Get more out of your data by pushing the limits of the types of analytics you can do with bAIcis™. The Berg Analytics platform enables agile data analysis with incredibly fast parallel processing, scalability to process massive volumes of data, and rich Big Data architecture. Berg Analytics provides a comprehensive set of analytics tools that leverage cutting edge research in machine learning and artificial intelligence.
The analytics tools range from: data mapping, factor analysis, machine learning, probabilistic inference, Bayesian networks and predictive modeling to big data integration.
- Ability to analyze a variety of structured and unstructured data – EMRs, clinical, operational, financial data and genomic data – to match treatments with outcomes, predict patients at risk for readmission and provide more efficient care at a reduced cost
- Analyze provider populations, patient characteristics and associated costs & outcomes to identify and predict the most clinically effective & cost efficient interventions
- Identify, predict and minimize fraud by instituting advanced analytics for detection, accuracy and consistency of claims
- Use historical data to personalize medical care by predicting or estimating patient outcomes
- Ability to apply Big Data analyses to extract clinically actionable information from gene sequencing data along with other key patient characteristics, medication and lab values
A CASE STUDY: UTILIZATION OF LOW VALUE PROCEDURES IN MEDICARE POPULATION
Berg Analytics' bAIcis™ platform was used to decipher the utilization rates and costs of healthcare services with questionable benefits or the low-value procedures across the medicare population in the US. Berg Analytics analyzed the entire limited CMS claims data set for the period of several consecutive years. Low-value procedures are defined by:
- Choosing wisely campaign
- US preventive services task force
- UK National Institute of Health and care Excellence
- Canadian agency for drugs and technologies in health