About The Data
The dashboard below surfaces mock patient data such as a patient ID, gender, race, and birthday. It also contains a wide variety of ICD10 Diagnosis Descriptions and diagnosis dates for each patient. Lastly, drug names were randomly associated with the patients and diagnosis. This dashboard example demonstrates how Sigma can process incredibly large datasets while simultaneously providing flexibility for deeper, ad hoc analysis.
Who Is This Dashboard For
This dashboard is intended primarily for individuals in the medical field who want to identify individuals that meet a certain criteria. This list could help healthcare providers inform treatment decisions and improve medical care. Medical researchers use cohort analysis to study the underlying causes of diseases and conditions and to develop new treatments and therapies.
Additionally, government agencies, such as public health agencies, may be interested in cohort analysis to understand the prevalence of certain diseases or conditions within a population. Cohort analysis can be used to develop strategies for disease prevention and control. Finally, pharmaceutical companies may use cohort analysis to study the effectiveness of different treatments and to inform the development of new drugs.
Healthcare & life science data poses unique challenges for the data analyst. It is one of the most complex and highly regulated types of data but can provide powerful insights if managed well. Analyzing medical data is also one of the most meaningful ways technology can help improve people’s day to day lives. This dashboard demonstrates how Sigma and Snowflake rise to the challenge and unlock the insights in healthcare and life science data.
If you’re interested in working with the right team and tools, reach out to phData today!
We are Sigma.
Sigma is a cloud-native analytics platform that uses a familiar spreadsheet interface to give business users instant access to explore and get insights from their cloud data warehouse. It requires no code or special training to explore billions of rows, augment with new data, or perform “what if” analysis on all data in real-time.
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