A new, revised document detailing best practices for patient matching between health information exchange partners was released earlier this month by The Sequoia Project, a nonprofit organization chartered to advance the implementation of secure health information exchange nationwide.
The document, titled “A Framework for Cross-Organizational Patient Identity Management 2018,” includes a case study that illustrates a healthcare organization’s patient-matching success rate improving from 10% to 95%; it also draws a patient-matching maturity model and lists “minimally acceptable” patient-matching practices for CIOs, CTOs and other technology leaders, according to a Sequoia Project news release.
A Patient Identity Management Workgroup — created by The Sequoia Project and composed of multiple industry, academic and government experts — provided comments to develop final recommendations for improving patient identity management.
The workgroup also incorporated proposals supporting patient identifier challenges in pediatrics, as there is currently no widely employed naming system for newborns that have not yet been given a legal name. Some considerations listed in the document for handling pediatrics include standards adoption, information governance, process and technology, vendor capture of multiple birth indicator, birth order and mother’s maiden name, and creation of a medical record prior to the birth event.
Eric HeflinCTO for The Sequoia Project
Patient matching is one of the most “significant challenges” to nationwide health information sharing, Eric Heflin, document lead author and CTO for The Sequoia Project, based in Vienna, Va., said in the release.
“This paper provides a roadmap for advancing our national patient matching strategy,” Heflin said. “We hope to see organizations adopt these minimal practices and maturity model for patient matching with their external health information exchange partners. If we can standardize, in practice, how EMRs and HIOs leverage existing standards, we will increase patient match rates dramatically, even in the absence of having a national unique patient identifier.”
HealtheConnect Alaska partners with NextGate to improve patient matching
A health information exchange organization known as HealtheConnect Alaska has selected the Enterprise Master Patient Index (EMPI) platform by NextGate, which provides patient matching and identity management services, as the foundation for enhancing its patient identity management.
According to a NextGate news release, NextGate’s EMPI will enable real-time patient matching across the health IT systems of more than 20 hospitals and 450 healthcare organizations within HealtheConnect Alaska’s network, which currently enables electronic medical records exchange for more than 500 participants statewide, including AARP Alaska, Premera Blue Cross Blue Shield of Alaska and the state of Alaska’s Department of Health and Social Services.
The EMPI platform will be able to deliver a comprehensive view of a patient’s medical record. Using patient-matching algorithms, records will be unified and duplicate copies of data will be eliminated, while each individual is assigned a unique identifier serving as a cross-reference for greater data exchange. The EMPI platform will provide an opportunity for HealtheConnect Alaska to empower participating physicians and hospitals with an extensive patient record at the point of care to enhance clinical decision-making.
“Effective coordination between providers hinges on the ability to view accurate data from across the network,” Laura Young, executive director of HealtheConnect Alaska, said in the release. “The EMPI platform will play a significant role in our transformational journey toward improved care team collaboration, workflow efficiency, and the creation of a more holistic and real-time portrait of patients.”
Cybersecurity firm publishes ‘Healthcare Hacking Trends on the Dark Web’
Cynerio, an Israel-based cybersecurity startup that launched earlier this year, recently published a study, called “Healthcare Hacking Trends on the Dark Web,” which provides a look at the buying and selling of protected health information (PHI) in the dark web black markets.
PHI data illegally retrieved by hackers from healthcare organizations usually includes information such as Social Security numbers, birthdates, medical procedures and results and, in some cases, financial information, according to the study.
The study outlined what hackers tend to do with protected health information once they’ve acquired it, which includes selling it in bundles called fullz, which are personal information records that can be used for fraud and extortion, the study said.
Cynerio’s study concluded one of the main reasons healthcare providers’ databases are hacked is because most clinical systems are “poorly patched and communicate through unsecure channels,” which hackers take advantage of to retrieve sensitive information.
FDA permits marketing of AI algorithm to aid in detection of wrist fractures
The U.S. Food and Drug Administration (FDA) permitted the marketing last month of computer-aided detection and diagnosis software called Imagen OsteoDetect, which is designed to detect wrist fractures in adult patients.
The diagnostic software uses an artificial intelligence algorithm to analyze two-dimensional X-ray images for signs of a common type of wrist fracture known as a distal radius fracture, according to an FDA news release. OsteoDetect is able to analyze wrist radiographs using machine learning techniques, allowing it to identify and highlight areas of distal radius fracture. The software marks the fracture’s location on the image, which can aid the provider in both detection and diagnosis.
“Artificial intelligence algorithms have tremendous potential to help healthcare providers diagnose and treat medical conditions,” Robert Ochs, acting deputy director for radiological health in the Office of In Vitro Diagnostics and Radiological Health in the FDA’s Center for Devices and Radiological Health, said in the release. “This software can help providers detect wrist fractures more quickly and aid in the diagnosis of fractures.”