Electronic Health records or EHR’s are a significant source of data of patients. However, analyzing the huge volumes of medical data at scale to derive actionable insights is a complex task. Eugenie’s machine learning equipped solution can scrutinize large EHR data to identify any potential health concerns for patients. Based on the conclusions, Eugenie can also suggest optimum corrective action.
Eugenie’s AI-backed algorithms can scrutinize a huge volume of patient data to establish similarity with past events to detect any medical discrepancies. Machine Learning based models can analyze data from previous diagnosis, drug history, lifestyle, genetic profiles, etc. to generate alerts, which can help practitioners in decision making.
Eugenie’s data layer can connect to any wearable device which can collect health data of varied users. This data can be processed by Eugenie’s solution to get personalized recommendations and alerts for each patient which can equip practitioners to act on time.
Eugenie's efficient solution can analyze extensive data from various operational departments to generate comprehensive insights to understand gaps in demand and supply of resources. This can result in the smooth running of daily operations of hospitals by effective resource allocation in terms of labor, logistics, and medications.
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