Claims data is generated whenever healthcare providers submit a request for payment to a health plan, providing information about the interactions between patients, healthcare providers, and insurers. The data typically include:
There are two types of medical claims data and they originate from two main sources, each shedding light on different aspects of healthcare utilization.
These are healthcare transactions that are in progress or have not yet reached their final resolution. It encompasses services that have been provided to a patient but are still in the process of being reviewed, adjudicated, or reimbursed.
Open claims are suitable for scenarios where a high volume of patients need to be captured, and a certain degree of missing data is acceptable, such as marketing use cases. This data tends to be more real-time.
They are generated by healthcare facilities that incorporate clinical data, which includes procedures, diagnoses, and encounters.
Conversely, closed claims signify healthcare transactions that have been finalized and resolved. The claims underwent review, processing, and reimbursement (if applicable), resulting in a determination of payment or denial. Closed claims are reflective of completed patient encounters, representing a comprehensive view of the care provided and associated costs.
Closed claims are suitable for scenarios where completeness of claims need to be captured, such as adverse events. This data tends to lag from weeks to months.
They originate from insurers, covering finances, coverage, and claims.
While the data offers valuable insights, it also presents challenges that must be carefully considered. It’s crucial to weigh the advantages and disadvantages before accessing and using claims data.
These limitations can be overcome or alleviated by strategically linking to real world data through other sources, such as electronic medical records (EMR), registries, and health data ecosystems. This allows for a more holistic and nuanced understanding of patient care, outcomes, and healthcare utilization.
By combining claims data with other real world data, more healthcare research applications become possible. From assessing treatment effectiveness to monitoring disease outbreaks and enhancing drug safety, it enables insights into real-world patient experiences and healthcare practices. Key applications include:
Comparative effectiveness research becomes possible. Organizations can evaluate the effectiveness of different healthcare interventions and treatments. By analyzing data from real-world patient populations, organizations can assess how various therapies perform in everyday clinical practice, providing valuable insights into treatment outcomes and patient responses.
Consider a study comparing the effectiveness of different antihypertensive medications in reducing cardiovascular events. Researchers can utilize claims data from a large and diverse patient cohort to determine which medications demonstrate superior outcomes in managing hypertension and preventing adverse cardiovascular events.
Situation: A biopharma company wanted to evaluate clinical and economic outcomes associated with lower disease activity states for patients with rheumatoid arthritis.
Need: The company needed to understand whether disease activity in rheumatoid arthritis was associated with adverse events, specifically hospitalization, emergency department visits, mortality, and medical costs.
Solution: By combining registry and claims data, the biopharma company was able to conduct clinical and economic assessments. This resulted in:
Policymakers, healthcare providers, and payers can focus on evaluating healthcare costs, resource utilization, and patient outcomes. This field of research informs decision making to improve the overall quality and efficiency of healthcare delivery.
A research study may examine the economic impact of implementing a new diabetes management program. By analyzing claims data, researchers can evaluate the program’s effect on hospital readmission rates, emergency room visits, and overall healthcare expenditures, helping organizations assess its cost-effectiveness and potential benefits.
Organizations can perform disease surveillance and epidemiological studies. By capturing a vast amount of patient information across different geographical regions, it allows organizations to monitor disease prevalence, incidence, and distribution in real-world populations. This helps identify potential health threats and guide public health interventions.
During a flu outbreak, researchers can use claims data to monitor trends in influenza diagnosis and treatment patterns. This information aids in predicting the spread of the disease, identifying high-risk populations, and informing targeted vaccination campaigns to mitigate the impact of the outbreak.
Situation: A biopharma company was conducting a national, placebo-controlled Phase III study to assess the safety and effectiveness of a new vaccine in adults 18 years and older.
Need: The company wanted to enable observation of infection rates and adverse events beyond the period of the trial, thus demonstrating long-term effectiveness of their vaccine. In order to do so, the client needed to find and connect to real world data (RWD) sources.
Solution: Through Datavant, the company linked clinical trial data with claims, electronic health records (EHRs), labs, and other sources of health information. They obtained the data necessary to complete the long-term evaluation of the vaccine’s safety and effectiveness.
Healthcare organizations can assess patient adherence to prescribed treatments and medications. Understanding patient adherence is crucial for evaluating treatment effectiveness and identifying opportunities to improve healthcare outcomes through better patient engagement and support.
Researchers can use claims data to analyze medication refill patterns and gaps in treatment for chronic conditions like diabetes. By identifying patients with poor adherence, healthcare providers can implement interventions such as patient education, reminders, or personalized care plans to improve treatment compliance and health outcomes.
Situation: A pharma company had an infusion therapy in a competitive oncology indication. The company noticed a significant drop in patient adherence.
Need: The company wanted to link patient data with claims and social determinants of health (SDOH) data to understand factors leading to the drop in adherence.
Solution: By linking claims data with patient and SDOH data through Datavant, the company:
Analysis structured around Current Procedural Terminology (CPT) codes is invaluable for understanding medical procedures and interventions.
However, claims data by itself offers limited understanding of patient conditions. To address this, integrating clinical data enriches analyses. Clinical data offers a broader context, detailing patient histories, diagnoses, and treatments, bridging the gap between procedures and individual health profiles.
Health data ecosystems, like Datavant, offer medical record retrieval services which provide a more comprehensive view of patients’ clinical state.
Researchers can use claims data to develop a predictive model for hospital readmission risk among heart failure patients. This model can help healthcare providers proactively identify patients with a higher likelihood of readmission, allowing them to provide targeted care and support to prevent unnecessary hospitalizations.
Situation: A large biopharma company with marketed immunotherapy was seeking to better understand the detailed treatment history and referral journeys of patients treated with their product.
Need: Their internal data only provided a window into a narrow part of the patient journey. The company needed a longitudinal view to drive therapy participation.
Solution: By linking claims data with first-party data through Datavant, the company was able to:
Privacy and data protection are paramount when working with claims data to ensure confidentiality and comply with ethical guidelines. Here are some measures that should be implemented:
Organizations can access a wealth of claims data through the Datavant ecosystem. Datavant connects disparate data sources, empowering organizations to gain a comprehensive view of patient populations, uncover new insights, and drive better outcomes.
Datavant ecosystem provides access and connectivity to various data sources, including:
Claims data has emerged as a powerful resource for real-world research, offering valuable insights into patient outcomes, healthcare utilization, and treatment effectiveness. Datavant empowers data-driven decisions by enabling organizations to securely access claims data.
AnalyticsIQ, a marketing data and analytics company, recently adopted Datavant’s state de-identification process to enhance the privacy of its SDOH datasets. By undergoing this privacy analysis prior to linking its data with other datasets, AnalyticsIQ has taken an extra step that could contribute to a more efficient Expert Determination (which is required when its data is linked with others in Datavant’s ecosystem).
AnalyticsIQ’s decision to adopt state de-identification standards underscores the importance of privacy in the data ecosystem. By addressing privacy challenges head-on, AnalyticsIQ and similar partners are poised to lead clinical research forward, providing datasets that are not only compliant with privacy requirements, but also ready for seamless integration into larger datasets.
"Stakeholders across the industry are seeking swift, secure access to high-quality, privacy-compliant SDOH data to drive efficiencies and improve patient outcomes,” says Christine Lee, head of health strategy and partnerships at AnalyticsIQ.
“By collaborating with Datavant to proactively perform state de-identification and Expert Determination on our consumer dataset, we help minimize potentially time-consuming steps upfront and enable partners to leverage actionable insights when they need them most. This approach underscores our commitment to supporting healthcare innovation while upholding the highest standards of privacy and compliance."
As the regulatory landscape continues to evolve, Datavant’s state de-identification product offers an innovative tool for privacy officers and data custodians alike. By addressing both state-specific and HIPAA requirements, companies can stay ahead of regulatory demands and build trust across data partners and end-users. For life sciences organizations, this can lead to faster, more reliable access to the datasets they need to drive research and innovation while supporting high privacy standards.
As life sciences companies increasingly rely on SDOH data to drive insights, the need for privacy-preserving solutions grows. Data ecosystems like Datavant’s, which link real-world datasets while safeguarding privacy, are critical to driving innovation in healthcare. By integrating state de-identified SDOH data, life sciences can gain a more comprehensive view of patient populations, uncover social factors that impact health outcomes, and ultimately guide clinical research that improves health.
Both payers and providers are increasingly utilizing SDOH data to enhance care delivery and improve health equity. By incorporating SDOH data into their strategies, both groups aim to deliver more personalized care, address disparities, and better understand the social factors affecting patient outcomes.
Payers increasingly leverage SDOH data to meet health equity requirements and enhance care delivery:
Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.
Capital District Physicians’ Health Plan (CDPHP) incorporated SDOH, partnering with Papa, to combat loneliness and isolation in older adults, families, and other vulnerable populations. CDPHP aimed to address:
By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.
Value-based care organizations face challenges in fully understanding their patient panels. SDOH data significantly assists providers to address these challenges and improve patient care. Here are some examples of how:
By leveraging SDOH data, providers gain a more comprehensive understanding of their patient population, leading to more targeted and personalized care interventions.
While accessing SDOH data offers significant advantages, challenges can arise from:
To overcome these challenges, providers must have robust data integration strategies, standardization efforts, and access to health data ecosystems to ensure comprehensive and timely access to SDOH data.
With Datavant, healthcare organizations are securely accessing SDOH data, and further enhancing the efficiency of their datasets through state de-identification capabilities - empowering stakeholders across the industry to make data-driven decisions that drive care forward.
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