Cerner Innovation, Inc.

Other

Siemens Medical Solutions Usa, Inc.

Bharat R Rao

Balaji Krishnapuram

Shipeng Yu

Faisal Farooq

Romer E Rosales

Glenn Fung

Jan DeHaan

Joseph Marcus Overhage

Farbod Rahmanian

Christopher Jude Amies

Radu Stefan Niculescu

Sathyakama Sandilya

Arun Kumar Goel

Thomas R Warrick

Hospital readmissions may be prevented. Readmission is prevented by predicting the probability of a given patient to be readmitted. The probability alone may prevent readmission by educating the patient or medical professional. The probability may be predicted during a patient stay and used to generate a workflow action item to reduce the probability, to warn, to output appropriate instructions, and/or assist in avoiding readmission. The probability may be specific to a hospital, physician group, or other entity, allowing prevention to focus on past readmission causes for the given entity.

Healthcare information technology system for predicting or preventing readmissions

Mapping of semantics in healthcare may involve accessing first transaction data in a first database, the first transaction data corresponding to a collection of a first number of fields defined for a condition using a first semantic system to store information and calculating a first distribution of information in the first transaction data. Mapping may also involve accessing second transaction data in a second database, the second transaction data corresponding to a second semantic system different than the first semantic system and the second database comprising a second number of fields using the second semantic system to store information, and calculating a second distribution of information in the second transaction data. The distributions may then be compared and a map relating the semantic systems may be generated and used to communicate between the first and second semantic systems.

Automated mapping of service codes in healthcare systems

Mapping of semantics in healthcare may involve accessing first transaction data of a first healthcare entity in a first database, the first transaction data corresponding to a collection of a first number of fields defined for a condition using a first semantic system to store information and calculating a first distribution of information in the first transaction data. Mapping may also involve accessing second transaction data of a second healthcare entity in a second database, the second transaction data corresponding to a second semantic system different than the first semantic system and the second database comprising a second number of fields using the second semantic system to store information, the second number of fields larger than the first number of fields and calculating a second distribution of information in the second transaction data. The distributions may then be compared and a map relating the semantic systems may be generated.

The present invention provides a data mining framework for mining high-quality structured clinical information. The data mining framework includes a data miner that mines medical information from a computerized patient record (CPR) based on domain-specific knowledge contained in a knowledge base. The data miner includes components for extracting information from the CPR, combining all available evidence in a principled fashion over time, and drawing inferences from this combination process. The mined medical information is stored in a structured CPR which can be a data warehouse.

Patient data mining

Growing community of inventors

Berwyn, PA, United States of America

Bharat R Rao