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Treatment Grouping and Risk Analysis
Introducing our ground-breaking technology for Episode Grouping: The DDI Grouper.
Analyzing, grouping and forecasting complex healthcare insurance claim data is difficult – especially when there are thousands of medical codes to evaluate. Our AI solution, the DDI Grouper and Risk technology, makes things simpler.
Administrative data health claims and demographic data are used by public and private entities to evaluate the health state, resource utilization, and current and future risk associated with individuals and populations. To do so, organized systems for categorizing conditions, procedures and drugs are required. Typically, the following fields from medical and pharmacy claims are used for administrative data analysis:
Number of Codes
Classifies an individual’s diseases/conditions
Describes and classifies procedures provided to an individual
Identifies drug(s) provided to an individual
As can be seen, the number of individual codes makes analysis of the health status and resource use for populations or individuals unmanageable if individual codes are used as input variables.
What are Episode Groupers?
To provide information a form suitable for analyzing the health status, resource utilization, and risk characteristics of a group as a basis for making financial, public health, and resource allocation decisions, categorization (“grouping”) of individual codes into rational groups is necessary. For example: There are more than one hundred ICD10 codes for atherosclerosis; these codes can be categorized into five (rather than one hundred-plus) categories (groups). Similar strategies can be implemented for procedure and drug codes.
The DDI Grouper (DDIG)
There are a number of grouper technologies in the marketplace; these have variously been developed by private and public entities. Some are marketed commercially. To address the shortcomings of most of these groupers, DDI has developed a grouping technology that meets these key requirements:
The probability of hospitalization for an individual is just one example of risk forecasting used by providers and other care managers to target services to the most at-risk members of a group.
Forecasting is used by payors to determine provider payment, by underwriters to set insurance premiums, and by employers to budget for employees’ health costs.
Risk forecasting is most accurate when it considers an individual’s:
Risk forecasting is best based on input from a grouper to avoid the problem of analyzing too many raw input variables (codes).
DDIR is a risk analysis and risk forecasting AI and Big Data technology. It has been developed using statistical methods and machine learning models with the DDIG grouping methodology as a foundation.
Through the DDIR system, a clinical and demographic risk profile is generated using age, gender and mix of DDIRs for a particular patient. This profile provides impact of current and future health risk on health care costs and health status.
We know there is lot of hype surrounding Data Science. What makes us different? We believe we can truly improve quality of life with the solutions we’re building. Our passion is to create technologies that learn and help humans make smarter and more efficient decisions. This will allow humans to more confidently make decisions that would be otherwise impossible or apprehendingly daunting with less progressive solutions. Our 100+ years of combined multi-disciplinary AI experience allows us to build solutions across many industries including Healthcare, Finance, Supply Chain, Retail, Agriculture, Hospitality, Gaming and Legal.
Adherence to delivering quality cost-effective solutions. Openness to market opportunities that are not dependent upon one geographic region.
We currently have offices in the United States and Sri Lanka. We have the ability to grow a multi-national client base.
We are engaged in continued research and development to improve upon best practices and effectively compete with market competitors, in respect to quality and cost, in a rapidly growing market. (e.g. provision of solutions when limited data is available).