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What is Big Data in Healthcare and How to Access It?

Datavant

August 21, 2023

5 Minutes

Table of Contents

According to Forrester Consulting, 82% of organizations demonstrating advanced analytics maturity have witnessed positive year-over-year revenue growth over the last three years.

Big data in healthcare holds the potential to transform patient care, population research, and operational strategies. As data takes the lead in driving insightful decision-making, the transformative impact on healthcare becomes increasingly evident.

However, despite data accessibility being more streamlined in other industries, the healthcare sector is still playing catch-up in this regard.

How Big Data in Healthcare is Different from Real World Data

Big Data in Healthcare: Big data in healthcare refers to the vast and diverse sets of information that are generated through various channels in the healthcare ecosystem. These data encompass clinical records, patient demographics, diagnostic images, genomic sequences, and much more. The distinguishing feature of big data lies in its volume, velocity, variety, and veracity. In essence, big data in healthcare encompasses information that is too extensive and intricate to be effectively managed and analyzed using conventional methods.

Real World Data (RWD): Real world data, on the other hand, encompasses all data that are collected outside the constraints of a controlled clinical trial environment. RWD includes data from electronic health records (EHRs), claims databases, patient registries, and wearable devices. While RWD is a subset of big data in healthcare, it provides a comprehensive view of patient health and treatment outcomes in real-world settings, offering insights into the effectiveness of therapies beyond the confines of clinical trials.

Types of Data in Healthcare and How They’re Generated

From electronic health records (EHRs) to genetic sequences, wearable device metrics to administrative records, the multitude of data streams generates various forms of data, such as:

Clinical data encompasses patient medical records, diagnostic images, lab results, and physician notes, offering a comprehensive view of patient health. EHRs capture patient information during clinical encounters. Lab tests and diagnostic imaging procedures generate data that contributes to medical histories and treatment plans.
Genomic data involves genetic sequences and markers, providing insights into individual genetic variations and disease predispositions. DNA sequencing technologies analyze an individual’s DNA, revealing genetic information. Genetic tests target specific genes or markers to assess disease risk and treatment options.
Wearable data includes health metrics like heart rate, activity levels, and sleep patterns collected by wearable devices and remote sensors. Wearable devices, such as smartwatches, continuously monitor physiological parameters. Remote patient monitoring employs medical sensors to transmit real-time health data to healthcare providers.
Administrative data comprises billing records, claims data, and insurance information, offering insights into services, diagnoses, and payments. Claims databases compile data from medical billing and insurance claims. Administrative records provide a financial perspective on patient care.
SDOH data includes demographic and geographic factors influencing health outcomes, providing context beyond medical data. Demographic data is gathered during patient interactions. Geographic data highlights location-based factors affecting health, like access to healthcare resources.
Patient-reported data includes self-assessments, experiences, and symptom tracking, contributing to a holistic view of patient well-being. Patients complete surveys or use apps to report symptoms, experiences, and medication adherence, enhancing patient engagement.
Research data involves clinical trial and biomedical research data, contributing to scientific advancements and treatment insights. Clinical trials generate data through controlled experiments. Biomedical research generates data from laboratory experiments aimed at understanding diseases and developing therapies.

Using Big Data in Healthcare

Access to big data in healthcare serves as a transformative force with the potential to enhance patient care, research advancements, and operational efficiency. Invaluable insights not only refine clinical decisions and predict disease trends, but drive other applications that include:

Predictive Analytics: Machine learning algorithms and AI thrive on patterns and correlations within vast datasets. They can help predict disease outbreaks, patient readmissions, and individual treatment responses. By identifying hidden relationships, these models enable proactive intervention and resource allocation.
Clinical Decision Support: Integrating analytics into clinical workflows empowers healthcare professionals with predictive insights. By analyzing patient data, including medical history, symptoms, and test results, machine learning models can assist in making accurate diagnoses and recommending optimal treatment plans.
Drug Discovery and Development: The process of identifying new drugs and treatment modalities is traditionally time-consuming and resource-intensive. Predictive analytics accelerates this process by sifting through massive datasets to pinpoint potential drug candidates and predict their efficacy, significantly reducing research timelines.
Population Health Management: Leveraging analytics enhances the precision of population health strategies. By analyzing diverse patient data, including demographics, socioeconomic factors, and health behaviors, healthcare systems can tailor preventive care programs and interventions to specific groups, improving overall health outcomes.
Personalized Medicine: Big data in healthcare enables the creation of patient-specific models that consider genetic, clinical, and lifestyle data to recommend personalized treatment plans. This approach enhances treatment effectiveness and minimizes adverse effects by accounting for individual variations.
Image and Signal Analysis: Machine learning algorithms excel at processing and interpreting complex medical images, such as MRIs and CT scans. They can accurately detect anomalies, aiding radiologists in early disease detection and improving patient outcomes.

Privacy and Security Considerations

Utilizing big data in healthcare requires significant privacy and security considerations. Organizations need to address:

Data De-identification: Ensuring that patient data is stripped of personally identifiable information before analysis.
Data Encryption: Implementing robust encryption methods to safeguard data during storage and transmission.
Access Controls: Restricting access to authorized personnel and ensuring compliance with regulations like HIPAA.
Consent and Transparency: Obtaining patient consent and maintaining transparency about data usage to establish trust.

Accessing Big Data in Healthcare

Organizations can access big data in healthcare through the Datavant ecosystem. Datavant connects disparate data sources, empowering organizations to gain a comprehensive view of patients, uncover new insights, and deliver on business objectives.

The Datavant ecosystem provides access and connectivity to various data, including:

SDOH data
Clinical data
Claims data
Patient records
Radiology images

Big data in healthcare holds immense potential in transforming healthcare. With Datavant, organizations can securely access healthcare data.

Spotlight on AnalyticsIQ: Privacy Leadership in State De-Identification

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."

Building Trust in Privacy-Preserving Data Ecosystems

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.

The Power of SDOH Data with Providers and Payers to Close Gaps in Care

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 Deploy Targeted Care Using SDOH Data

Payers increasingly leverage SDOH data to meet health equity requirements and enhance care delivery:

Tailored Member Programs: Payers develop specialized initiatives like nutrition delivery services and transportation to and from medical appointments.
Identifying Care Gaps: SDOH data helps payers identify gaps in care for underserved communities, enabling strategic in-home assessments and interventions.
Future Risk Adjustment Models: The Centers for Medicare & Medicaid Services (CMS) plans to incorporate SDOH-related Z codes into risk adjustment models, recognizing the significance of SDOH data in assessing healthcare needs.

Payers’ consideration of SDOH underscores their commitment to improving health equity, delivering targeted care, and addressing disparities for vulnerable populations.

Example: CDPHP supports physical and mental wellbeing with non-medical assistance

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:

Social isolation
Loneliness
Transportation barriers
Gaps in care

By integrating SDOH data, CDPHP enhanced their services to deliver comprehensive care for its Medicare Advantage members.

Providers Optimize Value-Based Care Using SDOH Data

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:

Onboard Patients Into Care Programs: Providers use SDOH data to identify patients who require additional support and connect them with appropriate resources.
Stratify Patients by Risk: SDOH data combined with clinical information identifies high-risk patients, enabling targeted interventions and resource allocation.
Manage Transition of Care: SDOH data informs post-discharge plans, considering social factors to support smoother transitions and reduce readmissions.

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:

Lack of Interoperability and Uniformity: Data exists in fragmented sources like electronic health records (EHRs), public health databases, social service systems, and proprietary databases. Integrating and securing data while ensuring data integrity and confidentiality can be complex, resource-intensive and risky.
Lag in Payer Claims Data: Payers can take weeks or months to release claims data. This delays informed decision-making, care improvement, analysis, and performance evaluation.
Incomplete Data Sets in Health Information Exchanges (HIEs): Not all healthcare providers or organizations participate in HIEs. This reduces the available data pool. Moreover, varying data sharing policies result in data gaps or inconsistencies.

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.

SDOH data holds immense potential in transforming healthcare and addressing health disparities.

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.