Clinical Trials

Crafting Compelling Stories from Clinical Trial Data: Leveraging Real-World Insights

By | Clinical Trials

Key Takeaways:

  • Integrating elements of storytelling enriches the presentation of clinical trial data, making it more engaging and informative.
  • Analyzing raw clinical trial data reveals hidden trends and patterns upon which to build a compelling story.
  • Adhering to ethical and regulatory standards is imperative when crafting narratives from clinical trial findings.
  • Augmenting clinical trial data with real-world outcomes allows for a more comprehensive understanding of treatment effectiveness, representing diverse demographics and lifestyles often underrepresented in the controlled clinical trial environment.
  • Communicating the real-world impact of treatments beyond statistical outcomes is crucial for showcasing scientific advancement to a broad audience.

In pharmaceutical research and development, the path from clinical trials to market is strictly defined. It involves analyzing data, conducting research, and ensuring compliance with regulations. It is essential to follow these steps to ensure the safety and efficacy of the drug. Although clinical trial data are often presented scientifically, they contain the potential for powerful storytelling beyond statistical tables and regulatory filings.

In this blog post, we explore the art of storytelling with clinical trial data and discuss how real-world perspectives can augment these insights.

Structuring the Story

Great storytelling hinges on certain fundamental elements. Below are a few of these elements and ways each can be applied to clinical trial data to help inform and engage the audience.

  • Character. In clinical trials, “character” refers to the intervention under study. Highlight the features, benefits, and potential impacts of the intervention.
  • Plot. Create a clear storyline. Start with the problem (condition or disease), introduce an intervention (treatment), describe the process (methodology), and conclude with the results.
  • Setting. Provide context by explaining the background and significance of the studied condition and why the clinical trial is crucial for advancing treatment.
  • Conflict. Discuss challenges faced during the clinical trial, such as recruitment difficulties or unexpected side effects.
  • Resolution. Share the clinical trial outcomes. Present statistical results, efficacy, safety, and any breakthrough findings.
  • Visual aids. Incorporate visuals such as infographics, charts, and interactive dashboards to make complex information more accessible and engaging.

Uncovering the Story

Interpreting clinical trial results can be challenging. To ensure findings are accessible to a broad audience, it is important to construct a compelling narrative around the data.

Clinical trial data serve as the backbone of drug development. In raw form, these data include patient demographics, treatment protocols, treatment responses, adverse events, and efficacy outcomes presented as numbers, tables, and graphs. However, analyses are often required to help uncover the stories within clinical trial data.

Analyzing clinical trial data allows for realizing trends and patterns that are not immediately apparent. Whether it’s the impact of a specific treatment on a particular subgroup or long-term effects beyond the clinical trial period, these insights contribute to a richer and more comprehensive storyline.

Beyond numerical outcomes and statistical significance, the impact of treatment extends to its ability to address unmet medical needs and improve patient outcomes. A balanced presentation of risks and benefits empowers healthcare professionals, policymakers, and patients to understand the data’s implications and make conscientious decisions.

Maintaining ethical standards and transparency is crucial to this process. Adhering to regulatory guidelines and clearly articulating limitations and biases ensures integrity in storytelling with clinical trial data.

Supporting the Story

Clinical trials are only one phase of a drug development lifecycle. Understanding the long-term safety and effectiveness of a treatment often requires evaluation beyond the controlled clinical trial environment.

For example, supplementing clinical trial data with real-world outcomes helps validate hypotheses, identify potential biomarkers, and uncover post-marketing insights. This approach accommodates variations in demographics, socioeconomics, and lifestyle choices often excluded from controlled clinical trials.

In addition, communicating how a treatment translates into improved quality of life or reduced healthcare burden further solidifies the narrative.


Clinical trial data are critical for advancing healthcare but are often challenging to interpret. Transforming clinical trial data into compelling stories involves focusing on the character, plot, setting, conflict, and resolution of the study while enriching the data with real-world perspectives. Crafting narratives that resonate with diverse stakeholders is crucial for conveying the true impact of treatments driving research, and advancing healthcare for the benefit of everyone.

Health data

Understanding Key Health Data Types: Clinical Trials, Claims, EHRs

By | Clinical Trials, EHR, Health Data Types

Key Takeaways:

  • Key healthcare data types include clinical trials, insurance claims, and electronic health records (EHRs), each with distinct purposes.
  • Clinical trial data directly captures efficacy and safety of interventions, but availability is limited until publication and may lack generalizability.
  • Insurance claims provide large-scale utilization patterns, outcomes metrics across diverse groups, and cost analysis, but lack clinical precision.
  • EHR data offers longitudinal individual patient history and care details in operational workflows but quality and standardization varies.
  • Combining evidence across clinical trials, claims data, and EHRs enables real-world monitoring of interventions to guide optimal decisions and policies.

In an era of big data and analytics-driven healthcare, evidence informing clinical and policy decisions draws from an expanding variety of data sources that capture different aspects of patient care and outcomes. Three vital sources of health data include structured databases tracking results of clinical trials, administrative insurance claims systems, and electronic health records (EHRs) compiled at hospitals and health systems. Each data type serves distinct purposes with inherent strengths and limitations.

This article explains the defining characteristics, appropriate use cases, and limitations of clinical trials, insurance claims data, and EHRs for healthcare and life science researchers, operators, and innovators.. Combining complementary dimensions across data types enables robust real-world monitoring of healthcare interventions to guide optimal decisions and policies matched to specific populations.

Clinical Trials

The randomized controlled trial (RCT) serves as the gold standard for evaluating safety and efficacy of diagnostic tests, devices, biologics, and therapeutics prior to regulatory approvals. Clinical trials compare treatments in specific patient groups, following strict protocols and monitoring outcomes over a set study period. Data elements captured include administered treatments, predefined clinical outcomes, patient-reported symptoms, clinician assessments, precision diagnostics, genomic biomarkers, other quantifiable endpoints, and adverse events.

RCT datasets supply the most scientifically valid assessment of efficacy and toxicity for an intervention compared to alternatives like placebos or other drugs because influential variables are intentionally balanced across study arms using eligibility criteria and random assignment. This internal validity comes at a cost of potentially reduced generalizability and applicability. As a result there is a challenge in translating benefits and risks accurately into heterogeneous real-world populations. Published trial findings often overstate effectiveness when applied more broadly. Additional data from pragmatic studies is needed to complement classical efficacy findings along the product lifecycle.

Supplemental data integration is required to expand evidence beyond the limited snapshots of clinical trial participants and into continuous monitoring of outcomes across wider populations who are prescribed the treatments clinically. Here the high-level perspectives of insurance claims data and granular clinical details contained in EHRs play a vital role.

Insurance Claims

Administrative claims systems maintained by public and commercial health insurers serve payment and reimbursement purposes rather than research goals. Yet analysis of population-level claims data containing coded diagnoses, procedures performed, medications dispensed, specialty types, facilities visited, costs billed and reimbursed enables important usage trends, treatment patterns, acute events, and cost efficiency insights which complements clinical trials.

Claims provide researchers a broad window into diagnoses, prescribed interventions, and health outcomes frequently spanning millions of covered lives across geographical regions that are absent from most trials. Claims data encompasses at all covered care delivered rather than isolated interventions. Examining trends over longer timeframes across more diverse patients who differ from strict trial eligibility enables assessment of real-world utilization frequencies, comparative effectiveness versus alternatives, clinical guideline adherence, acute complication rates, mortality metrics, readmission trends, and direct plus indirect medical costs.

However, claims data lacks the precise clinical measures systematically captured in trials and EHR records. Billing codes often fail to specify clinical severity or capture quality of life impacts. Available data elements focus primarily on how much and how often healthcare services are used rather than qualitative clinical details or patient-reported outcomes. Underlying diagnoses and accuracy of coding may require supplementary validation. Despite its limitations, claims data plays a crucial role in providing essential information for healthcare professionals, researchers, and policymakers. It serves as a valuable tool for monitoring diverse aspects of the healthcare system, ultimately contributing to the assurance of efficient, safe, and effective treatments.

While abbreviated claims codes document utilization events at a population level and clinical trials quantify experience for circumscribed groups, the patient-centric Electronic Health Record (EHR) details comprehensive individual-level clinical data as an immutable ledger accumulated over years of clinical encounters across care settings. The longitudinal EHR chronicles detailed diagnoses, signs and symptoms, lab orders and results, exam findings, procedures conducted, prescriptions written, physician notes and orders, referral details, communications around critical results, and other discrete or unstructured elements reflecting patient complexity often excluded from claims data and trials.


EHRs provide fine-grained data for precision medicine inquiries into subsets of patients with common clinical trajectories, risk profiles, comorbidities, socioeconomic factors, access challenges, genomic risks, family histories of related illnesses, lifestyle behaviors like smoking, and personalized interventions based on advanced molecular markers. EHR data supports deep phenotyping algorithms and temporal pattern analyses that can extract cohort comparisons not feasible solely from claims.

Secondary use of EHR data faces challenges in representativeness when drawing data from single health systems rather than national networks, variability in coding terminologies and data entry fields across platforms, fragmentation forcing linkage between separate specialties and sites of care, semi-structured formats with mixed discrete codified and free text variables, and data quality gaps during clinician workflow constraints. Population-based claims data ensures inclusion of patients seeking care across all available providers rather than just one health system.

Integrating Complementary Evidence

Definitive clinical trial efficacy remains the gold standard when initially evaluating medical interventions, while large-scale claims data offers a complementary view of broader utilization patterns and comparative outcomes across more diverse populations who are receiving interventions in clinical practice. However, as interventions diffuse beyond the research setting, reliable acquisition of clinical details requires merging population-based signals from claims with deep clinical data contained uniquely within EHRs.

Combining evidence across clinical trials, claims databases, and EHR repositories maximizes strengths of each data type while overcoming inherent limitations of any single source. Clinical trials determine effectiveness, and combining insights from large-scale claims data with detailed clinical information in EHRs is crucial for assessing interventions as they transition from research to practical healthcare, contributing to overall healthcare improvement.


Aspect Clinical Trial Data Claims Data EHR Data
Primary Purpose Research and development of new treatments Billing and reimbursement for services Patient care and health record keeping
Data Source Controlled clinical studies Insurance companies, healthcare providers Healthcare providers
Data Types Included Patient demographics, treatment details, outcomes Patient demographics, services rendered, cost Patient demographics, medical history, diagnostics, treatment plans
Data Structure Highly structured and standardized Structured but varies with payer systems Structured and unstructured (e.g., doctor’s notes)
Temporal Span Limited to the duration of the trial Longitudinal, covering the duration of coverage Longitudinal, covering comprehensive patient history
Access and Privacy Restricted, subject to clinical trial protocols Restricted, governed by health insurance portability and accountability act (HIPAA) regulations Restricted, governed by HIPAA and patient consent
Primary Users Researchers, pharmaceutical companies Healthcare providers, payers, policy makers Healthcare providers, patients
Data Volume and Variety Relatively limited, focused on specific conditions Large, diverse covering a wide range of conditions and services Large, diverse, includes a wide range of medical information
Use in Healthcare Drug development, understanding treatment effectiveness Healthcare economics, policy making, fraud detection Direct patient care, diagnosis, treatment planning
Challenges Limited generalizability, high cost Variability in coding, potential for missing data Inconsistent data entry, variability in EHR systems



Clinical Trials vs. Real-World

Clinical Trials vs. Real-World Data: Understanding the Differences and Complementary Roles

By | Clinical Trials

Key Takeaways:

  • Clinical trials are controlled experiments designed to evaluate safety and efficacy of new drugs or devices. Real-world data comes from more diverse, less controlled sources like electronic health records and medical claims.
  • Clinical trials have strict inclusion/exclusion criteria and measure predefined outcomes. Real-world data reflects broader populations with various comorbidities and outcomes.
  • Clinical trials are required for regulatory approval but have limitations like small sample sizes. Real-world evidence can complement trials with larger volumes of data over longer time periods.
  • Real-world data comes from routine clinical practice rather than protocol-driven trials. It provides supplementary information on effectiveness and safety.
  • Limitations of real-world data include lack of randomization, potential biases and confounders. Analytic methods help account for these limitations.
  • Real-world evidence has growing applications in medical product development, post-market surveillance, regulatory decisions and clinical guideline development.

Clinical Trials vs. Real-World Data

Clinical trials are prospective studies that systematically evaluate the safety and efficacy of investigational drugs, devices or treatment strategies in accordance with predefined protocols and statistical analysis plans. They are considered the gold standard for assessing the benefits and risks of medical interventions prior to regulatory approval. In clinical trials, participants are assigned to receive an investigational product or comparator/placebo according to a randomized scheme. These studies are designed to minimize bias and carefully control variables that may affect outcomes. Participants are closely monitored per protocol, and data is collected on prespecified points in time. The resulting evidence from randomized controlled trials serves as the primary basis for regulatory decisions regarding drug and device approvals.

In contrast, real-world data (RWD) refers to data derived from various non-experimental or observational sources that reflect routine clinical practice. Sources of RWD include electronic health records (EHRs), medical claims, registry data and patient-generated data from mobile devices, surveys or wearables. Real-world evidence (RWE) is the clinical evidence generated from aggregation and analysis of RWD. While clinical trials evaluate medical products under ideal, controlled conditions in limited samples of patients, RWD offers information about usage, effectiveness and safety in broader patient populations in real-world settings.

Some key differences between clinical trials and real-world data:

Sample Populations – Clinical trials have strict inclusion and exclusion criteria, resulting in homogeneous samples that often under represent minorities, elderly, pediatric and complex patient groups. RWD reflects more diverse real-world populations with various comorbidities and concomitant medications.

  • Settings – Clinical trials are conducted at specialized research sites under tightly controlled conditions. RWD comes from routine care settings like hospitals, clinics and pharmacies across diverse geographies and populations.
  • Interventions – Clinical trials administer interventions per protocol. RWD reflects variabilities in real-world treatment patterns and patient adherence.
  • Outcomes – Clinical trials measure prespecified outcomes over limited timeframes. RWD captures broader outcomes like patient-reported outcomes, quality of life, hospitalizations and costs over longer periods in real-world practice.
  • Data Collection – Clinical trials collect data per protocol at predefined assessment points. RWD is collected during routine care and reflected in patient records and claims.
  • Sample Size – Clinical trials often have small sample sizes with a few hundred to several thousand patients. RWD encompasses data from tens or hundreds of thousands of patients.
  • Randomization – Clinical trials use randomization to minimize bias when assigning interventions. RWD studies are observational without the benefits of randomization.

While randomized controlled trials provide high quality evidence for drug/device approvals and clinical recommendations, RWD offers complementary information on effectiveness, safety, prescribing patterns and health outcomes:

  • RWD can provide broader demographic representation for subpopulations underrepresented in trials.
  • RWD can inform on long-term safety, durability of treatment effects and comparative effectiveness between therapies.
  • RWD can provide larger sample sizes to study rare events or outcomes.
  • RWD can reflect real-world utilization rates, switching patterns and adherence to therapies.
  • RWD offers granular data for personalized medicine, risk identification, prediction modeling and tailored interventions.
  • RWD is more timely, cost-effective and scalable than conducting large trials.

However, RWD has inherent limitations compared to clinical trials:

  • Lack of randomization increases potential for bias and confounding.
  • Incomplete data or misclassification errors are common with medical records.
  • Inability to firmly conclude causality due to observational nature.
  • Possible selection biases and variations in care delivery across settings.
  • Inconsistencies in definitions, coding, documentation practices over time and sites.

Analytical methods help account for these limitations when generating real-world evidence from RWD:

  • Advanced analytics like machine learning can identify trends and associations within large RWD.
  • Predictive modeling and simulations can estimate treatment effects.
  • Adjusting for confounders, stratification, matching patients, propensity scoring help reduce biases.
  • Expert review of data and methodology helps ensure reliability.

Applications of RWE are expanding and gaining acceptance from key stakeholders:

  • Supplement clinical trial data for regulatory, coverage and payment decisions around medical products.
  • Post-market surveillance of drug and device safety and utilization in real-world practice.
  • Life cycle evidence generation for new indications, formulations, combination products.
  • Provide inputs into clinical guidelines by professional societies.
  • Risk identification/stratification, predictive modeling and personalized medicine.
  • Value-based contracting between manufacturers and payers.
  • Risk management and safety programs for hospitals and health systems.

In summary, clinical trials provide foundational evidence to introduce new medical products, while RWE offers complementary insights on effectiveness, safety, prescribing patterns and health outcomes at a larger scale across diverse real-world populations. Advanced analytics help derive meaningful RWE from RWD, with growing applications across the healthcare life science ecosystems. Together, these sources of evidence offer a multifaceted understanding to guide optimal use of medical products and improve patient care.


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