Can Data Analysis Revolutionize Patient Care.

19 May 2023, 21:14
Can Data Analysis Revolutionize Patient Care? šŸ„šŸ§‘ā€āš•ļø In the realm of healthcare, a crucial challenge persists: the lack of comprehensive and evidence-based care guidelines for clinicians managing their patients. Shockingly, only around 20% of patients are connected to standard care guidelines, and a mere 4% of patient care situations are backed by randomized, controlled clinical trials. As a result, medical professionals often find themselves making treatment decisions based on intuition and experience alone, especially when faced with patients having multiple conditions. To tackle this pressing issue, the concept of digital consultations supported by advanced technology has emerged as a potential solution. Pioneering institutions like Stanford University School of Medicine have introduced services like the Green Button Informatics Consult Service. These services leverage routinely collected, de-identified data from millions of individuals to provide on-demand evidence in situations where adequate evidence is lacking. Equipped with access to longitudinal patient records, these consultations staffed by physicians and data scientists yield evidence-based insights within 24 hours, empowering attending physicians to make informed decisions and provide personalized patient care. The transition from paper charts to electronic health records has played a pivotal role in making patient data more accessible. šŸ—‚ļøšŸ¤– In the realm of big data, DeHealth stands as a revolutionary force. Through its cutting-edge approach to data collection, analysis, and sharing, DeHealth is transforming medical research and care delivery. By harnessing blockchain technology to ensure data security and privacy, DeHealth empowers patients to share their medical data for research while retaining control over its usage. Our advancements not only improve the effectiveness of care but also facilitate the transformation of data into actionable insights through data analytics and machine learning.