Clinical Trial

Personalized medicine is all set to transform the future of healthcare. With the advent of modern-day technology and tools, clinicians can now offer personalized-care and cater to the specific needs of their patients. To support physicians in this aspect, clinical researchers across the globe are tapping into the potential of real-world data. For instance, in rare diseases and cancer care, conventional clinical trials are unlikely to provide large scale aggregate data for complex analysis. This lacuna can be overcome by examining real-world data which can provide us with a direction to test a particular drug for particular patient group. Having said that, using the available real-world data to offer tailor made therapies for patients is not as easy as it sounds.

The accuracy of the data obtained has to be validated before it can be used to improve patient outcomes. The future of clinical trials is going to be data driven with increased reliability on real world data gathered from EHR and other self-reported patient data sources. Real-world evidence can predict the comparative effectiveness of medications, and this can be very helpful in offering personalized healthcare.

The goal of clinical researchers is to reach a stage where adequate real-world data is available to categorize and compare different patient subgroups. Such data sets can add much value to clinical research and revolutionize personalized medicine.

Areas of Implementing Personalized Care using RWD:

Cancer treatments:  By analyzing tumor genetics and patient characteristics, oncologists can identify targeted therapies that are most likely to be effective for a particular patient. Real-world data is used to monitor patient outcomes and identify potential side effects of treatments, allowing for adjustments to be made to optimize patient care.

Management of chronic diseases:  By analyzing data from a large patient population, researchers can identify factors that contribute to disease progression and develop personalized treatment plans that are tailored to each patient’s specific needs.

Associated Challenges:

One of the challenges for the scalability of personalized medicine is the lack of granular level health data. This gap can be bridged by the large volumes of real-world data available due to modern-day digital health advancements like EHRMs.

Other challenges are as follows,

  • Data standardization: Real-world data comes from diverse sources and may use different formats and vocabularies Data, which can make it difficult to compare and combine data across different studies.
  • Interpretation: Real-world data may be subject to interpretation bias, where researchers may interpret the data differently based on their own biases or assumptions.
  • Validation: Real-world data often lacks the rigorous validation processes that are used in clinical trials, which can make it difficult to assess the quality and reliability of the data.
  • Integration with electronic health records: Electronic health records (EHRs) are a key source of real-world data, but integrating this data into research can be challenging due to issues with data standardization, interoperability, and privacy.

With best practices and adequate protocols, real-world data can largely influence the standard of care in personalized medicine.  

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