When planning a study, selecting the right RWD source is just as important as framing the right research question. From #EHRs, claims, and registries to digital health data, each source brings unique strengths, and limitations. Sometimes one dataset is enough. But often, combining multiple data sources is the key to unlocking deeper insights, whether it’s understanding medication adherence in #Type2Diabetes, exploring the long-term cognitive impact of post-COVID syndrome, or linking lifestyle patterns to glycaemic control. The right data source can make or break your study.

1. Choosing the Right
Real-World Data
(RWD) Source:
Why It Matters

2. When planning a study, selecting the right data source is just as important as asking
the right research question. Each RWD type, whether EHRs, claims, registries, or digital
health data, has its strengths and limitations. Therefore, effective research starts with
choosing a data source that best aligns with your research question.
Quick snapshot for some commonly used data sources
EHRs
Claims
Strengths: Rich clinical detail, longitudinal patient history
Limitation: Often incomplete outside of clinical visits, limited
on outcomes like QoL or costs, varies by system
Appropriate for: Clinical outcomes, lab values, disease
progression, etc
Disease
Specific
Registries
Strengths: Disease-specific, curated data
Limitation: May not be population-representative
Appropriate for: Rare disease studies, outcomes research
Strengths: Structured, large-scale, payer-verified data
Limitation: Limited clinical depth, billing-focused
Appropriate for: Health service utilization and access to care,
medication adherence etc
Digital
Health
Data
Strengths: Real-time, continuous monitoring
Limitation: Data standardization, selection bias
Appropriate for: Behavioral insights, daily activity tracking

3. Which
data source
would you choose?

4. How does medication adherence affect
hospitalization risk in Type 2 Diabetes?
Claims data combined with EHR
Claims data shows whether medications were dispensed, while EHR data reveals
the clinical context—such as patient needs, response, and effectiveness. Together,
they provide a comprehensive view of medication adherence and its impact
on hospitalization risk.
Why combined
EHR data adds clinical depth like HbA1c levels, BMI, comorbidities, and lab results to
stratify patients by risk levels
Why EHR data
Claims capture prescription fills, refills, etc, ideal for tracking medication adherence
over time. Also provides insight into hospitalizations and healthcare utilization patterns.
Why claims data
1

5. What is the long-term cognitive
impact of post-COVID syndrome?
EHR + patient registries
2
EHRs provide real-world clinical context, while registries enrich it with patient-centric
outcomes. Together, they enable better longitudinal tracking and comparative
analyses across care settings.
Why combined
Specialized COVID registries collect and track patient reported outcomes,
neurocognitive assessment results, and long-term symptoms that cannot be
obtained from a typical patient visit in a traditional clinical practice (or even when
they occur at a health system). The registry data often includes research derived
variables such as measures of quality of life, which are not normally collected in EHRs.
Why patient registries
EHRs offer real-world clinical insights, such as diagnoses, lab findings, and cognitive
assessments. Longitudinal follow-up within hospital systems also helps identify
trends such as memory decline, fatigue, and brain fog.
Why EHR data

6. How do lifestyle patterns influence glycemic
control in Type 2 diabetes patients?
Digital health data + EHRs
Combining both creates a holistic view of the patient, enabling researchers to
link lifestyle patterns with glycemic control more accurately and identify
personalized intervention opportunities
Choosing one source may work. But often, combining multiple
datasets is the key to answering complex questions.
So what’s your go-to RWD source? Share your experiences!
Why combined
Includes HbA1c trends, medication changes, comorbidities, and physician
notes to correlate lifestyle factors with clinical outcomes
Why EHR data
Continuous glucose monitoring (CGM), dietary tracking apps, and wearable
activity data provide real-time insights into exercise, diet, and sleep patterns
that affect blood sugar levels
Why digital health data
3