The challenges facing the healthcare industry in 2020 and beyond are unprecedented in scope and complexity. Institutions must evolve and innovate to remain competitive, maximize performance, and generate revenue. With value-based care in motion, the coming year will see increased emphasis on consumerism, analytics and social determinants, as well as dependence on data sharing and greater collaboration among all healthcare players—from patient to health system to payer.
At the center of it all is the ability to correctly link individuals to their health data. If these initiatives are to succeed, the information stored and collected on patients must be readily available, accurate and complete. Here are three universal healthcare initiatives in 2020 that hinge on patient matching and identification.
Social determinants of health
With a greater understanding of the impact of socioeconomic and behavioral forces on one’s health, providers in 2020 are looking to tap social determinates data to gain deeper insights into their populations and effectively put those insights to work. Since social determinants now make up the majority of factors contributing to population health, building a total picture of an individual to offer intervention and support requires accurate patient identification and cross-system interoperability that extends out into non-clinical settings.
Frustrated with their EHRs inability to incorporate data from community service providers, progressive healthcare organizations in the new year will look to patient matching technology as a strategic advantage to integrate social determinants of health (SDOH) data more quickly and efficiently. Complementing extensive clinical data with SDOH data will allow care managers to make more informed care decisions and apply data-rich insights into a patient’s treatment plan.
Tools that offer reliable patient identification and medical records management facilitate the ability to track individuals uniquely across a diverse set of systems. This gives providers the opportunity to find potential gaps in care by seeing the entirety of a patient’s medical history.
Incorporating SDOH data into the EHR using patient matching technology can help organizations identify at-risk individuals and reduce readmissions among its vulnerable populations. By tracking individuals across disparate clinics and public health agencies, health IT leaders can leverage such data as food or housing insecurity and transportation issues into its care services.
Analytics and reporting
Today’s healthcare organizations, facing new competition from tech-first companies like Google and Apple, need unprecedented clarity and reliability of their data for reporting and analytics. Deriving value and insight from data is no longer a matter of availability and consumption. The data aggregated must be de-duplicated, free of errors and managed in a manner that moves securely with the patient across the continuum. Poor data quality undermines critical business objectives and leads to skewed analytics and ineffective or slow decision making. According to Forrester, nearly one-third of analysts spend more than 40 percent of their time vetting and validating their analytics data before making any strategic decisions. Poor data quality also hits an organizations bottom line, costing $15 million annually, according to Gartner’s 2017 Data Quality Market Survey.
Healthcare institutions looking to instill superior levels of accuracy in their data should invest in automated, enterprise-grade patient identification tools that facilitate fluid health information exchange and data integrity. Reliance on EHRs for patient matching can no longer keep pace with the volume and complexity of healthcare’s growing IT ecosystem. This is because master patient indexes (MPI) within EHR systems—built for single vendor-based environments—lack the sophisticated capability to link data across various settings and locations. Without common technical standards in place, EHRs continue to collect data in various formats that only serve to perpetuate the issue of duplicate and disparate information.
As providers become more dependent on population health and plan to adopt machine learning, AI, and precision medicine—all of which rely on high-quality data—use of robust, best-in-class tools that positively identify patients will be in high demand. Delivering the right information, on the right individual, at the right time is essential to helping busy clinicians and physicians provide the best care possible. Rethinking the current approach to maintaining data quality is the first step. Healthcare intuitions must stop believing that EHR data will hold any value if the information collected is inaccurate, incomplete, outdated, or inconsistent.
Consumer demand for a better patient experience is changing the business of healthcare, with 92 percent of healthcare consumers citing it as a top strategic priority for hospitals. Providers should be addressing consumer satisfaction and experience to effectively treat, attract, and retain patients in 2020.
A seamless registration process, simplified communication, and a tailored care plan that improves one’s health, are a few of many opportunities to improve the patient experience.
So, how can patient matching and identification help organizations improve the patient experience? Take for example the tedious and redundant chore of filling out forms at registration. Tasks like entering in your demographic info, verifying insurance, and conducting self-health assessments each time you visit your provider. We’ve all had the experience of having to tell our birthday and address to ten different people—even on the same visit. When individuals have a single best record that is shared across the network, it circumvents the need for consumers to repeatedly fill out the same forms over and over again.
When it comes to health data access, patients cannot be fully engaged in their care if their data is incomplete or worse, mingled with someone else’s. Today, individuals continue to struggle with ownership over their medical records and poorly designed portals have done little to enhance patient satisfaction scores. Improved patient access to data can flourish, however, when individuals are accurately identified and consistently matched to their data. For providers to successfully empower their patients with meaningful information to make informed decisions about their health, data must be free of errors, duplicates and incomplete information. When the consumer becomes a part of their own care team, they serve as yet another participant who can manage the complexities of their care. Further, with data no longer siloed in a system’s EHR, individuals can not only access their personal data but share it with who they deem appropriate.
Patient frustration tied to billing inaccuracies and poor communication can also be diminished when information is accurate and up-to-date. So, if a patient moves, marries, or changes their phone number, their demographic data is current across all touch points of care.
The issue of poor patient identification will become increasingly more problematic as more data and applications penetrate the health IT environment. As population health and accountable care take hold, healthcare institutions will find themselves under increased pressure to effectively identify, track and manage individuals across care settings.
As a result, organizations in 2020 will need unprecedented clarity and reliability into their patient’s medical record to avoid redundant or unnecessary tests and procedures, erroneous reporting and analytics, billing inaccuracies, administrative burdens, denied claims and lost revenue. To support informed clinical decision making, effective episodic care and cost management, and a better provider and patient experience will require a single, comprehensive view of one’s health history.
Organizations that rely on Epic or Cerner are not immune to this issue. This is because master patient indexes (MPI) within EHRs remain exceedingly limited in their ability to compare and link records from external sources. According to a 2018 report by Pew Charitable Trusts, EHR match rates within facilities are as low as 80 percent‑—meaning one out of five patients may not be completely matched to his or her record. When exchanging records outside the organization, match rates can be as low as 50 percent—even when the providers are running the same vendor EHR.
Leveraging an EMPI is an industry best practice, essential for promoting safety and interoperability and helping evolving healthcare enterprises map an individual’s entire care journey. According to a survey by Black Book Research, hospitals without an Enterprise Master Patient Index (EMPI) in place for managing patient identification reported duplicate record rates of 18 percent within their organization and 24 percent when exchanging records out-of-network. As a centralized, vendor neutral platform for enabling bidirectional access to patient information, an EMPI is critical platform to ensuring data flows freely (and accurately) from provider to provider. A robust, automated identity matching engine driving an EMPI that uses both probabilistic and deterministic matching algorithms to account for minor variations in patient data, will generate the single best record to link the right patient to the right data. Mature and reputable EMPI platforms can boost record matching accuracy as high as 99 percent.
If organizations fail to take patient matching and identification seriously, quality and safety, revenue and reimbursements, and patient and physician satisfaction suffer. When the same patient is accounted for multiple times or their health history resides in silos, coordination of care deteriorates, analytics are flawed, and quality reporting is askew.
With robust patient identification tools, however, healthcare delivery organizations can effectively improve the patient experience and lower costs, as well as keep pace with changing reimbursement models, technological innovations, and the consumerization and retailization of healthcare that will accelerate over the next decade.
This article was originally published on the NextGate Blog and is republished here with permission.