The Golden Record: Achieving transformation with comprehensive data

Placing high-quality, interoperable data in longitudinal records can revolutionize patient care and address health disparities.

This article is Part 2 of a 2-part series. Read Part 1 here

There is a growing emphasis in healthcare on developing a comprehensive "golden record” that combines multiple data types, including clinical data, social determinants of health data, behavioral health data and consumer data to drive a more holistic approach to care delivery.

Drawing on my 20-year experience as a board-certified internist and pediatrician, I completely understand and acknowledge the profound importance of clinical data in this endeavor. An easily transactable “golden record” made of a rich data fabric is invaluable to facilitating a proactive patient journey, resulting in improved healthcare outcomes and reduced cost and waste.

However, creating a reliable and accurate longitudinal patient record that consists of high-quality and interoperable data presents significant challenges. Healthcare data is, by its nature, multi-source and multi-format, exchanged in a host of different electronic health record (EHR) “languages” and “dialects.”

To put the variation in perspective, imagine EHR A and B speak different languages, and EHR A’s implementation in one hospital is in a different dialect than it is at another. This language and dialect incongruence renders data potentially unusable because of inconsistencies in formatting and completeness. Non-conforming coding, incomplete normalization and limited data interoperability have significant repercussions throughout the entire continuum of care, particularly for vulnerable populations at risk.

Data quality and health disparities

Population level data reveals significant differences in incidence and prevalence rates of disease, hospitalizations, complications and mortality rates based on demographic factors such as race and ethnicity. This problem is exacerbated by the inadequate exchange of data between different episodes of care, particularly in those communities with the greatest care fragmentation.

Whether through direct cause or correlation, evaluating the quality of clinical data could prove valuable in navigating the increased realization that social factors impact outcomes of care more significantly than the care itself.

Availity’s clinical solutions team is collaborating with medical students from Rutgers’ New Jersey Medical School on a research project aimed at showcasing the significance of data quality assessment in addressing interoperability issues within electronic health records (EHRs) and its differential impact on various demographic sub cohorts.

Through this study, we have uncovered signals in clinical data that show that documentation and data quality is less robust in certain demographic groups, ultimately caused by the way these groups access healthcare and potentially worsening the quality of care at-risk groups receive. The project is examining and comparing demographic cohorts, across variables such as race, ethnicity, language, age, race-adjusted ZIP code income, religion and gender, to compare the comprehensiveness and quality of documents across different cohort groups.

Early results identify significant variance in data quality and completeness with vulnerable populations showing negative data capture across both measures, highlighting challenges in data equity for certain at-risk communities.

These insights showing the impact of social factors on patient care and the ability to accurately capture this information, combined with the current state of healthcare, also shed light on the opportunity for data quality assessment technology to serve as a valuable resource in identifying populations and patients within populations that are affected by these growing concerns. It is important to recognize, however, that ongoing measurement and feedback on multi-source data quality is just one step toward sustainable improvement of source clinical data.

Overcoming challenges with data assets

Despite its promise, the process of operationalizing and maximizing the investment in clinical data doesn't stop after data quality issues are identified. To effectively use clinical data as a strategic asset, the content that makes up the data and how it’s structured must be parsed, semantically normalized, enriched and synthesized.

Currently, the methods used to improve data quality involve skilled data scientists and analysts who utilize various tools to normalize and make clinical data structurally and semantically compatible. However, these traditional approaches are challenging and difficult to scale with the increasing volumes of clinical data and evolving data standards and terminologies. Consequently, these approaches are expensive, inefficient and limited in their effectiveness.

Healthcare organizations should prioritize making investments in technologies that enhance data quality and interoperability in real-time and at scale, enabling them to fully leverage the value of clinical data across various enterprise functions and optimizing return on investment. By investing in clinical data acquisition and transformative resources, healthcare organizations can convert raw clinical data into a valuable asset that is standardized, well-organized and actionable, returning many benefits.

For example, a patient’s medication record may be represented differently in the different EHR systems used by their primary care physician and specialist, resulting in medication duplication or worse, the prescription of a medication that is contraindicated when evaluated in their complete medication list.

Through the utilization of methods such as normalization, enrichment, reorganization and de-duplication, healthcare institutions can detect and harmonize disparities. This guarantees that the data remains up-to-date, consistently represented and precise across various care sites. Additionally, crucial metadata is appended to enable insights at both individual and population levels.

Embracing a scalable approach that involves collecting and semantically normalizing extensive data into a comprehensive longitudinal data asset has transformative implications. The effects of which go beyond merely capturing the complete narrative of each patient, regardless of their socioeconomic background — it propels care from a reactive approach to a proactive one.

Through the utilization of high-quality, interoperable, longitudinal data, we can expedite the dissemination of precise patient insights. This strategy not only elevates healthcare for payers and providers but, above all, enhances the well-being of the patients who lie at the heart of it all.

Paulo Pinho MD, is a vice president and medical director of innovation at Availity.

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