- Understand the different types of healthcare-related data that are used for research and to direct patient care.
- Learn how the 21st Century Cures Act has changed how observational research is used.
- Explore innovative solutions for how data is being used by payers, regulators and clinicians, as well as understand the limitations, challenges and gaps in utilizing this data.
The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) defines real-world data (RWD) as “data used for decision-making that are not collected in conventional randomized clinical trials (RCTs).”
There are other definitions, but this one is the most quoted and generally accepted.
In a recent PINC AI™ webinar, Jim Sianis, Senior Director of Real-World Evidence for Premier’s PINC AI™ Applied Sciences Division, speaks on the types of health data used for research and direct patient care. Jim also focuses on the strengths, limitations and weaknesses since COVID-19 and how they play a major role in the way regulatory bodies are using this data. This overview provides the essentials to help life sciences companies and health systems understand the best database to utilize for future needs.
Real-World Data Used for Life Sciences
The primary focus of this webinar is how RWD can be used for life sciences and health systems research. Applied research is a way to seek answers to various questions and find solutions. It is basic, but essential. A good example would be comparing one product to another and doing basic outcomes research. The research can be used to support health outcomes research, market analysis or to track new products post launch. Applied research has also been used for protocol modeling.
21st Century Cures Act and Observational Research
In 2016, the 21st Century Cures Act was signed into law and is designed to help accelerate drug approvals by the U.S. Food and Drug Administration (FDA). In addition, it will assist the FDA in with cancer research, fund the fight against opioid abuse and fund mental health treatment. Not to mention, there are instructions for each state to improve care when it comes to mental health.
The 21st Century Cares Act puts a focus on how to use RWD to generate real-world evidence (RWE) to support submissions seeking regulatory approval.
Obtaining De-identified Data
There are two methods of de-identifying data: one is through safe harbor and the other is through the completion of a statistically de-identified process using a biostatistician.
1. Safe Harbor
Safe harbor is Health Insurance Portability and Accountability Act (HIPAA) protected. HIPAA restricts access to individuals’ private medical information. Safe harbor is said to be the easier route in which identifiers and service data are removed, thus creating de-identified data.
2. Statistically De-identified Process
This complex statistical process, performed by a biostatistician, de-identifies the data and retains HIPAA-compliant patient data elements without the ability to identify specific patients. This process maintains HIPAA protections.
Strengths and Weaknesses
Clinical databases are an important part of running healthcare, generating RWE and providing comparative information from actual practice, but they come with their weaknesses. These databases capture massive amounts of data points that must be aggregated, standardized and turned into actionable insights that can be used to inform clinical decision making – all while maintaining patient privacy.
This is a tough task considering the introduction of new data sources such as smartphones, surveys and wearables controlled by the patient. Given these new sources, the PINC AI™ Applied Sciences (PAS) team and the PINC AI™ Healthcare Database work in tandem to ensure quality, compliant datasets are available that can enhance real-world evidence generation. This patient-generated data is collected in real time and can be connected to a person’s healthcare record to provide insights that cannot be gleaned from other sources.
Success for the Future
The main thing to remember is there is no such thing as a “perfect” data set, but there doesn’t have to be. It is imperative to know the goals that you want to accomplish, which will help decide which data source would have the best fit for purpose. Jim mentions that sitting down and outlining the outcomes of interest and deciding what is important will go a long way. The decision making will become easier and more efficient. Remember, don’t let the perfect be the enemy of the good.
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