Three Ways to use Real-World Evidence to Improve Research and Outcomes

Key takeaways:

• Understand the importance of using real-world evidence to conduct clinical research, improve discovery, prove solution value and improve outcomes.
• Learn the components of the PINC AI^™ Healthcare Database (PHD).
• Understand the importance of partnership to improve healthcare.

Additional resources:

• Learn more about how you can use PINC AI™ Healthcare Data to conduct evidence-based analyses of drugs, devices, and disease states and to accelerate medical solution development.
• Listen to the Webinar

Real-world data (RWD) and real-world evidence (RWE) are growing and expected to become a $2.3 billion market by 2026, up from $1.2 billion in 2021. They’re increasingly being utilized to provide actionable insights that improve research and accelerate bringing new innovations to patients.

While exciting, it comes with the complexities of how to access and connect the RWD and RWE and turn them from information into ideas.

Below are three ways to effectively use RWD and RWE to conduct clinical studies, improve drug, device, and diagnostic discovery, and provide proof that a healthcare solution delivers real value by doing what it’s intended to do.

1. Combine RWD/RWE with clinical trial data to improve discovery.

RWE is based on RWD that comes from outside of a clinical trial, but when combined with clinical trial research, it can dramatically shorten the regulatory approval process and increase chances of U.S. Food and Drug Administration (FDA)or European Union Medical Device Regulation approvals.

Clinical trial data can help reduce confounding variables, control the information and allow for understanding of solution efficacy and safety during a finite time period in very specific patient populations.

However, there’s a lot more that can be derived if clinical trial data is combined with RWE, because RWE can illuminate information that a clinical trial cannot. For example, RWE can highlight adverse events or uncover a new patient population that could benefit from a product that wouldn’t have shown up during the trial because it happened in patients that were not studied due to the inclusion/exclusion (I/E) criteria.

In one real-life example, a health system that separates their research into three buckets – national network involvement, investigator-initiated projects and the clinical trial portfolio across various health conditions – uses RWE/RWD to drive their research questions. It helps them look at research from a different angle, drive new hypotheses, answer practical questions, and apply the results in real time.

Combining RWE and clinical trial data can help researchers uncover early risk of disease, allow health systems to take a more proactive treatment approach and use the data to drive provider behavior change to be more in alignment with the latest evidence and guidelines.

2. Utilize standardized data and break down data silos.

It’s important to use standardized, timely and connected data. Typically, data is contained in what can be referred to as ‘silos’ such as clinical trials, disease specific research, radiology, laboratory and pharmacy departments. This data is all outside of an electronic medical record (EMR), meaning it isn’t standardized and is hard to access or aggregate.

When data is connected and standardized it spurs healthcare innovation and helps life science companies, hospitals and health systems understand overall performance in comparison to competitors and industry benchmarks.

Whether the question being answered is about clinical efficacy, cost effectiveness, quality or efficiency, standardized data like that derived from the PHD can extract the right RWD/RWE to answer the questions at the right time.

What is the PINC AI^™ Healthcare Database?

The PINC AI^™ Healthcare Database (PHD) is a retrospective database filled with more than 20 years’ worth of charge master data from 1,100 facilities encompassing visits from 240 million unique patients.

The PHD includes information on hospital-based outpatient visits, ambulatory visits and pharmacy fills as well as different types of payments from cash and commercial payer to Medicare and Medicaid.

Additionally, the dataset targeted can combine data from the PHD with structured and unstructured data such as notes from an EMR using natural language processing (NLP). Tapping into these notes provides a full picture of the patient that historically could only be achieved through manual chart reviews.

This type of robust, geographically diverse data can help life science companies, hospitals and health systems conduct clinical, financial and outcome analyses on drugs, devices, treatments, disease states, epidemiology, resource utilization, healthcare economics and burden of illness determinations.

3. Engage in the right partnerships to improve data, research, quality and patient outcomes.

To get the most benefit out of RWD/RWE, finding the right partner is critical. Just this year, more than 80 percentof life sciences companies stated their intent to partner with data and data science companies for just that reason.

Answering any of the questions discussed above requires access to broad, standardized and clean datasets, research-ready testing networks, and artificial intelligence solutions to compress research timelines.

One such partner is the PINC AI^™ Innovation and Research Collaborative (PIRC), a network of 278+ leading research hospitals across 38 states that all leverage shared clinical technology and data to enable faster identification of eligible patients and rapid-cycle research.

This network utilizes technology tools such as NLP and machine learning to find patients who meet complex I/E criteria and mine unstructured, text-based notes to rapidly identify patients based on a host of demographic, socio-economic, social determinants and other indicators.

This type of partnership can help life sciences companies, hospitals and health systems illuminate trends in cost of care, trends in diseases, assess resource utilization, run smaller clinical trials and enhance clinical outcomes. In addition, this access to data can open the door for future collaboration opportunities leading to new patient information and countless new medical solutions.

As life sciences companies evolve, they will continue to seek ways to harness the power of RWD, to improve patient-centered clinical trials, uncover data-driven insights and expedite treatment development.

The use of RWD/RWE will only increase and prove to be critical in supporting smarter healthcare and driving efficiencies that improve patient outcomes.

It’s time to make PINC AI™ Healthcare Data (PHD) part of your research and outcome improvement strategy.

Learn how PINC AI^TM Applied Sciences and the PHD can help you connect data points, break down data silos, streamline research and make actionable insights accessible to clinicians across your organization.

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