Cerner Innovation, Inc.

Siemens Medical Solutions Usa, Inc.

Farbod Rahmanian

Radu Stefan Niculescu

William Alexander Landi

Balaji Krishnapuram

Shipeng Yu

William Michael Hasling

Todd W Fritsche

Vikram Anand

Camille Anne Egan

Brian Thornton

Bette Jane Reese

Muthu Premananth

Daniel Antohe

R Bharat Rao

Glenn Fung

Jan DeHaan

Romer E Rosales

Faisal Farooq

Joseph Marcus Overhage

Bharat R Rao

Sriram Krishnan

Harald Steck

Markus Bundschus

By extracting clinical data of any format from respective different sources, a data repository normalized to a generic format is created. A medical domain specific language may be used to interact with the data repository for identifying cohorts and gaps in care for the respective cohorts. Any rules for finding gaps in care are converted into the medical domain specific language for determining gaps. This standardization in both the data repository and rule application may allow for a true cost and time to value solution accessible to many different medical practices.

Gap in care determination using a generic repository for healthcare

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.

Medical related quality of care information is extracted and edited for reporting. Patient records are mined. The mining may include mining unstructured data to create structured information. Measures are derived automatically from the structured information. A user may then edit the measures, data points used to derive the measures, or other quality metric based on expert review. The editing may allow for a better quality report. Tools may be provided to configure reports, allowing generation of new or different reports.

Quality metric extraction and editing for medical data

Quality improvement factors in patient care are ranked. Hospital performance is measured, such as a CMS measure. The variables and/or values relative contribution to quality of care is determined using medical records of the hospital. The variables and/or values are ranked according influence of the quality of care result. The ranking is performed by a given medical institution at a desired time rather than based on a broad study. The medical institution may regularly determine variables (e.g., admitting doctor) and/or values (e.g., doctor X) that are relevant to a decreased quality of care. Quality may be regularly improved using a software product.

System and method for ranking quality improvement factors in patient care

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Leesport, PA, United States of America

Farbod Rahmanian