eMed Support Systems
eMed Support Systems has developed a Digital Patient Twin to analyze over 120 clinical characteristics from Electronic Health Records (EHRs) and generate analytics to make data-driven decisions and manage chronic cardiovascular patients proactively in order to save their lives, payers’ costs and physicians’ time. With actionable dashboards healthcare providers and physicians can view patients triaged by their cardiovascular risks and a list of patients not achieving their treatment goals for proactive management. Personalized Clinical Decision Support System (CDSS) report is available on every patient with optimal treatment strategy recommendations according to clinical guidelines. When some crucial data is not captured about the patient (e.g. cholesterol level) eMed Support identifies patients with possible deviations from target levels with an AI prediction algorithm, so they can be tested proactively, diagnosed and start the treatment earlier.
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in5 Tech
Al Sufouh 2
Dubai
United Arab Emirates
+971 50 891 5362
https://emedsup.org/
Funding 💰
Key people 🧑🤝🧑
- Denis Losik - Co-founder & CEO
- Alexey Penskikh - Co-founder & CTO
- Kseniya Ponomareva - Business Development Manager
- Konstantin Armyanov - Co-founder & CBDO
Highlights ⭐
- Solving the problem: Patients usually have a lot of comorbidities, and clinicians have to know a huge number of medications as well as how they interact. Thus, in practice, it can be difficult to treat a patient, especially in a situation where a quick decision is required because a diagnosis must be made based on available data from libraries such as PubMed in addition to dozens of clinical guidelines. eMedSupport solves this by helping physicians make evidence-based medicine (EBM) prescriptions and control achievements of treatment targets to prevent every patient from suffering from cardiovascular catastrophe.
- It matters: Cardiovascular diseases (CVDs) are the number 1 cause of mortality, accounting for 2/3 of all deaths in the UAE. Globally, CVDs are the leading cause of death globally, taking an estimated 17.9 million lives each year, according to WHO. 🔗
- Years in the making: It took research doctors experienced in Clinical Practice Guidelines and specialists in modeling and meta-analysis more than four years to develop the current version of CDSS. Thus, a self-sufficient ecosystem was created for collecting quality Big Data in medicine, analyzing and training AI to select the best therapy for each patient.
- Data-based solution: eMedSupport's solution was developed based on clinical guidelines, drug reference books, prognostic scales, and clinical trial data. Its predictive AI is trained on real-world clinical data, including EMR structured and unstructured data, laboratory data, up-to-date world clinical studies, and image data.
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