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Healthcare is poised to become the fastest-growing source of data in the world over the next year (RBC Capital Markets), accumulating from a wide variety of sources, including hospitals, doctors' offices, retail clinics and portable devices. Although this wealth of information holds immense potential to revolutionize healthcare, its precise management is tedious and its effective exploitation a challenge.
An important factor in this challenge is that correctly attributing data to an individual and linking the data together is non-trivial. Of course, this is essential to be able to interpret and use all the data effectively. Ensuring the integrity of identity information is essential: a person's data must be accessible, accurate, consistent, and in the appropriate context for the person receiving and using it.
Challenges of Maintaining Data Accuracy
According to the World Health Organization, “failure to correctly identify patients can cause many problems and have serious consequences for health care delivery.” The risk of duplicate, inaccurate, incomplete or inconsistent identity data in records creates wide-ranging problems. Inaccurate data leads to multiple appointments or tests, as well as processing delays.
Unfortunately, studies indicate that errors in matching records to the correct person occur up to 50% of the time. Health technology companies must ensure the accuracy and relevance of every information they collect, regardless of the wide range of diverse institutions from which it originates.
This includes data from primary care settings, hospitals, clinics, pharmacies, consumer-generated data, as well as IT systems such as EHRs, patient registries, radiology information systems, devices medical, etc. As more data is generated rapidly, the risk of inaccurate identity data also increases. As a result, internal teams and even customers are forced to spend time resolving duplicates, increasing total cost of ownership and reducing confidence in data quality.
To put this concern into context, consider a home blood glucose monitoring device. For device data to be actionable, information must be obtained not only from the device, but also directly from the consumer, the provider managing diabetes care, a pharmacy, and an insurer. It is highly likely that each of these sources uses a different system with inconsistent data cleanliness.
To complicate matters further, a person may also be known by different variations of their name in different systems – for example, Michael, Mike, Mickey. The complexity of matching all data from different sources and formats into one clean record makes the risk of error extremely high.
Effective management of personal data is table stakes
Enterprise Master Person Index (EMPI) technology plays a crucial role in solving data matching problems by providing a centralized repository for identity information, ensuring accurate matching of persons across various systems, data integrity and interoperability.
For health technology companies building innovative technologies and data-driven applications, an EMPI is a fundamental part of achieving and streamlining data accuracy within their solutions. This builds customer confidence in the quality of information provided by (or feeding into) the product, particularly when ingesting, integrating and reconciling data from multiple sources.
Some of the most advanced EMPI solutions are beginning to leverage AI and machine learning (ML) to automatically link people records, solving linkage and data quality issues. This reduces manual intervention by mirroring human decision-making to resolve data linkage and quality issues within an EMPI. Preferred actions are automated, improving the accuracy, consistency and credibility of downstream data and reducing the workload of data managers and data consumers.
Deciding to buy or build an EMPI
To achieve their growth goals and maintain customer satisfaction, successful health technology companies must be clear and invest in their core competencies and competitive differentiators instead of spending additional resources and developing infrastructure. Leaning on a reliable EMPI partner with a proven track record can help solve the challenges of streamlining and managing increasing amounts of data, resulting in high-quality data, faster time to market and reliable scalability.
EMPI solutions, complemented by AI The technologies provide a solid foundation for achieving accurate and consistent identification from sources across the healthcare ecosystem. For health technology managers and development teams, EMPI technology not only improves data integrity for their customers, but it also delivers efficiency and reduces risk for the product team and business of health technology. Highly accurate and current personal data ensures that the decisions and analyzes presented by the product are supported by reliable and reliable data.
About Jitin Asnaani
Jitin Asnaani is the Chief Product Officer at Rhapsody where Jitin leads Rhapsody product strategy and execution, with a focus on accelerating digital health transformation and adoption. He has extensive experience in interoperability and digital health, leading major industry initiatives such as CommonWell Health Alliance, Project Argonaut and Project Direct. Jitin also led business development at Bamboo Health and Health Gorilla.