Data Analytics: Redefining Healthcare Management
Big data is now slowly revolutionizing global healthcare management. Pharma companies have been gathering years of clinical research data into medical databases, while providers and payers have been digitalizing their patient records. Simultaneously, recent technology advancements have made it easier to analyze and collect information from multiple sources including hospitals, payers, laboratories, and physician offices.
“Real-World Evidence” is the concept of pharmaceutical companies deriving incremental value from newer healthcare offerings as opposed to the already existing ones. Proven incremental benefits of approved products provide pharma companies an advantage to increase their prices.
Data Analytics for Significant Savings
Looking forward to 2018, the trends observed over the last three years suggest a progressive consideration of data analytics toward transforming healthcare. This can be attributed to structural development and the adoption of data analytics tools by healthcare firms.
Globally, healthcare industry stakeholders are aware of their most important priorities, including the need for faster, more efficient clinical research outcomes and transformation in the concept of personalized medicine. This requires more precise healthcare management. The healthcare stakeholders (payers, providers, regulatory bodies and pharma companies) believe that these priorities can only be achieved by leveraging the huge repositories of patient and clinical data.
According to McKinsey, application of big data strategies for more informed healthcare decision making can generate savings of close to USD 100 billion across the US healthcare system annually. This is possible by improving the efficiency of clinical trials, optimizing innovation, and building new tools for consumers, physicians, payers, and regulators.
Cloud is an Imperative
With the scope of clinical research extending, it is necessary to have a system in place to effectively manage continuously piling clinical data. There is a need for more flexibility and efficiency in clinical process development, specifically when it comes to the accessibility of data and faster study outcomes.
In the pharmaceutical industry, the clinical research process is one of the biggest factors aiding data growth. Effective utilization of this data helps pharmaceutical companies quickly identify potential new drug candidates and develop them into approved, reimbursed and more effective medicines.
In a scenario where the clinical system shifts to a continuous flow process, the laboratory analyzers produce data simultaneously which must be processed at the same time. This makes the existence of cloud-based data management tools more crucial.
Looking Beyond Clinical Trial Data
Pharmaceutical companies have now discovered the importance of real-world evidence over the routine study outcomes of clinical trials. A few years ago, that was good enough to get the FDA approval for drug commercialization. But now, pharma companies need to convince the regulatory bodies that their drug products have an incremental value over the already existing practice of healthcare.
With real-world evidence, pharma companies can get a higher tier of pricing. Leveraging the data analytics platform can help pharma companies perform the clinical trial faster with improved outcomes. For implementing this within the organization, pharma companies generally hunt for partners providing data analytics platforms.
However, pharma companies are liable to have a value proposition toward implementation of this system. Payers and regulatory bodies should be convinced that the system implemented is of legitimate value. The data analytics providers must also have a user-friendly database platform in place which can be easily understood and leveraged by the pharma company for their own study purpose. The key issue faced here is that of the data privacy and limited data access.
Data analytics is steering global healthcare management toward a brighter future. However, it is extremely important to ensure that the data is generated from authentic and reliable sources and it is updated on a regular basis. This is expected to drive the adoption and implementation of precision medicine overcoming the traditional concept of “one size fits all.”