Rising to the challenge of data aggregation

Virtually everyone working in healthcare is aware of the challenges with data aggregation. Healthcare providers (HCPs) use different systems that don’t typically communicate well with each other. This makes it challenging for HCPs to get a holistic view of the patient throughout the full care continuum.

This is a critical challenge, because a clear view of population health – the ability to see individual health records and identify health trends that apply to the greater population – helps to circumvent prevalent issues, including the opportunity to discover health concerns and provide screenings and treatments within the window of opportunity. And to do this well, we must have more than a few records – we need volumes.

Add to this challenge the fact that patients often give alternative names – Jose, Joe, Joseph…Robert, Bob, Bobby…etc. Until the day we have a national patient identifier, it will remain challenging to know whether this is the same patient or different people.

Patient matching/record linkage (AKA “Master Patient Index”) is an integral component for this generation of healthcare metrics and the consolidation of multiple data sources. It is a method of finding all pairs of records that describe or belong to a single patient, assigning a Master Patient Index number to denote the unique patient, and determining the attribute values for that patient across all records deemed to be a match.

In 2019, Blaze Advisors developed a customized population health platform for our ONEcare networks to cut through these challenges and bring the patient back to the center.  The Clinical Intelligence Engine (CIE)© extracts and integrates data from disparate EHR and para-EHR sources (including but not limited to ADT, claims, pharmacy field, laboratory, remote monitoring, care management, ambulatory, and social services) into a master data aggregation tool/reporting engine, providing a comprehensive and holistic view of a patient’s interaction with medical and non-medical resources. The CIE uses analytics to identify key information from every record, such as first/last name, phonetic first/last name, DOB, and demography (including adjustments for misspellings, abbreviations, and nicknames) to determine if two records are the same person. Then, information about the same patient is combined into a single record, generating a clean dataset that’s ready to produce reports, key performance indicators, and, in general, to allow organizations to explore its own data across dimensions. We built the CIE  to be fully customizable and able to adapt to the information that’s available.  Flexibility is critically important in low-context environments, like a behavioral health network, where EHR adoption is limited and we must rely on para-EHR data to fill the gaps.  Too often, clinically integrated networks are hamstrung in connecting their non-standard EHR data, missing an opportunity to leverage community data for correlative research and analysis.

Blaze is hearing positive things. One client – a network of hospital systems – has been working in multiple EHRs for a long time. Now, with the CIE, they have a tremendous asset and, for the first time, an aggregated data source to examine and plan around population health. Since implementing the Clinical Intelligence Engine, this network has seen a significant uptick in critical, actionable information being available, which has streamlined their processes and helped their understanding not only of individual patients, but their own population as a whole.