly half indicated they planned to do so within the next year.

Clearly, a unique identifier on its own cannot adequately address patient misidentification. What is required is technology that can catch and correct errors and identify and resolve duplicate and overlaid records, all of which is necessary to improve existing systems regardless of the presence of a unique patient identifier. The right technology can also address the privacy and security concerns and ensure data integrity across the continuum.

Thus, the goal should be technology that enables end-to-end protection of the enterprise master patient index (EMPI) by operating in multiple environments and at multiple stages throughout the patient record process. For example, leveraging biometrics to collect a photo along with the information needed to create a patient record in the EMPI, and advanced deterministic and probabilistic matching algorithms to analyze and clean patient data before a record is updated. Or leveraging text messaging to send the patient a link to take and submit a selfie and photo of their driver’s license to validate their identity and search for any record matches before assigning biometric credentials to new patients. This validation and search would ideally be supported with facial recognition software.

In an end-to-end protection model, mismatches are caught upfront – something that EHRs cannot do on their own as most systems’ patient lookup functionality requires specific processes and data to be entered field by field, in just the right way. If even one detail is off, a search can yield invalid results leading to the creation of a new, duplicate patient record – adding to rather than resolving the problem.

Conversely, dynamic patient lookup solutions return instant patient results as they are typed into the system search bar, just like a web browser. Everyone involved in the patient matching process can narrow and refine results as they type to achieve positive patient identification.

Getting it Right at the Start

The first step of every patient encounter – and the grassroots solution to the nation’s patient identification problem – is accurately matching the patient to their medical record at the outset whether or not a unique patient identifier is in use. This makes registration, where 90% of patient record errors begin, a critical moment for eliminating medical errors, unnecessary costs, and safety issues associated with an EMPI tainted by duplicate records.

Deploying the processes, standards, and technologies to enable clean patient records at registration prevents downstream contamination into other departments, from clinical to imaging to billing, and enhances revenue cycle efficiencies to reduce days in A/R and decrease denials. Positive patient identification also enables digital transformation across the healthcare system, leading to improved interoperability, patient engagement and even improved patient access.

   

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