International Business Machines Corporation

Other

Epic Systems Corporation

Merative US L.p.

Paul C Tang

Kenney Ng

Uri Kartoun

Michael Alan Picheny

Andrew Jason Lavery

Yoonyoung Park

Charalambos Stavropoulos

Amy Chiu

Murthy V Devarakonda

Sarah J Miller

Francis Campion

Tanya Rudakevych

Sophie Batchelder

Amarendra Das

Preethi Raghavan

Charles Young

Veronica Aldous

Michael J LaScaleia

Jeffrey Lawrence Sokolov

Amar Das

David Osofsky

Melissa Honour

Jacob Green

A plurality of events are extracted from a plurality of electronic health records associated with a first patient. The extracted plurality of events are analyzed to identify a plurality of stimulus events and a plurality of response events. An association between a first stimulus event and a first response event is determined. A stimulus-response (SR) variable is generated for the first patient based at least in part on the determined association, and the generated SR variable is integrated into one or more predictive cognitive models.

Identifying and extracting stimulus-response variables from electronic health records

A plurality of attributes are extracted from a plurality of electronic health records, where each electronic health record is associated with a patient in a plurality of patients. Additionally, a training data set and a scoring data set are generated based on the plurality of attributes, and a patient similarity model is trained based on the training data set. A precision cohort is identified, where the precision cohort includes patients in the plurality of patients from the scoring data set that are similar to a first patient based on an electronic health record of the first patient and the similarity model. At least one result statistic for each of a plurality of treatments given to patients in the precision cohort is determined, and a first treatment of the plurality of treatments is selected for the first patient based at least in part on the determined result statistics.

Generating personalized treatment options using precision cohorts and data driven models

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a question prediction and answering engine for predicting questions a medical professional is attempting to answer. An interaction monitoring component monitors interaction of a medical professional with a patient electronic medical record (EMR). A question selection component selects a set of questions the medical professional is attempting to obtain an answer to from the patient EMR. The question prediction and answering engine analyzes the patient EMR to generate a set of answers to the set of questions from at least a portion of the patient EMR and outputs a report correlating the set of questions and the set of answers to the medical professional.

Automated prediction and answering of medical professional questions directed to patient based on EMR

A notation assistant system has a machine learning system, a notation processing system, a scoring system, and a suggestion system. The machine learning system trains a classifier for rating a factor related to a clinical narrative note that describes a patient's health status. The notation processing system processes a clinical narrative note and the scoring system determines a factor rating, such as a completion score or a clarity score. The scoring system provides the factor rating to an end-user device to display to a user. The notation assistant system is configured to perform a method as a user is entering a clinical narrative note to provide real-time feedback, such as the factor rating. The suggestion system is configured to provide suggestions for modifying the clinical narrative note to improve the rating factor. The notation assistant system applies to patient health conditions such as a health status of a patient's heart for a patient receiving care for congestive heart failure.

Notation assistant system for providing feedback on a clinical narrative note

A system and associated method for assessing level of completeness of healthcare data storage based on collected data. The system includes a collection system, a measurement system, an expectation system, and an alerting system. In a method of assessing the health of stored data, the system collects real-time data from at least one data source, determines measurements for a plurality of parameters based on the collected data, and generates expectations for a future period of time for the plurality of parameters based on data analysis technique. The system also compares the expectations for the future period of time to subsequent measurements collected for that period of time to determine whether the subsequent measurements satisfy an expectation threshold and provide an alert to a client terminal. The alert is a result of the comparison of the expectations and the subsequent measurements and provides an assessment of data storage quality and alerts on anticipated deficiencies.

Systems and methods for determining data storage health and alerting to breakdowns in data collection

A method, a system, and a computer program product are provided. Speech signals from a medical conversation between a medical provider and a patient are converted to text based on a first domain model associated with a medical scenario. The first domain model is selected from multiple domain models associated with a workflow of the medical provider. One or more triggers are detected, each of which indicates a respective change in the medical scenario. A corresponding second domain model is applied to the medical conversation to more accurately convert the speech signals to text in response to each of the detected one or more triggers. The corresponding second domain model is associated with a respective change in the medical scenario of the workflow of the medical provider. A clinical note is provided based on the text produced by converting the speech signals.

Optimizing speech to text conversion and text summarization using a medical provider workflow model

A mechanism is provided in a data processing system comprising a processor and a memory, the memory comprising instructions that are executed by the processor to specifically configure the processor to implement a relevant information graphical presentation engine for providing a graphical user interface (GUI) that presents information from a patient electronic medical record (EMR). The relevant information graphical presentation engine generates a GUI presenting patient EMR data pertinent to a condition of the patient. The relevant information graphical presentation engine identifies a change in treatment recorded in the patient EMR, submits a question to a healthcare cognitive system to query the patient EMR for a reason for the change in treatment, and receives at least one answer from the healthcare cognitive system identifying of a reason portion of the patient EMR that provides a reason for the change in treatment. The relevant information graphical presentation engine generates a GUI element that references the reason portion of the patient EMR and inserts the GUI element in the GUI in association with the information from the patient EMR pertinent to the outcome. The relevant information graphical presentation engine outputs the GUI to the healthcare professional.

Contextual EMR based dashboard graphical user interface elements

A mechanism is provided for presenting contextually relevant patient data in relation to other patients in a graphical user interface. Using a medical condition associated with a patient and a current treatment being followed by the patient, a cohort of similar patients with the same medical condition are identified. For each of the patients in the cohort, a set of next treatments are identified for those patients that stopped the current treatment and followed a next treatment. For each next treatment followed by a patent in the cohort of similar patients, a determination is made as to whether the next treatment controlled or failed to control the same medical condition for the patient. Then a presentation is provided indicating the set of next treatments, a number of patients following each next treatment, whether each treatment controlled or failed to control the same medical condition, and a statistical significance.

Presenting contextually relevant patient data in relation to other patients to a medical professional

A method, computer system, and a computer program product for optimizing speech recognition in a smart medical room. The present invention may include selecting, from a database, one or more speech domain models based on a plurality of signals from a plurality of biometric sensors associated with a plurality of medical equipment, wherein the one or more speech domain models are trained with one or more feedback from a clinician based on a medical encounter and from a continuous feedback display in the smart medical room, wherein the one or more feedback from the clinician is based on an optional notification to the clinician to confirm the one or more speech models in use.

Smart medical room optimization of speech recognition systems

A method, computer system, and a computer program product for optimizing speech recognition in a smart medical room. The present invention may include receiving a piece of verbal data associated with a medical encounter from one or more audio recording devices. The present invention may also include accessing a plurality of signals from a plurality of biometric sensors associated with a plurality of medical equipment associated with the smart medical room based on the received piece of verbal data associated with the medical encounter. The present invention may further include selecting, from a database, one or more speech domain models based on the accessed plurality of signals from the plurality of biometric sensors associated with the plurality of medical equipment, wherein the one or more speech domain models are utilized to optimize a transcription of speech during the medical encounter in the smart medical room.

An active guidelines capability or component is linked to a computerized patient record system to integrate the use of clinical guidelines in the workflow of clinicians treating patients. Many healthcare institutions maintain sets of clinical guidelines describing recommended treatment or analysis options for patients displaying sets of symptoms or for whom certain diagnoses have been made. The active guidelines feature adds an active guidelines tag to such clinical guidelines so that when the clinician accesses the clinical guidelines and wishes to follow the recommendation, the clinician merely has to click on a hypertext created from the tag which then transmits action orders, also contained in the tag, to be transmitted to the computerized patient record system for implementation.

Growing community of inventors

Los Altos, CA, United States of America

Paul C Tang