Managing the Explosion of Healthcare Data to Harness its Power healthcare bigdata data AI health patient
What is big data in healthcare?The role of artificial intelligence in big data managementThese advances have percolated into everything from digital communication with patients to software modeling of test results and, most importantly, in this context, collection and storage of data.
More recently, big data has introduced information from modern healthcare tools such as telemonitoring, wearable medical devices, and healthcare apps. As technology in telehealth and wearable devices continues to evolve, their contribution to big data will only increase. Cost can also be reduced, and efficiency increased on a smaller scale at the individual level, with clinicians using data to make decisions rather than simply training and professional opinion. Quality of care is increased using tools like predictive analytics to detect disease at early stages and resources of demographics, lab tests, and diagnoses to help inform clinician treatment.
Related StoriesAI was first used in medicine in the 1950s, in which algorithms aided by a computer were employed to assist in clinician-led diagnoses. Since then, AI-based approaches have promised the execution of tasks that would normally require human assistance and judgment. This incorporation would allow medical professionals to focus their skills on creative problem-solving and streamline the healthcare system.
Predictive medicine, in which disease diagnosis and outcomes can be assessed through risk factors, assessing multimorbidity, and patient data diagnostics, facilitates overall clinical decision-making. This accelerates the quality and efficiency of care provided, decreasing overall costs and improving outcomes for individual patients.Challenges in future developments While AI and machine learning-based management of Big Data in healthcare is promising, it is not perfected.
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