Cerner Innovation, Inc.

Cerner Corporation

John Christopher Murrish

Douglas S McNair

Kanakasabha K Kailasam

Tanuj Gupta

David Cohen

Alex Vickers

J Bryan Ince

Josh Smith

Mark A Hoffman

Michael Alan Ash

Sasanka Are

Bharat Sutariya

Hugh H Ryan

Leo V Perez

Douglas S Mcnair

John Kuckelman

Thomas Anthony Fangman

Andrew Roberts

Mario Manese

William Stadler

Pedro Alves

Kanakasabha Kailasam

Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients. Embodiments of the invention provide decision support services including providing timely contextual patient information including condition risks, risk factors and relevant clinical information that are dynamically updatable; imputing missing patient information; dynamically generating assessments for obtaining additional patient information based on context; data-mining and information discovery services including discovering new knowledge; identifying or evaluating treatments or sequences of patient care actions and behaviors, and providing recommendations based on this; intelligent, adaptive decision support services including identifying critical junctures in patient care processes, such as points in time that warrant close attention by caregivers; near-real time querying across diverse health records data sources, which may use diverse clinical nomenclatures and ontologies; improved natural language processing services; and other decision support services.

User interface for clinical decision support

Dynamically determining risk of clinical condition

Systems, methods, and computer-readable storage media are provided for determining and ascribing clinical conditions or diagnoses to patients and provide them to a caregiver, such as attending clinicians or other appropriate health services personnel. In particular, embodiments of the disclosure determine likely phenotypic findings that are salient to the decision-making context for a current human patient, based on anticipative sequence-mining and trajectory-mining. A sequential pattern mining and sequence itemset matching system is provided for determining likely, temporally-relevant concepts that are manifested in the information that is produced during the course of a patient's care. A clinician or caregiver may be provided the sequence itemset matching by generating a list or notice. In addition or alternatively, the results may be stored in an EHR associated with the patient.

Clinical decision support system using phenotypic features

Methods, systems, and computer-readable media are provided for managing health status of persons with a chronic condition including providing dynamic, adaptive monitoring, detection, and prediction of suicide risk to a person at risk for suicide related to mental health. In an embodiment, a patient-assessment application is used to obtain information periodically on a patient's mental health status. Based on this information, a logistic regression model is employed to determine a patient's probability of attempting suicide. The probability is evaluated against a default threshold to determine if the patient's status has changed significantly, and if the threshold is exceeded, an action is evoked. In one embodiment, the action includes providing notice to the patient's care provider, caregiver, or case manager.

Decision support for managing mental health conditions

Methods and systems for enhanced natural language processing of clinical documentation are provided. Using natural language processing, a clinical condition is extracted from unstructured data within a current electronic document. A clinical ontology identifying itemsets associated with the clinical condition is retrieved, and indicators of relevant clinical concepts, as identified from the ontology, are searched from within the patient's longitudinal record, which comprises documentation from at least a prior encounter. Based on the whether the clinical concepts are present in the patent's record, a confidence is assigned to the NLP-extracted clinical condition, and one or more actions may be performed.

Systems and methods for enhancing natural language processing

Methods, systems, and computer-readable media are provided for facilitating record matching and entity resolution and for enabling improvements in record linkage. A power-spectrum-based temporal pattern-specific weight may be incorporated into record linkage methods to enhance the record linkage accuracy and statistical performance. For example, in embodiments, a value-specific weight may be calculated from a population-based frequency of field-specific values and provides an opportunity to capture and measure the relative importance of specific values found in a field. A timeseries-derived Bayesian power spectrum weight may be calculated from the population-based frequency of temporal pattern-specific values in terms of intensities at various frequencies of the power spectrum computed from the timeseries, and further provides an opportunity to capture and measure the relative importance of specific sequences of care episodes.

System and method for record linkage

A system, method, and computer-readable media are provided for facilitating clinical decision making, and in particular, facilitating treatment of a person having congestive heart failure. The method includes the step of receiving patient information for a patient and using at least one solver to determine a patient condition or recommended treatment based on patient parameters or patient information.

Computerized systems and methods for facilitating clinical decision making

Embodiments of the invention provide decision support services, including an enhanced user interface. A reason for a clinical visit is determined and an initial workflow may be provided via the user interface during a clinical visit. The workflow may include a set of initial user interface elements that are determined based on a context associated with the clinical visit. The set of initial user interface elements may include a first initial interface element associated with a pre-identified clinical concept. During the clinical visit, a first suggested interface element can be provided. The first suggested interface element is generated during the workflow based on detecting a selection of the first initial interface element. The first suggested interface element can be associated with a first suggested clinical concept that is related to the pre-identified clinical concept.

Determining new knowledge for clinical decision support

Dynamic assessment for decision support

Systems, methods, and computer-readable media are provided for facilitating clinical decision making by directing the emission of computer-generated health-care related recommendations towards contexts in which the recipient will likely find the recommendations salient and will likely welcome them and act upon them. 'Uptake' of computer-generated recommendations for diagnostic tests or therapeutic interventions is thereby substantially increased, and 'alert fatigue' is substantially decreased. Embodiments of our technology overcome certain drawbacks associated with the prior art by providing a means for ascertaining which decision-support recommendations are likely to be favorably considered by the recipient and acted-upon (recommendation &#x2018;uptake&#x2019;). System and method embodiments for providing a predicted probability of user uptake of a context-specific system-generated recommendation patient are disclosed herein and for applying that information to decide whether or not to emit the relevant recommendation.

Decision-support recommendation optimization

Enhanced natural language processing

Methods, computer systems, and computer readable media for transforming raw healthcare data into relevant healthcare data are provided. Transformation of raw healthcare data into relevant data is accomplished by receiving raw data from a plurality of disparate data sources, indexing the data, and mapping the data. Embodiments transform raw data into relevant data through the use of natural language processing, synonymy and ontology mapping, and adaptive knowledge processing. The relevant data is stored in a one record that includes information necessary to understand and represent that patient.

Health information transformation system

Growing community of inventors

Overland Park, KS, United States of America

John Christopher Murrish