"Mortality should be a central endpoint in most health analytics – extending patient survival is, after all, the goal of diagnostic and treatment protocols. Thus, measuring survival (through mortality metrics) should be part of any analysis of treatment efficacy, safety, public health policy, and protocol design." - Jason LaBonte, CEO at Veritas Data Research.
Mortality data is a vital component of healthcare decision-making, providing invaluable insights for the industry. However, despite its significance, many healthcare organizations face numerous challenges and misconceptions that hinder the effective utilization of this critical resource. One major obstacle is the lack of comprehensive mortality data capture in clinical datasets commonly used for real-world data analytics, such as insurance claims or electronic health records, particularly when patients pass away outside of healthcare facilities. In fact, analysis conducted by Veritas Data Research shows that just 20% of U.S. deaths are recorded in medical claims.
Data quality discrepancies, challenges around standardization and consolidation, and privacy concerns further compound the difficulties faced by organizations that try to aggregate the numerous sources of mortality data themselves. Despite these challenges, mortality data has the potential to drive significant improvements in healthcare across various sectors:
Pharmaceutical companies can utilize mortality data to assess the efficacy and safety of their drugs, enabling them to make data-driven decisions in clinical trials and post-market surveillance, ultimately leading to the development of safer and more effective treatments.
Healthcare providers and payers can leverage mortality data to identify high-risk patient populations, develop targeted interventions, and optimize resource allocation, resulting in improved patient outcomes and reduced healthcare costs.
Analytics companies can harness the power of mortality data to develop advanced predictive models and risk stratification tools, empowering healthcare organizations to proactively identify and address potential trends, thereby enhancing population health management efforts.
Veritas, an innovator in developing industry-leading mortality and reference data solutions, indexes mortality data from a staggering 40,000 sources across the United States, ranging from online obituaries and funeral home notices to military and veterans cemetery listings and the Social Security Administration's Limited Access Death Master File. The sheer volume and diversity of these sources present a significant challenge to data collection, processing, and collation, requiring Veritas to employ sophisticated automation and data curation techniques to transform the raw, unstructured data into a standardized, high quality analytics-ready dataset.
Another hurdle is the successful integration of mortality data with existing healthcare analytics workflows, which demands careful planning, clear data governance policies, and the use of modern data sharing tools. Finally, while there are many real-world examples of successful mortality data integration, a common struggle faced by new users is the lack of access to specialized expertise to help them through the process. Veritas takes pride in its ability to help healthcare organizations overcome these challenges, empowering them to effectively leverage mortality data and drive meaningful improvements in patient care. With this in mind, Veritas has recently released its Verify solution, allowing customers to quickly know which individuals within their own data are deceased without needing to ingest Veritas's full historical mortality data file or build an internal identity resolution (matching) process to determine the status of individuals of interest.
While publicly available mortality datasets are generally considered "safe" from a privacy perspective, potential re-identification risks may arise on combination with other de-identified health datasets. For instance, in a study published in the Journal of the American Medical Association titled "Reidentification of Individuals in Chicago's Homicide Database: A Technical and Legal Study" (Ochoa et al., 2002)[1], researchers demonstrated that individuals could be re-identified by linking publicly available homicide records with other datasets containing demographic information. By combining this data with other publicly available datasets, such as voter registration records or property tax databases, the researchers were able to match the records and identify specific individuals in some cases.
This study highlights the inherent risk of re-identification when multiple datasets containing overlapping or complementary information are combined, even if each dataset alone does not directly identify individuals. To mitigate these risks when working with sensitive health data, healthcare organizations can implement strict data governance policies, including guidelines for data aggregation, de-identification, and access control. Developing a robust data governance framework and partnering with experts in data privacy is crucial for ensuring the integrity and responsible use of mortality data while maximizing data utility. For this reason, Veritas Data Research works with groups like Integral, who provide the expertise and the technology required to use mortality data in compliant and privacy-centric analytics.
One of the most significant challenges in prioritizing data security and privacy is achieving compliance with relevant regulations, which can be a complex and time-consuming process. As the demand for privacy-sensitive data continues to grow, particularly in areas like healthcare and mortality research, innovations in this space will be essential for enabling impactful analyses while maintaining confidence in data security and compliance.
"We've heard from many organizations that the process of working with privacy-sensitive data governed by regulations is often slow, opaque, and uncertain. Organizations want to use real data instead of synthetic data, but the compliance process acts as a major blocker to progress. By leveraging automation, we've developed a pre-purchase evaluation tool that enables users to quickly assess the privacy risks and compliance requirements of a dataset before purchase or use."- Shubh Sinha, Chief Executive Officer at Integral.
Integral's expert-in-the-middle approach and automation enable companies to quickly understand privacy risks and compliance requirements, streamlining the process of working with sensitive healthcare data.
In conclusion, healthcare organizations seeking to harness the power of mortality data must navigate a complex landscape of data quality, integration, privacy, and security challenges. By partnering with innovative companies like Integral, which offers best-in-class privacy solutions, and Veritas, which provides the most complete and accurate mortality data, organizations can quickly overcome these obstacles and unlock the full potential of this untapped data resource.
