Use RWD, SDOH and patient mapping to eliminate blind spots in patient journey

Although most diseases are well defined, patent experiences for people with the same condition may vary for a number of reasons. It has a great physical and psychological impact on the patient. Complications, genetics, financial status, social life, race, life experience, and environmental factors also play a role. This is why clinicians and pharmaceutical companies need to consider real-world data (RWD) for drug development. Without this critical information, clinicians and drug developers create “blind spots” that prevent them from fully understanding the status of effective treatment and what they need. A recent webinar held by MedCity News and Purplelab explores the importance of patient-reported results and is critical to fixing these blind spots through non-small cell lung cancer (NSCLC) lenses.

RWD is an essential tool for identifying and highlighting important risk factors that are often overlooked. These data, such as social determinants of health and socioeconomic barriers, provide a more comprehensive view and reveal these “blind spots” that traditional clinical trials are based on their designs that simply cannot be done.

The primary purpose of clinical trials is to evaluate the effectiveness and safety of new treatments, usually compared to current best practice treatments. The clinical trial protocol provides information on all aspects of the study, from the diagnostic testing of biomarkers to the administration of therapeutic modes such as surgery, chemotherapy, targeted therapy or immunotherapy.

Patient-centric results

One challenge in clinical trials is that recruiting participants can take up to one year. Work and family commitment poses potential barriers in terms of routine, face-to-face attendance. The clinical nature of the study means that participants’ vital sign data are often different from their home environment. Changes in drug adherence, daily challenges, and emotional loss with chronic diseases differ for each clinical trial participant and may have a significant impact on clinical trial outcomes.

Despite randomized clinical trials in people with NSCLC trace tumor response and survival, the nuances of this disease significantly affect the quality of life of patients with disease, broadly speaking, which clinical studies failed to capture. Pain and fatigue are the most common symptoms and can cause emotional damage over a prolonged period of time. They lead to inability to move, make walking difficult, contribute to restless sleep, and pose challenges at work. However, this information has not been systematically recorded or evaluated in clinical data.

Although clinical trials require high levels of patient adherence to be effective, compliance can be complex. Unmanaged side effects, comorbidities such as depression, transportation and occupational needs can make the patient a basis for compliance.

Social determinants of health

Defined as a non-medical factor that affects the patient’s living time and quality of life, the Social Determinant of Health (SDOH) covers the social, economic and environmental conditions faced by individuals and their communities. SDOH is particularly relevant to oncology and is increasingly seen as a key factor affecting health and quality of life outcomes. Some studies have combined SDOH factor with 75% of cancer. SDOH is considered an independent risk factor for poor health, exacerbating inequality throughout the cancer care continuum.

Given that NSCLC is a respiratory disease, it is not surprising that the environment plays an important role in the journey of NSCLC patients, as noted in Webinar as the main information and informatics system as noted with the GCS computation catalyst of GENENTECH.

“It’s important to be able to add other data to the data we get from the patient journey. I can say, ‘Look, there are [a higher number of people with asthma] In this town. But what I can also look for is pollution maps that show the types of pollution that exist nationwide. Is this environmental factor stressed and could affect the development of asthma? The ability to join other data sources in a meaningful and truly population-based way, rather than any individual patient, the real ability does seek those deeper insights, looking for correlations that might be causality, depending on the certain diseases you come up with are more common in certain populations in some places. ”

Regional legislation can also determine the types of screening available to states.

Steven Emrick, senior vice president of Purplelab Clinical Information Solutions and HealthNexus®, noted that biomarker testing for non-small cell lung cancer varies. 16 states require this biomarker test.

Purplelab actually studied this not long ago, and they looked at the patient population being tested for biomarker for non-small cell lung cancer. They found that the rate of lung cancer is very similar among white and African Americans per person per 100,000 people. But this is an ongoing problem in this country.

“For me, the real world Polaris is a way of using data to not only affect drug development, treatment development, diagnostic development, but also to impact these insights, bringing these insights back to regulators and policy makers to change health conditions.”

Patient Journey Mapping

One way to help you visualize your personal complex medical experience over time is through patient journey mapping. The goal is to better understand the disorders, support, interaction with services, and overall patient outcomes from the patient’s perspective. This approach helps determine the friction points and opportunities for improvement throughout the care.

Dividing the patient’s journey into different stages helps with a comprehensive patient mapping. In common frameworks are:

• Before diagnosis: Capture initial symptoms, self-assessment, research and initial attention
• Initial Contact: First direct interaction with the healthcare system (e.g., call center, in person)
• Diagnosis: The process of confirming the condition and its staging
• Treatment: Active management of the disease, including therapy and ongoing care
• Post-treatment/on-progressive care: follow-up, symptom management, lifestyle adjustments and long-term well-being

Utilize RWD Source:

Electronic Health Records (EHRS): These provide granular clinical details including diagnosis (e.g., ICD-10 code), procedures (e.g., CPT code), laboratory results (e.g., biomarker tests), prescriptions, and physician notes. Although EHR is rich in clinical depth, EHR may lack a comprehensive view of care provided outside of a specific health system and often contains unstructured text requiring advanced processing.

Administrative claim data: This data captures the payer's billing and reimbursement information, providing a portrait view of the patient's encounters in a variety of providers and settings. This is invaluable for understanding healthcare utilization, costs, and tracking patient mobility.

Patient registration: These systematically collect specific (usually granular) information about patients with a particular disease or patients receiving a particular treatment. The registry may include data elements that are typically found in the EHR or claim, such as detailed biomarker assessment, behavioral factors (e.g., smoking status), and patient-reported results (PROS).

Social Media Data: Public posts and discussions on lung cancer-specific forums and platforms that provide unfiltered realistic insights into patient symptoms, side effects, treatment challenges, and emotional impact. This provides a raw, real-time understanding of patient emotions and priorities.

Patient-reported results: These are direct reports from patients, understanding their health status, symptoms, functional status and quality of life, captured without a clinician's explanation. Advantages are crucial to understanding what really matters to the patient, such as relief of symptoms, quality of life, and treatment satisfaction.

Analytical technology: Qualitative Data Analysis (QDA) is used on social media posts and interview records to identify topics and patterns. For larger data sets from EHR and claims, advanced analytics including AI and machine learning are used to identify trends and predict patient behavior. Retrospective and prospective observational studies are common designs for analyzing RWD.

The utilization of digitization of patient data is tremendously transforming drug development. It is advancing a more personalized approach to health care and helping clinicians meet patients. This is the only way to improve recruitment and participation in clinical research and will help us develop more effective drugs that will not only improve patient health for a wide range of patient populations, but also improve their quality of life. Reducing therapeutic side effects will also improve drug compliance and lead to a healthier, more robust healthcare industry.