The healthcare industry faces various challenges in assessing the real-world effectiveness and safety of treatments outside controlled clinical trial settings. Traditional randomized controlled trials (RCTs) provide valuable insights but may not fully capture the diverse patient populations and real-world conditions encountered in everyday practice. Real-world evidence (RWE) addresses this gap by leveraging data from routine clinical care, electronic health records (EHRs), and insurance claims to offer a broader understanding of treatment outcomes and patient experiences. By analyzing RWE, stakeholders including physicians, regulators, and payers can make more informed decisions about treatment efficacy, safety profiles, and healthcare resource utilization.
With the rise of electronic medical records (EMRs), health claims systems, and advancements in computing power, the healthcare industry is undergoing significant changes. These developments have created a wealth of real-world data that helps us understand how healthcare services are used and how we can improve them. However, the real power lies in combining this data to get a full picture of how patients interact with the healthcare system.
Patient Journey Analytics, powered by RWE, is revolutionizing how healthcare providers understand and enhance the end-to-end patient experience. RWE plays a pivotal role in enhancing Patient Journey Analytics by integrating various data sources such as EMRs, health claims, patient feedback, and even social media. By using real-world data effectively, healthcare providers can gain insights that improve patient outcomes, streamline operations, and enhance overall healthcare quality.
This comprehensive approach allows healthcare providers to map out and analyses every stage of a patient’s healthcare journey—from initial symptoms through treatment decisions to long-term outcomes. Key aspects include:
- Data Integration and Analysis: Combining clinical data with patient-reported information enables a deeper understanding of patient behaviors and treatment responses. Advanced analytics, including Machine Learning (ML), are used to predict patient needs and optimize treatment pathways.
- Predictive Modelling: Leveraging RWE through ML enables healthcare providers to forecast treatment patterns, patient outcomes, and healthcare trends. This proactive approach aids in planning and delivering personalized care.
- Enhancing Treatment Effectiveness: Long-term RWE analysis helps assess treatment effectiveness over time, informing future strategies and resource allocation for improved patient care.
Thus, by breaking down healthcare system barriers and fostering collaboration, Patient Journey Analytics driven by RWE accelerates innovation and enhances operational efficiency across healthcare delivery.
RWE Project Stages
Below are the key project stages for RWE projects-
- Data Selection Based On Application
There is no single real world data source that provides comprehensive information across patient treatments. Some data sources provide data regarding patient’s prescriptions and help identify physicians and clinical sites, while other data sources provide deep information on individual patient healthcare including diagnoses and lab reports. Key business questions of clients usually help in determining real world data source selection. - Market Definition Followed By Cohort Finalization
This stage forms the foundation of real-world data analysis. Defining the market means identifying the scope of analysis—such as country, treatment setting (e.g., hospital or outpatient), or therapy type—based on the business objective. Once the market is clearly outlined, the next step is to define patient cohorts, i.e., groups of patients who share specific characteristics relevant to the study.Cohort finalization involves applying criteria such as diagnosis codes, treatment history, and timing of care to identify patients who meet the study objectives. For example, in an oncology study, cohorts may be created based on cancer type, stage of diagnosis, or treatment regimen. This step is crucial because the accuracy of cohort definitions directly impacts the quality of the insights generated. It ensures that the analysis is focused on the right group of patients and aligned with the key business questions. - Project Infrastructure Setup, Data Load & Quality Checks
The process of setting up the infrastructure depends on whether the data comes from the client or the company’s own sources. Setting up the system can take about a week, depending on the platform and the overall size of the data. Quality checks ensure the available data is reliable and ready for analysis. These data quality checks can include:- Data Feasibility – Making sure the data is complete and free from errors.
- Joining Data Sets – Combining different data sources correctly and verifying accuracy.
- Filtering and Processing Data – Ensuring only relevant data is used by applying necessary filters.
- Creating Transactional Records – Organizing data to track patient treatments over time while removing duplicate entries.
These steps help maintain accuracy and ensure meaningful insights can be drawn from the data.
- Business Rules Based On Therapeutic Area
Business rules form the foundation of Real-World Evidence (RWE) analysis. These rules are designed based on the therapy area, project requirements, and data availability. They guide how data is processed and interpreted to ensure meaningful insights. Some of the key business rules include:- Indication(disease) Assignment – Determines how a patient is classified under a specific condition or disease based on diagnosis codes, lab tests, or treatment history.
- Patient Eligibility – Evaluates whether a patient should be included in the study by analyzing their medical history, treatment records, and timeframes.
- Line of Therapy Assignment – Defines different stages of treatment based on changes in a patient’s prescribed regimen. This helps in understanding how treatment progresses over time.
Turning Data into Insights: Analytical Steps & Business Rules
RWE analysis involves multiple steps to transform raw data into useful insights. Below is an explanation of each step:
- Rx Fill Dates – Tracks when a patient refills their prescription. This helps determine if the patient continues or discontinues the treatment.
- Drug Episodes – Groups prescriptions into meaningful treatment periods to identify valid therapy combinations and treatment durations.
- Treatment Regimens – Defines the start of a treatment journey (index event) and establishes baseline and follow-up periods to assess treatment impact.
- Pre-/Post-Index Analysis – Examines how a patient’s treatment changes over time, including switching to a different therapy, adding new treatments, or discontinuing existing ones.
- Lines of Therapy – Maps out how patients move through different treatment stages, helping to identify patterns in patient care and business insights.
Each step in the analysis is interdependent, meaning a change in one step may affect all subsequent steps. Business rule parameters are flexible and rely on a combination of data analysis, medical expertise, and past precedents. Updating business rules often requires adjusting multiple steps, which can lead to additional rework. By structuring business rules and analytical steps clearly, RWE analysis can provide a more accurate and comprehensive understanding of patient journeys and treatment effectiveness.
Analysis Framework
The analysis framework helps shape commercial strategy using real world data. It can be customized based on specific business needs and includes the following key steps:
- Data Collection & Preparation: In this stage, experts identify relevant data sources (e.g., insurance claims, prescriptions). The data is then collected, checked for quality, and prepared for analysis.
- Defining the Target Population: In this stage, the data is structured to focus on the right group of patients. Medical codes (diagnosis, treatment, etc.) are mapped to specific conditions. Patients are grouped into relevant categories based on predefined rules.
- Patient Journey Analysis: In this stage, business rules are applied to track patient progress through different treatments. Data is processed to study treatment effectiveness and therapy transitions.
- Further Analysis & Insights In this stage, additional deep-dive analyses are conducted to answer specific business questions. Support is provided for customized data requests and ad hoc studies.
This structured approach helps businesses understand patient trends, improve treatment strategies, and make informed decisions.
Summary
Patient Journey Analytics powered by real-world evidence isn’t just about analysing data—it’s about using insights to innovate and collaborate across healthcare. By harnessing RWE, healthcare providers can navigate complexities more effectively, improve patient experiences, and achieve sustainable growth.
I am Pratik Jain working as a Senior Product Analyst and have worked in the past in the Pharma Consulting sector. I am motivated towards learning new technologies and workstreams which are shaping the modern Data Science and Product Landscape. I also love playing boardgames and badminton in my free time.
