Abstract: Methods, systems, and computer-readable media are disclosed herein for a concurrent comparative tool for assessing sub-models of a data model pipeline in a deployed or pre-deployment environment. The tool may compute a plurality of performance measures that quantitatively assess the performance of each sub-model in the data model pipeline based on a configuration file that facilitates validation of the technological performance and predictive accuracy of the sub-model. Additionally, multiple versions of a sub-model deployed in similar data model pipelines, or in a pre-deployment environment, may be comparatively evaluated. A leading version of the sub-model may be identified and deployed.

Abstract: Methods are provided for the surveillance and monitoring of a patient's medical care when the patient is treated at two or more medical organizations having different medical record systems. Patient information is received from a first medical organization and populated into an active risk assessment array that monitors the patient's risk for developing a particular disease or condition. Patient information is then received from a second medical organization and populated into the array. It is determined that actionable criteria have been met, and in response, a notification or alert is sent to the medical organizations indicating that the patient is at risk.

Abstract: Methods, computer systems, and computer storage media are provided for utilizing machine learning to predict health plans. A machine learning model is trained to predict valid combinations of employer-payer-health plan in response to one or more missing identifiers based on transaction data from electronic data interchange (EDI) insurance transactions that include valid combinations of employer identifier, payer identifier, and health plan identifier. In response to a request to identify a valid combination based on at least one missing identifier, at least one known identifier corresponding to an employer name, a payer name, or a health plan name is inputted and work location data associated with a patient. The machine learning model generates and displays on a user interface, a predicted set of one or more valid combinations of employer-payer-health plans that correspond to the one known identifier and the work location information that is inputted.

Abstract: Computerized methods for modifying at least one component of a clinical order are provided. The methods may include receiving a clinical order, the clinical order having a first component associated with a first phase and a second component associated with a second phase, wherein the first and second components have a first attribute in common, receiving an indication to modify the first attribute of the first component, and modifying the first attribute of the first component and the second component based on the indication received. The methods may further include associating the first and second components of the clinical order to form a cross-phase group and displaying the first and second components in association with an electronic record, e.g., an electronic medical record, in conjunction with a cross-phase group identifier to identify the components as members of the cross-phase group. Computerized systems for performing the disclosed methods are also provided.

Abstract: Technologies are provided for automatically routing service requests (e.g., trouble tickets). More particularly, embodiments of the present invention provide a real-time routing system that helps reduce the wait time on the assignment of client logged support requests. To do so, a batch job may run regularly (e.g., every 5 minutes) to identify newly logged tickets by querying a ticket database corresponding to a ticket system. The smart routing systems utilizes an iterative process to identify the most appropriate associate on the most appropriate support team for assigning the ticket. Once the appropriate associate has been identified, the client as well as the associate may be notified regarding the ticket assignment.

Abstract: Methods, systems, and computer-readable media are disclosed herein for automatically identifying and selecting, based on machine learning model(s), one or more patient educations instructions as suggested smart recommendations for a particular patient, based on parameter matching and filtering with other patient parameters. The parameters of a current patient can be automatically matched to parameters that are associated with other patients, on a one-to-one parameter basis or based on specific combinations parameters. Based on the presence of one or more shared parameters between a current patient and other patients as well as the strength of the parameter match, one or more instructions that were provided to the other patients can be recommended for the current patient automatically. Instruction(s) selected for the current patient can then be stored in association with the parameters of the current patient and used to evaluate and make recommendations for subsequent patients.

Abstract: Methods, computer systems, and computer-storage medium are provided for providing clinical information for a person, such as a test result, immediately and the information automatically and immediately pushed to the ordering physicians device. A graphical representation is generated for a clinician to select clinical notification options. The received selection creates a notification to be automatically sent to one or more clinician devices. Automatic notifications reduce delay in clinician action when new clinician information is provided.

Abstract: Technologies are provided for determining an individual's likelihood of relapsing into prior behavior subsequent to a treatment for a mental health or addiction disorder, and in some instances predicting a likelihood time frame for such a relapse. Target subjects having a risk of addiction relapse, non-adherence to a treatment program, or absconding, may be automatically identified based on a multiplicative-regression model for relative survival (MRS) that is developed for predicting risk or likelihood of relapse or non-adherence. Further, in some embodiments, a leading indicator of near-term future abnormalities may be provided thereby proactively notifying supervisory personnel responsible for the person and providing such personnel with timely notice to enable effective corrective, preventive, or trend-modifying maneuvers to be undertaken.

Abstract: A diagnostic and decision support technology is provided for determining the presence, identity, and/or severity of an inherited lysosomal storage disorder. In particular, a mechanism is provided to detect and classify a lysosomal storage disorder in a human patient, which utilizes a logistic regression classifier determined based on a multi-variable-composite-biomarker comprising a specific set of physiological variables of the patient. This multi-variable statistical predictive biomarker approach may be employed for identifying persons whose attributes are consistent with features of sphingolipidoses, such as Fabry's Disease.

Abstract: 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.

Abstract: 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.

Abstract: Systems, methods, and devices are provided for responding to requests received from users in a healthcare facility, and for generating alerts based upon criticality detected in received verbal utterances. A personal assistant device (PAD) may receive a verbal request for information. The identity of the requestor may be verified and a response to the request may be received by the PAD. In some cases, prior to audibly outputting the response, it may be determined whether the response contains protected health information (PHI). Upon determining that the response contains PHI, the presence of persons other than the patient in proximity to the PAD and/or security of the location of the PAD may be evaluated prior to audibly outputting the response. Additionally, alerts having a heightened criticality may be generated when a received verbal utterance is determined to have a heightened criticality.

Abstract: A computer-implemented trading platform, system, and method are provided for facilitating the trading and accounting for health quality offsets. So-called “health quality credits” related to emissions of ‘potentially avoidable complications’ (PACs) or ‘potentially avoidable mortality’ (PAMs). Emissions trading or “cap-and-trade” is an administrative approach used to control emissions that degrade public-goods assets by providing economic incentives for achieving and sustaining economically significant reductions in the emissions of pollutants that impair public goods such as clean air or health.

Abstract: Methods, systems, and computer-storage media are provided for generating graphical representations of audit events. A party-of-interest is represented by a central node, and one or more peripheral nodes surrounding the central node represent parties having electronic records accessed by the party-of-interest during a selected time frame. The size of the peripheral nodes represents a frequency of access of the node's respective electronic record. Each of the peripheral nodes is actionable enabling a user to view information related to the audit event.

Abstract: Systems and methods are provided for managing patient assistance requests in a healthcare facility, documenting items (e.g., minor, routine, and/or frequently-performed items) in association with a patient's records in an Electronic Healthcare Information System, and cancelling patient requests for assistance. Indications that requests for assistance have been received and/or are being addressed by an appropriate healthcare team member may be audibly output from a speaker associated with a personal assistant device. Healthcare team members may verbally provide items for documentation in association with a patient's medical records, the items for documentation being received by a listening component of a personal assistant device and transmitted to an EHIS for documentation.

Abstract: Methods and systems are provided for detecting objects or patient safety events in a patient room. Artificial intelligence is utilized to enhance safety issue recognition capabilities by the methods and systems. Sensors collect a series of images and depth data in a room of a patient. Data (corresponding to images and depth data of an object or patent safety event) is received from the sensors and compared to stored data to identify the object or patient safety event. The images are communicated to a central video monitoring system and a user may be prompted to confirm if the object requires learning or a patient safety event occurred (or identify the object or patient safety event) or to provide additional parameters or actions. A patient safety learning system analyzes the data and incorporates the user response to enhance safety issue recognition capabilities of the system and reduce false alerts.

Abstract: Methods, systems, and computer-readable media for a system and method are provided for assessing the value of predictive models monitoring medical conditions. Trends in an individual's medical condition are determined based on monitoring values, and the trends are associated with the actions performed in response to the monitoring values and in accordance with the predictive models. The trends may indicate that an individual's condition is improving, worsening, or remaining stable in response to the action taken. Models used for multiple conditions for an individual or for a population of individuals may be assessed in this manner to generate knowledge regarding the performance of the models based on the actions taken. This knowledge may be used to assess the value of the models in terms of the models' performance and may provide insight on way to improve the models.

Abstract: Methods, systems, and computer-readable media are disclosed herein combine randomization functionalities with the machine-learning prioritization of workflows for performance testing. In aspects, a primary workflow having a sequence of user interface steps is input. Testing workflows are generated that represent each variable position of unlocked steps in the sequence of the primary workflow while maintaining the sequential position of any locked steps. These testing workflows are then ingested to a machine learning model that identifies as subset of the testing workflows to prioritize over other. Specifically, testing workflows are prioritized that at least partially match sequence patterns in historical workflow data that is associated with vulnerable computer code. The subset is output and tested by testing engine to generate a report of any vulnerable computer code.

Abstract: Methods, systems, and computer-storage media are provided for utilizing multiple Picture Archiving Communication Systems (PACS) to view one more medical images by storing one or more PACS at a database within the system. Requests are received from one or more users at one or more facilities to utilize one or more PACS to view one or more medical images. After accessing the database to determine one or more PACS authorized for each facility from which a request is received, one or more users are provided with one or more PACS to view medical images associated with radiological exams and provide the necessary assessments and reports for treatment.

Abstract: In some examples, data associated one or more medical presentations of a person is received. A mapping that associates the data to one or more candidate diagnoses is determined. The one or more candidate diagnoses correspond to one or more possible medical conditions associated with the person. A validation score for a first candidate diagnosis, of the one or more candidate diagnoses, is generated based on a set of features associated with the person. The validation score indicates an accuracy of the candidate diagnosis. At least partially in response to the generating of the validation score, an indicator is caused to be provided at a user device. The indicator indicates the validation score.