Real-World Data: Bringing Wound Care Innovation to Life

Over the last few years, excitement has been building about the benefits of using real-world evidence (RWE), obtained from real-world data (RWD), to demonstrate the efficacy and safety of drugs and devices. Pharmaceutical companies, device manufacturers and, most importantly, the Food and Drug Administration (FDA) are enthusiastic about the benefits of research based on RWD.

“The use of computers, mobile devices, wearables and other biosensors to gather and store huge amounts of health-related data has been rapidly accelerating,” explains the FDA. “This data holds potential to allow us to better design and conduct clinical trials and studies in the health care setting to answer questions previously thought infeasible. In addition, with the development of sophisticated, new analytical capabilities, we are better able to analyze these data and apply the results of our analyses to medical product development and approval.”

RCT versus RWD studies

In the past, scientific studies were not considered credible unless they were randomized control trials (RCTs). In a randomized controlled trial, study subjects are placed by chance into either a group that receives a particular intervention or a control group. The “control” can be the use of a standard practice, a placebo, or no intervention at all. The results from this group are compared to the treated group to determine if positive results occur.

While RCTs reduce bias and give researchers a rigorous tool to examine cause-effect relationships between an intervention and outcome, they also present researchers with ethical and applicability issues. Furthermore, RCTs are costly, time-consuming and can be logistically complicated.

In contrast to prospectively managed and highly selective RCTs, RWD studies mine massive datasets from electronic health records (EHRs) and other sources for information about how products have been used and the results. These broad, retrospective studies reflect actual clinical situations, and they can be done for a fraction of the time and cost.

With big data analysis and artificial intelligence (AI), we now have the ability to easily search large databases such as electronic health records and stored data sets for information about how the patient used the device, medication, adaptation, or treatment plan in real life. It creates new data and analysis that can be applied to future treatments.

The Evolution of Data in Wound Care Research

Wound care patients have historically been underrepresented in clinical randomized control trials. Wound care patients are more likely to be elderly and have comorbid diagnoses, making it difficult to determine causation or to control as variables. This also makes it more difficult to identify them as potential clinical trial participants and to keep them engaged in long-term studies.

Additionally, existing wound care research studies, when confined to RCTs, do not fully account for the complexity or the variables involved in treatment and outcomes in wound care. Accessing real world data, using it to further understand research conclusions, and being able to adapt recommendations and treatments to the way real patients react and follow through will make wound care treatments more effective in the future.

In July 2024 the FDA released its suggested guidelines for using real world data sets, indicating the widespread acceptance of this kind of data collection and analysis. Part of the 21^st Century Cares Act passed in 2016 allowed the FDA to use real world data to improve innovation and research additional indications for already approved medications.

Electronic Health Records: A Data Gold Mine

The adoption of modern electronic health records systems has transformed researchers’ access to patient data and given medical experts the ability to follow patients across longer time periods for more comprehensive information. Data of all kinds found in EHRs can be valuable for wound care research, especially:

• Patient demographics
• Progress notes
• Medications
• Vital signs across time
• Immunizations
• Radiology reports
• Lab work
• Imaging
• Surgeries and procedures
• Complications of past illnesses and side effects of medications

EHRs that are specific to wound care workflows can be even more helpful. These systems typically include images of wounds, measurements, detailed descriptions of wounds and discharge, and methods to track healing. Electronic health records have standardized the way physicians and wound care nurses document wounds. Structured data from these systems enables the use of RWE by including all relevant information in a format that can be analyzed by artificial intelligence or machine learning systems.

Large Database Sets Available for Real World Data Collection

One of the primary sources of real-world data for research and health care development are electronic health records. As of 2021, nearly 90% of office-based physicians use EHRs and 78% use a certified HER. In 2025, 96% of hospitals in the United States have fully adopted EHRs and 92% of acute long-term care facilities have done the same. These high levels of adoption mean that the vast majority of patients, including wound care patients, are being tracked by an EHR system.

In addition to EHRs, other databases store real world data that can inform and influence research.

• Disease registries: There are some national registries for specific diseases such as the  Swedish National Quality Registry for Ulcer Treatment (RiksSår).
• Medicare and Medicaid data
• Administrative Claims data: Insurance companies, as well as Medicare and Medicaid, track claims and insurance payments for specific treatments and procedures.
• Wound Imaging: EHRs and other technologies can use images of wounds for comparison and data collection across a large user base.
• Qualified Clinical Data Registries (QCDR): These include systems like the US Wound and Podiatrist Registry that use large data sets to understand the risk of complications, healing progressions, and the cost-effectiveness of various treatments.
• The FDA Sentinel Database: This database collects information about the safety of FDA-approved vaccines, medical devices, medications, and biologics.
• The Electronic Health Records for Clinical Research Project: This European database aims to reduce the time it takes to recruit participants in clinical trials by giving researchers access to EHRs to find suitable candidates.

The Wound Care Collaborative Community (WCCC) works to determine which databases are best for collecting real world data about wounds. While work toward a comprehensive database that allows appropriate access to structured data sets that researchers can use is still in its infancy, the WCCC’s goal is to develop a better structure for randomized control trials that will include the scope and complexity of wound care.

One recent example of the use of EHRs and real world data sets to understand the trends and cost of wound care treatment was a five year retrospective analysis of Medicare data about patients who developed foot ulcers, surgical wound infections, and other traumatic or chronic wounds. Collecting this data allowed researchers to track the prevalence of various types of ulcers, the cost of treatment, which patients are at highest risk, and what treatments were most effective.

Wearables Add Depth to Current Data

Wound healing is very complex and varies greatly depending on the individual. Because it’s difficult to predict healing patterns, particularly for chronic or recurring wounds, wearable technology is playing a greater role in monitoring changes in wounds, making predictions about the best treatments, and giving patients the self-efficacy to monitor their progress. New wound biomarker monitoring can sense and send information about the wound’s temperature, moisture, pH balance, and more. Not only has this information not been available to physicians in the past, but it has also not been available to researchers trying to understand how wounds heal and what factors impede progress.

Mobile technology can monitor a wide variety of health markers including:

• Heart rate
• Sleep patterns
• Exercise
• Blood pressure
• Weight
• Glucose levels
• Blood oxygen saturation
• Stress levels
• Body temperature
• Menstrual cycle
• Fall detection
• Heartbeat irregularities
• Seizures

Wearable technology has rapidly advanced past simple smart watches that act as activities trackers into devices that can monitor any area of health. Wearables provide more data than can be collected by a single test or observation period and improve compliance with treatment plans with automated notifications or medication delivery.

Using data from wearables allows researchers to combine clinical data with continuous monitoring of additional factors such as sleep or blood pressure that may alter outcomes. They also offer a peek into how patients integrate changes into their lives and pinpoint where they experience stress, a lack of support, or poor access to resources.

Other mobile technologies, such as wound assessment apps, support patients at home between appointments with professionals. They can use the data collected via these apps to track normal progression of disease and identify signs of healing or infection. Because patients have access to these at any time, the data collected here can be more comprehensive and detailed than photos and measurements taken at occasional health care appointments.

Ethical Considerations for Using Real World Data

As with all medical research, HIPAA guidelines must be adhered to and patient privacy protected. If EHRs or databases with information gathered from wearable devices are used, all identifying information should be scrubbed and data used in aggregate. Linking together various databases further increases the risk of compromising privacy.

Some suggestions to maintain data privacy when using real world data include:

• Establishing processes for collecting and storing data.
• Ensuring all participants understand how the data is being used and consent to participating.
• De-identifying data by removing any link between a particular person and their data.
• Registering all studies in a clinical trial registry.

Current Applications for and Studies Using Real World Data

Real world data has already revolutionized the way researchers approach clinical studies by giving them access to more data than ever before. Large data sets have improved studies, changed processes, and developed more cost-effective treatment plans for a variety of health concerns, including wound care.

More eligible patients can be found to participate in clinical trials using RWD. This leads to better representation of groups who were previously excluded from clinical trials because they were hard to reach or were unlikely to volunteer. RWD supports better trial design by starting with more data on a larger and more diverse population.

Changes to pharmaceuticals are happening fast now that there is additional access to RWD. Companies get immediate, actionable insights into prescribing patterns, patient behaviors, and barriers to access. Databases can provide these insights constantly rather than waiting for RCTs to be published.

Tracking vaccines and drugs over a long period of time is essential to monitor their effectiveness and safety. RWD provides information on medications post-launch and can result in additional indications or warnings. For example, the warning about the Johnson & Johnson COVID-19 vaccine increasing blood clots in young women was established via real world evidence after the product was used.

Emerging Technologies and Future Possibilities

The ability to access and use real world data along with the rapid development of artificial intelligence and machine learning means the applications of this data are increasing constantly. The fastest (and fastest growing) way to analyze the large data sets RWD produces is through machine learning. This new technology enables researchers to extract more insights and identify additional patterns that would otherwise take likely years of number crunching.

Natural language processing (NLP) takes unstructured data like clinical notes, patient descriptions, and discharge summaries and transform them into data sets that can be used for research purposes.

As the ability to use RWD improves, clinicians will benefit by getting additional information about safety of medications, have additional data points for making decisions, be able to monitor compliance, and reduce costs. Databases for specialties such as wound care will improve outcomes for patients when researchers and clinicians have more comprehensive information about the way treatments are delivered, plans are followed, and other diagnoses affect medications. Wound care EHRs collect and analyze data on patients of all demographics, locations, stages of treatment, and severity. This information will transform the way wound care physicians, nurses, and facilities deliver care.