Cerner Innovation, Inc.

Siemens Aktiengesellschaft

Cerner Health Services, Inc.

Joseph Marcus Overhage

Faisal Farooq

Glenn Fung

Jan DeHaan

Balaji Krishnapuram

Shipeng Yu

John Haley

Farbod Rahmanian

Bharat R Rao

R Bharat Rao

Bharat Sutariya

Vikram Anand

Cole Anthony Erdmann

Sebastian Philipp Brandt

Luigi Iannone

Nicholas Drummond

Ignazio Palmisano

Systems, methods, and storage media useful in a computer healthcare system to consume clinical quality language queries in a programmatic manner are disclosed. Exemplary implementations may: load a CQL query from a third party; transform the CQL query to a modified structured query language query; load medical data elements from a database record store for each patient in one or more defined patient populations; execute the modified SQL query on medical data elements from the database record store for each patient in one or more defined patient populations; and load results of the modified SQL query.

Systems, methods, and storage media useful in a computer healthcare system to consume clinical quality language queries in a programmatic manner

Methods, systems, and computer-readable media are provided for generating and outputting medical billing data. Data is captured by pervasive devices before, during, and after a patient encounter with a healthcare provider. Any unstructured data is transformed into usable structured data, and determinations, such as a service provided by the healthcare provider, are made based on that data as well as other stored data. Based on those determinations, billing data for billing claims related to the patient visit is generated and outputted to a payer of the claims. The billing data may include a medical code that is predicted by a billing model. Billing models may be created from data captured by the pervasive devices from patient visits and data in external data stores, such as electronic health records.

Systems and methods for capturing data, creating billable information and outputting billable information

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

Adaptive medical data collection for medical entities may involve managing content by receiving data indicating a context, identifying at least one application or knowledge base associated with the context, designating the identified application or knowledge base as active, and accessing the active application or knowledge base to provide information at an interface point for a medical professionals and a patient.

Adaptive medical documentation system

Adaptive medical data collection for medical entities may involve triggering an analysis of electronic records in response to information input into an Electronic Medical Record (EMR) of a patient. Determining a potential condition for the patient based on the analysis. Identifying additional information indicated as relevant to the potential condition of the patient, and generating a request for the identified additional information.

Automatic mapping of semantics in healthcare is provided. Data sets have different semantics (e.g., Gender designated with M and F in one system and Sex designated with 1 or 2 in another system). For semantic interoperability, the semantic links between the semantic systems of different healthcare entities are created (e.g., Gender=Sex and/or 1=F and 2=M) by a processor from statistics of the data itself. The distribution of variables, values, or variables and values, with or without other information and/or logic, is used to create a map from one semantic system to another. Similar distributions of other variable and/or values are likely to be for variables and/or values with the same meaning.

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.

Growing community of inventors

Zionville, IL, United States of America

Joseph Marcus Overhage