Digitization in healthcare is gaining momentum through the use of data aggregation, advanced data analytics, and increased access to big data. Life sciences organizations are digitally transforming their processes by using these tools to accelerate the development of new drugs, devices, and digital medicine solutions.
They are also faced with numerous challenges, including navigating healthcare reforms, delivering innovation and value, complying with regulatory norms, optimizing supply chains, and operating within the complex healthcare ecosystem.
In order to increase the efficiency of drug development programs, these challenges require organizations to rebuild the integration layer into a new data curation and enrichment layer. And, within the scope of the 21st Century Cures Act, these organizations can accelerate medical product development and innovation, and focus more on real-world data and real-world evidence to support decision making.
Real world data (RWD) refers to observational data derived from electronic health records (EHRs), claims and billing activities, and other sources. Real-world evidence (RWE) is the evidence obtained from RWD, which gives important data regarding the usage, the potential benefits, and the risks of a drug.
In order to properly optimize and utilize RWD, the industry must have access to data that is consistent, accessible, usable, reliable, and secure. Curation and enrichment programs create these data qualities for organizations that have massive volumes of heterogeneous and dynamic data sets.
How does RWD improve the healthcare ecosystem?
From drug development to delivering value-based care, there is a huge opportunity for RWD to improve patient outcomes and the healthcare ecosystem as a whole. Knowing how a medicinal product is used by patients can help stakeholders across the continuum of care to make important real-time decisions.
RWD can reduce inefficiencies and fill in care gaps by sharing information throughout the ecosystem of payers, providers, patients, manufacturers, and regulators. This sharing of data from real-world scenarios enables all stakeholders to derive actionable insights and deliver better health outcomes.
Leveraging data to improve outcomes
The efficacy of randomized controlled trials is being increasingly challenged by healthcare providers and payers. These parties are asking for real-life data to validate the safety and efficacy of their drugs.
As the drug development process takes years and relies on expensive and inefficient clinical trials processes, it fails to provide a full picture of patient health. Life sciences companies can improve this process by optimizing the use of RWD and RWE to better understand populations and social determinants of health that affect patient behaviors and outcomes.
RWE is used by different stakeholders in various ways:
From RWD to RWE: The role of Data Curation and Enrichment
With an increase in use of computers, mobiles, and wearables, RWD and RWE are growing in both volume and depth. They are gaining utility as advanced analytics capabilities like artificial intelligence (AI) and machine learning enable more personalized insights. RWD can also be used with AI to optimize clinical trial design and improve patient recruitment.
Before creating RWE through insights from RWD, the data should be assessed for completeness, consistency, and accuracy. It should be assessed on whether it contains all the critical elements needed to evaluate a medical product and its claims.
The process of integrating, normalizing, standardizing, enriching, and then visualizing data facilitates data liquidity and dexterity. The transition of RWD to RWE happens in the following ways:
Data Curation: This process usually involves reducing cases and measures that don’t meet a quality standard, such as the removal of cases or observations for missing values or missing inclusion criteria. Data curation includes various activities like data integration, selection, classification, transformation, validation, and preservation.
Data Enrichment: Data enrichment is the process of enhancing existing information by adding missing or incomplete data. Its primary goals are associating, enhancing, and refining the quality and utility of raw data.
With large amounts of heterogeneous data at its disposal, the priority for the life sciences industry then becomes managing the quality of data that is being used. The emphasis is on data cleansing, unification, fixing and enabling quality checks to make sure that the aggregated data is standardized to avoid errors while interpreting the information.
Innovaccer's FHIR-enabled Data Activation Platform helps life sciences organizations activate and compress data into a single longitudinal record. It links and harmonizes de-identified data from structured and unstructured sources, integrates it, normalizes it, and maps it to a common data model to produce a unified patient record.
The Data Activation Platform curates and enriches data in the following ways:
The Road Ahead
In this emerging, data-driven environment, federal agencies have emphasized the use of RWD to modernize clinical trials and allow researchers to better understand and act on their implications. By integrating health data from disparate sources, addressing the lack of high-quality data, and monitoring longitudinal datasets, the stakeholders across the healthcare ecosystem will be able to use robust and standardized data sets to better navigate the complexities of healthcare delivery.
To get more insights into the scope of data enrichment amidst the rising RWD dependency, get a demo.
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