Building a successful clinical registry involves three critical steps: data acquisition, analytical transformation to identify meaningful
insights and dissemination of actionable recommendations to clinicians and stakeholders. Among these, the foundational step is
acquiring robust data. For a registry to achieve its potential, it must rely on high-quality data collected from various sources. This
guide delves into the types and sources of data you can leverage to construct a high-functioning clinical registry while addressing
potential challenges.
The Role of Data in Registries
Types of Clinical Registry Data
Registries rely on four primary categories of data:
– Clinical Data
– Patient-Generated Data
– Cost and Utilization Data
– Public Health Data
Each data type offers unique insights and utility. Let’s explore their characteristics, acquisition methods, and limitations.
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1. Clinical Data
What is Clinical Data?
Clinical data represents the cornerstone of most registries. It includes patient demographics, family history, comorbidities,
treatment histories, and outcomes. These data enable registries to support quality improvement, research, virtual trials, and stakeholder
activities.
Sources of Clinical Data
The primary source is the patient’s medical record, which provides comprehensive and longitudinal details about care delivery. Records
from hospitals, clinics, laboratories, and pharmacies paint a holistic picture of patient health, making them invaluable for registries.
Collecting Clinical Data from EHRs
Most healthcare facilities utilize electronic health records (EHRs), which can be integrated into registries. Flexible integration methods
are essential for harmonizing data from diverse systems. Clinical Research Organizations can support this process by designing solutions
that facilitate interoperability, enabling registries to aggregate data seamlessly across multiple EHR platforms.
Limitations of Clinical Data from EHRs
EHR data may lack the specificity required for high-impact quality improvement or research. Additionally, gaps in data related to
resource-intensive conditions can hinder comprehensive analysis. This can be mitigated by supplementing EHR data with electronic case report forms (eCRFs), enabling detailed clinical abstraction. Clinical Research Organizations bring value by implementing eCRF solutions, ensuring that registries capture comprehensive and accurate datasets to inform decision-making.
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2. Patient-Generated Data
What is Patient-Generated Data?
Patient-generated health data (PGHD) comprises health-related information recorded directly by patients or caregivers. Examples
include treatment histories, symptoms, biometric readings, and patient-reported outcomes (PROs).
Sources of Patient-Generated Data
PROs are a primary source of PGHD. These data are collected directly from patients without external interpretation, providing unique
insights into their experiences, quality of life, and outcomes. Other sources include wearable devices, mobile apps, and patient registries.
Collecting PRO Data
Effective PRO collection relies on user-friendly solutions, such as digital surveys. Engaged patients are more likely to contribute
accurate data. Clinical Research Organizations can enhance engagement by designing intuitive survey platforms tailored to diverse patient populations. Passive data collection from wearables or smart devices further enriches datasets without increasing patient or provider burden.
Challenges of Patient-Generated Data
Accuracy and engagement are key challenges. However, studies show a strong correlation between patient-reported and clinically documented data, affirming its reliability. Clinical Research Organizations add value by developing strategies to enhance patient participation, such as integrating communication tools, offering personalized experiences, and simplifying data-sharing methods.
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3. Cost and Utilization Data
What is Cost and Utilization Data?
Cost and utilization data evaluate healthcare value by comparing outcomes with costs. Different stakeholders define “cost” uniquely:
Providers: Expenses incurred during care delivery.
Payers: Payments made to providers.
Patients: Out-of-pocket expenses.
Healthcare utilization encompasses the reasons and frequency of service use, such as prevention, treatment, or information-seeking.
Sources of Cost and Utilization Data
These data are sourced from health insurers, government organizations, and public payers. Examples include claims data and datasets from organizations like the Centers for Medicare and Medicaid Services (CMS) and the Agency for Healthcare Research and Quality (AHRQ).
Challenges with Cost and Utilization Data
Claims data may be delayed, adjusted, or inconsistent due to coding changes, posing challenges for registry integration. Clinical Research
Organizations bring expertise in overcoming these issues by employing advanced analytics and designing models for longitudinal cost
assessments. These tools allow registries to capture the full spectrum of care value and support data-driven decision-making.
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4. Public Health Data
The Importance of Public Health Data
Public health data examines factors beyond clinical care that influence health outcomes, such as socioeconomic and environmental
determinants. These insights are essential for registries seeking to address broader community health objectives.
Sources of Public Health Data
Government datasets provide critical public health insights. Examples include:
Behavioral Risk Factor Surveillance System (BRFSS): Tracks health outcomes and behaviors.
CDC WONDER: Offers extensive public-use data on mortality, birth rates, and other metrics.
USDA Economic Research Service (ERS): Provides data on food access and its health implications.
Challenges and Integration
Combining public health and clinical data creates a multi-dimensional view of health, enabling registries to tackle complex objectives.
Clinical Research Organizations play a critical role in integrating these datasets, ensuring that public health and clinical information
are harmonized for a comprehensive understanding of health outcomes.
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Key Considerations for Building a Registry
To maximize a registry’s success:
Define Objectives: Align data collection with the registry’s specific goals.
Ensure Data Quality: Address gaps and limitations in datasets.
Use Advanced Tools: Leverage analytics and technology to streamline data acquisition and analysis.
Engage Stakeholders: Foster collaboration among data stewards, clinicians, and patients.
Clinical Research Organizations are instrumental in achieving these goals, offering expertise in data collection, integration, and analysis. Their contributions ensure that registries not only meet their immediate objectives but also deliver long-term value to healthcare systems and stakeholders. By leveraging Clinical Data Management Solutions and incorporating diverse data types, while addressing challenges proactively, registries can drive meaningful improvements in quality, research, and patient outcomes.
