Cerner Innovation, Inc.

Siemens Medical Solutions Usa, Inc.

Other

International Business Machines Corporation

Siemens Aktiengesellschaft

Cerner Health Services, Inc.

Faisal Farooq

Balaji Krishnapuram

Shipeng Yu

Glenn Fung

Joseph Marcus Overhage

Jan DeHaan

Bharat R Rao

Romer E Rosales

R Bharat Rao

John Haley

Farbod Rahmanian

Marie Angelopoulos

Vijay Krishnarao Naik

Shilpa N Mahatma

Vikram Anand

Michal Ozery-Flato

Sebastian Philipp Brandt

Luigi Iannone

Nicholas Drummond

Ignazio Palmisano

Brendan Shea

Stanley Chung

John Weis

Leemor M Yuravlivker

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

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

A mechanism is provided in a data processing system to implement a feature extraction tool for graphical user interface based feature extraction. The feature extraction tool receives selection by a user of a dataset from which features are to be extracted. The feature extraction tool loads a plurality of feature definitions. The feature extraction tool generates a graphical user interface that allows the user to add features from the plurality of features to a feature file. The feature extraction tool presents the graphical user interface to the user and receives user selection of at least one feature to be added to the feature file. The feature extraction tool generates the feature file based on the user selection of the at least one feature.

Graphical user interface based feature extraction application for machine learning and cognitive models

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.

A method, including receiving a data source selection from a user or software application, the data source including medical information of a plurality of patients, receiving, from the user or software application, a data pattern that is related to a concept to be explored in the data source, querying the data source to find information that approximately matches the data pattern; and receiving the information from the data source, wherein the information includes unstructured data, assigning a classification to individual parts of the information based on the part's relationship to the data pattern, and outputting the classified information to the user or software application.

Automated patient/document identification and categorization for medical data

Growing community of inventors

Norristown, PA, United States of America

Faisal Farooq