The Importance of Real World Evidence at Present

Why Real World Evidence Matters Now

Randomized controlled trials remain a cornerstone for evaluating medical treatments. They are essential for demonstrating efficacy, safety and regulatory value under controlled conditions.

But clinical trials cannot always answer every question that matters after a treatment reaches real patients in real clinical settings.

Important real-world questions include:

• What happens when patients manage treatment at home?
• What happens when adherence changes over time?
• Which patients are more likely to discontinue therapy?
• Which side effects create confusion, anxiety or early drop-off?
• Which interventions actually help patients stay on treatment?
• How do treatments perform across different populations, behaviors, workflows and healthcare system constraints?

This is where Real World Evidence becomes increasingly important. Real World Evidence does not replace clinical trials. It complements them by helping healthcare stakeholders understand how treatments, pathways and support models perform in everyday clinical practice.

 From Real World Data to Real World Evidence

Real World Data refers to health-related data that is routinely collected outside traditional randomized clinical trials.

This can include electronic health records, claims data, registries, patient-reported outcomes, digital health tools, remote monitoring devices, treatment utilization data and patient engagement data.

Real World Evidence is generated when these data are analyzed in a structured and methodologically sound way to support clinical, operational, regulatory or market-access decisions.

For pharma and healthcare organizations, the question is no longer whether data exists. The question is whether the right data is collected, structured, interpreted and used effectively.

The Missing Layer: What Happens Between Visits

Many real-world challenges are not visible during the clinic visit. They happen between visits, when the patient is at home.

This is where treatment success is often shaped:

• Does the patient understand the treatment?
• Does the patient start treatment correctly?
• Does the patient continue over time?
• Does the patient know how to manage expected side effects?
• Does the patient report symptoms early enough?
• Does the patient receive the right intervention at the right moment?
• Does the care team know which patient needs attention?

Traditional healthcare systems often have limited visibility into these moments. Health Cloud Connect is designed to close this gap.

Health Cloud Connect as a Real World Data Layer

B-online Health Cloud Connect enables healthcare organizations and pharma companies to orchestrate digital patient journeys before, during and between care events.

The system can support patients through digital pathways that include:

• Treatment onboarding
• Personalized education
• Medication and appointment reminders
• Symptom and side-effect check-ins
• Patient-reported outcomes
• Adherence and persistence signals
• Remote monitoring where relevant
• Pharmacist or nurse follow-up
• Escalation rules for clinical teams
• Automated communication via WhatsApp, SMS, email, portals or AI-supported assistants
• Dashboards and program analytics

Every interaction can become a structured data point. Over time, these data points create a longitudinal picture of the patient journey in the real world.

This is where Health Cloud Connect moves beyond engagement. It becomes a practical infrastructure for Real World Data collection.

What Type of RWD Can the System Generate?

A patient journey platform can generate several categories of real-world data that are highly valuable for healthcare and pharma decision-making.

1. Engagement Data

This includes whether patients open messages, watch educational videos, complete forms, respond to check-ins and continue through the digital pathway.

Engagement data can help identify whether the support model is reaching patients and where drop-off occurs.

2. Adherence and Persistence Indicators

The system can identify signals such as missed confirmations, delayed responses, treatment interruption risk, incomplete onboarding, missed follow-up steps or repeated need for support.

These signals do not replace clinical adherence measurement, but they can help identify patients who may be at risk.

3. Patient-Reported Outcomes and Experience

Patients can report symptoms, quality-of-life indicators, confidence, treatment burden, satisfaction, concerns, side effects and barriers to continuation.

This type of information is often missing from traditional operational data.

4. Side-Effect and Symptom Navigation

Structured check-ins can help understand which symptoms are commonly reported, when they occur, how patients respond and which issues require escalation.

In pharma-related programs, this must be connected to clear pharmacovigilance processes.

5. Operational Care Data

The system can measure how many patients require escalation, which type of cases reach the care team, response times, task volume, pathway completion and workload distribution.

This helps providers and pharma teams evaluate not only patient outcomes, but also program efficiency.

6. Intervention Data

Health Cloud Connect can document what intervention was delivered to the patient and when.

For example:

• Educational content
• Reminder sequence
• Nurse call
• Pharmacist consultation
• Escalation to HCP
• Digital assistant response
• Procedure preparation message
• Follow-up after missed interaction

This creates the foundation for learning which interventions may be associated with better engagement, adherence or persistence.

Turning Data into Recommendations

The value of RWE is not only in reporting what happened. The greater value is in helping teams decide what to do next.

Health Cloud Connect can support decision-making at three levels:

Patient Level

The system can guide the patient with personalized next steps:

• Complete onboarding
• Watch a relevant educational video
• Answer a symptom check-in
• Prepare for a procedure
• Contact a pharmacist
• Speak with the clinic
• Continue follow-up according to the pathway

Care-Team Level

The system can help nurses, pharmacists, coordinators and HCPs prioritize patients:

• Patients at risk of discontinuation
• Patients reporting concerning symptoms
• Patients who did not complete preparation
• Patients who missed key pathway steps
• Patients who need human follow-up
• Patients who can continue with automated support

This helps reduce low-value manual work and focus professional attention where it matters most.

Program Level

For pharma, providers and health systems, aggregated data can support better program decisions:

• Which pathway steps improve completion
• Where patients drop out
• Which patient segments need more support
• Which messages or channels perform better
• Where escalation rules need adjustment
• What content should be improved
• Where the program creates operational burden

This is the bridge between patient engagement and Real World Evidence.

Why Longitudinal Data Matters

One of the most important advantages of Health Cloud Connect is that it follows patients over time. A single interaction has limited value, but a longitudinal journey can show patterns.

These patterns may include:

• Early treatment confusion
• Declining engagement
• Recurring symptoms
• Missed reminders
• Response to education
• Increased confidence after pharmacist support
• Reduced need for escalation after better onboarding
• Changes in reported quality of life or treatment burden

This continuous view can help organizations understand not only whether a program was used, but how it influenced the patient journey.

In chronic care, obesity, diabetes, respiratory disease, oncology support, dermatology, biologic treatments and long-term therapies, this longitudinal layer is especially important.

RWE Is Not Just More Data — It Is Better Data Design

The real challenge in RWE is not collecting as much data as possible. The challenge is collecting the right data with the right structure, governance and purpose.

For Health Cloud Connect, this means each program should define:

• The clinical or operational question
• The relevant patient population
• The pathway design
• The data points to be collected
• The patient consent model
• Privacy and security requirements
• Pharmacovigilance workflows where relevant
• Data quality rules
• Escalation logic
• Reporting dashboards
• Analytic methods
• Limitations and potential bias

This is essential because Real World Evidence must be credible, explainable and fit for purpose.

Practical Use Cases

Obesity and GLP-1 Treatment Support

Health Cloud Connect can help follow patients during treatment initiation, titration, lifestyle adaptation and persistence challenges.

Relevant RWD may include onboarding completion, reported side effects, missed interactions, patient confidence, persistence signals and need for pharmacist or nurse support.

Diabetes Care

The system can support reminders, education, glucose-monitoring prompts, lifestyle support, PROs and escalation for high-risk signals.

Relevant RWD may include pathway engagement, self-reported barriers, completion of monitoring tasks and response to interventions.

Procedure Preparation

Before a procedure, the system can send videos, instructions, reminders and readiness checks.

Relevant RWD may include video completion, readiness confirmation, missing documents, patient questions, cancellations, no-shows and escalation needs.

Pharmacist Connect

The system can manage structured digital communication between patients and pharmacists.

Relevant RWD may include medication understanding, adherence barriers, side-effect questions, consultation outcomes, follow-up needs and escalation to physicians.

PSP Programs

For pharma-sponsored or client-owned patient support programs, Health Cloud Connect can structure the entire digital journey while producing aggregated insights on engagement, adherence risk, persistence and support needs.

The Role of AI

Artificial intelligence can help convert real-world data into actionable recommendations.

In Health Cloud Connect, AI-supported tools can help:

• Identify patients at risk of disengagement
• Recommend the next best action
• Summarize patient interactions
• Support triage workflows
• Identify recurring barriers
• Generate program-level insights
• Help care teams prioritize follow-up

However, AI must be used responsibly. In healthcare, AI should support professional decision-making, not replace it.

Models should be monitored for bias, explainability, safety and alignment with approved clinical and operational protocols.

Implications for Pharma and Healthcare Organizations

For pharma, Real World Evidence can help understand how treatments are used outside clinical trials, where patients struggle, and what type of support may improve adherence and persistence.

For providers, RWE can help improve care pathways, reduce unnecessary manual workload and identify patients who need intervention earlier.

For patients, better use of real-world data can translate into more personalized, timely and practical support.

For the healthcare system, the opportunity is to turn fragmented patient interactions into a continuous learning infrastructure.

Conclusion

Real World Evidence is becoming one of the most important layers in modern healthcare decision-making.

But RWE does not start with dashboards. It starts with the patient journey.

Health Cloud Connect enables organizations to support patients over time while collecting structured, meaningful real-world data from the treatment journey itself.

By connecting digital pathways, patient-reported outcomes, adherence signals, professional follow-up, escalation workflows and analytics, the system can help turn everyday patient engagement into evidence that supports better decisions.

The future of healthcare will not be driven only by more data. It will be driven by the ability to collect the right data, understand it in context, and act on it at the right time.

That is the opportunity for Health Cloud Connect: to transform patient engagement between visits into real-world evidence for better care, better decisions and better outcomes.