Bridge the knowledge gap to strengthen cross-system patient identity management

Moving from one healthcare provider to another is not uncommon. Various reasons, such as patient satisfaction issues or the need for specialized medical care from multiple physicians, compel patients to visit different healthcare providers. According to one survey, patients in the United States see an average of 18.7 different physicians during their lives.

This movement throughout the health system creates a complex footprint of health services delivered and received, making it difficult to fully understand and track the entirety of patient care. These challenges can lead to duplication of health services, poor patient management, and data security concerns.

Managing patient identities is a complex process, especially when healthcare providers use different systems to collect and track patient information. Human errors and other identity discrepancies complicate this task even further. Many of these challenges are further exacerbated in low- and middle-income countries (LMICs) due to limited infrastructure and resource constraints.

Technologies to support unique patient identification or patient identity management (PIM) have been gaining traction as health systems in LMICs work to better link and manage patient data. In recent years, biometrics-based technologies have emerged as a potential solution for matching and identifying individuals. Given the nascent deployment of biometrics for health in LMICs, however, many practitioners are often left seeking implementation advice and lessons learned.

Bridging the knowledge gap

The Data Use Community (DUC), with support from the United States President’s Emergency Plan for AIDS Relief (PEPFAR), is a global community of practitioners consisting of government officials, implementers, technologists, and donors working to advance the use of data and technology to improve health outcomes. Adopting a practice-based community approach, the DUC brings together the people, tools, and technology to bridge the knowledge gap. The community meets monthly to share “best fit” solutions, in-country experiences, and lessons learned. At ICF, we support the DUC with operational and strategic guidance and provide technical assistance by developing resources and other artifacts for the community.

As interest in biometrics increased among DUC members, it became clear that a resource was needed for practitioners to maximize the potential of these technologies. We led the development of a Biometrics Module, part of the Patient Identity Management Toolkit, to provide an overview of the different types of biometrics, implementation considerations, and real-world experiences. Our team has also documented past presentations on biometrics from DUC members via case studies that provide a deeper look at the technologies used and lessons learned. These case studies break down complex, technical concepts into more tangible solutions.

Bringing people, technology, and data together

In 2023, the DUC had presentations from Nigeria, Haiti, Kenya, and Zimbabwe to share about their experiences implementing biometrics. Several overarching themes emerged out of these conversations. When thinking about implementing biometrics, there are three focus areas practitioners need to consider: The people involved with developing and using the technology, the different options and considerations for the technology itself, and the data needed to support the use of biometrics.

For each of these focus areas, there were recurring barriers faced by the different countries, regardless of context. When dealing with people, a lack of trust and limited capacity makes it difficult to scale nationally. Technology and infrastructure issues like unreliable connectivity hinder the ability to authenticate fingerprints in real-time, which is an important step of the matching process. In terms of data, duplicates and the quality of biometric capture was a challenge across countries.

There are also clear common enablers applicable across countries and contexts. For instance, engaging stakeholders early and throughout the planning and implementation process builds trust. Creating a centralized system that links with an existing system, such as a national data repository, can help with fingerprint data storage and the patient identification process during patient registration. Leveraging emerging technologies like an adaptive machine-learning algorithm that uses a mixture of attributes to identify and link patient records can be used to mitigate errors caused by duplicates and similar identifiers.