AI-enabled predictive analytics and risk assessment in healthcare

Target audience

Healthcare managers

Managerial and predictive analytics for morbidity and mortality control

• Management based on big data and machine learning models

• Automatic risk stratification of the entire population of the region

• Prediction of disease development and mortality forecasting

• Population monitoring of the prevalence of risk factors

• Control of the appropriate EHR management

• Monitoring of the achievement of target levels and metrics

Clinicians

Clinical decision support for the reduction of the burden and decrease of medical errors

• Clinical Decision Support System (CDSS)

• Automatic analysis of electronic health records (EHR) using AI

• Extraction of medical information from unstructured medical records

• Automatic determination of the likelihood of disease development, identification of risk factors and a final comprehensive assessment of patient’s risk group

• Identification of suspected diseases (symptom checker)

• Individual recommendations to physicians and patients based on clinical guidelines

Pharmaceutical companies

Reduction of the cost of clinical trials by analyzing routine clinical practice data

• Analysis of real-world clinical data (RWD)

• Automatic detection of suspicions of various early-stage diseases at, helping physicians in making correct clinical decisions

• Search for candidates for clinical trials

• Improving the effectiveness of drug therapy due to accurate identification of high-risk patients

• Search for unknown disease predictors based on RWD analysis

• Creation of custom machine learning models

Research organizations

Development of scientific research in the field of artificial intelligence

• Participation in joint scientific research in the field of artificial intelligence and digital technologies in medicine

• Building datasets for research and development

• Machine learning and custom predictive model development

• RWD research

• Implementation of innovative technologies in research and educational activities

Insurance companiess

Reduction of the cost of patient care and insurance claims

• Automatic assessment of patients' insurance risks (underwriting)

• Reduced insurance premiums due to personalized assessment of patients' EHRs and more effective prevention

• Identification of patients at high risk of hospitalization, disability, etc.

• Building custom machine learning models

Contact Us

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