3 Real-World Applications: Improving Outcomes with Data Integration

3 Real-World Applications: Improving Outcomes with Data Integration

Data is the backbone of modern healthcare, yet traditional datasets
often provide incomplete and fragmented insights into patient care. In therapeutic areas such as cardiology, oncology and central nervous system (CNS) disorders, understanding treatment efficacy, patient outcomes and healthcare disparities requires more than isolated data points. 

Blended datasets can potentially bridge this gap by linking de-identified data including claims, mortality, social determinants of health (SDOH), lab results and genomic data, which can offer a more comprehensive, privacy-preserving approach to healthcare analytics.

Datasets are beginning to transform how we understand therapeutic impact, which means researchers and clinicians can gain a more comprehensive picture of treatment effectiveness and patient outcomes. This approach is particularly valuable in complex therapeutic areas such as cardiology, oncology and central nervous system (CNS) disorders, where patient journeys are complex and inconsistent and traditional data sources alone may provide an incomplete story.

The Mechanisms Behind Blended Datasets: A New Era of Interoperability

The power of blended datasets often lies in interoperability and the ability to seamlessly link disparate datasets while maintaining patient privacy and compliance with regulatory frameworks. In recent data innovation, tokenization can play a critical role in this process by de-identifying patient data and enabling privacy-preserving record linkage across multiple data sources.

By linking de-identified data including claims data with mortality records, lab results, electronic health records (EHRs), genomic data and SDOH factors, healthcare stakeholders can:

• Track long-term treatment efficacy beyond traditional clinical trial settings.
• Identify disparities in healthcare access and outcomes at a granular level.
• Improve clinical decision-making by integrating real-world data (RWD) into patient care pathways.

This approach enables researchers, payers and providers to leverage blended datasets securely and privately. It allows for a comprehensive analysis of treatment responses, incorporating clinical, environmental and social influences.

Three Real-World Example Applications to Drive Better Outcomes Through Data Integration

Blended datasets are already making a significant impact in tracking treatment outcomes, improving healthcare equity and accelerating drug development. Below are real-world applications of this approach in CNS, cardiovascular and oncology therapeutics.

1. Advancing Cardiology Research with Multi-Source Data Integration

Cardiovascular diseases remain one of the leading causes of mortality worldwide. However, traditional cardiology research often relies on claims and EHR data alone, missing critical lifestyle, behavioral and socioeconomic factors that influence heart health.

By integrating de-identified data, including SDOH data with clinical and de-identified claims datasets, researchers can:

• Identify geographic regions with higher heart disease prevalence and tailor public health interventions accordingly.
• Assess medication adherence patterns in relation to socioeconomic factors such as food insecurity or transportation access.
• Link mortality data with treatment adherence to understand which therapies result in the best long-term outcomes.

For example, a study could use blended datasets to analyze the impact of medication adherence among heart failure patients in different income brackets. Findings might reveal that patients in lower-income communities were significantly more likely to discontinue beta-blockers due to cost and access issues, reinforcing the need for tailored intervention programs.¹

2. Oncology: Enhancing Precision Medicine Through Linked Datasets

Oncology research thrives on granular, patient-level data, but many studies fail to incorporate critical RWD beyond clinical trials. Blended datasets allow researchers to merge de-identified data including claims, mortality, genomics and SDOH to better understand:

• Which treatments drive the best long-term survival rates across different demographics.
• How socioeconomic and environmental factors impact cancer progression and patient adherence to treatment.
• The role of genetic variations in treatment response, by linking clinical trials with post-market surveillance.

For instance, in breast cancer research, blended datasets have helped uncover disparities in treatment initiation among different racial and socioeconomic groups. By linking oncology registry data with SDOH factors, researchers found that patients in underserved communities often experience delays in receiving targeted therapies, leading to poorer outcomes.² This insight has driven policy changes aimed at improving access to precision medicine.

3. Central Nervous System (CNS) Disorders: Bridging the Data Gap for Neurological Care

CNS disorders—such as Alzheimer’s, Parkinson’s, and multiple sclerosis (MS)—are notoriously difficult to study due to their long progression timelines and the complexity of symptoms. Traditional datasets, such as claims and EHRs, offer only a partial view of the patient journey.

By incorporating blended datasets that merge:

• De-identified data including claims and prescription data to track medication adherence and healthcare utilization.
• De-identified SDOH to identify factors influencing disease progression (e.g., air quality and access to neurology specialists).
• De-identified mortality and long-term care records to analyze the effectiveness of interventions over time.

Researchers can uncover valuable insights into disease patterns, treatment response and long-term outcomes, leading to earlier interventions and more personalized treatment pathways.

The Future of Therapeutic Insights with Blended Datasets

The integration of blended datasets is redefining how we approach therapeutic research, clinical trials and healthcare delivery. By linking multiple de-identified data sources in a privacy-preserving and interoperable way, we can better understand disease progression, improve treatment access and drive more equitable healthcare solutions.

For organizations looking to leverage blended datasets for deeper therapeutic insights, download our tokenization infographic to explore how RWD data can transform healthcare analytics and decision-making.

_{¹https://ascpt.onlinelibrary.wiley.com/doi/10.1002/cpt.3526?af=R2 Oncology and Disparities in Cancer Outcomes
²Cardiology Research and Socioeconomic Factors in Heart Disease}