Abstract: A system for informing self-treatment remedy selection. The system includes a computing device configured to identify a self-treatment remedy associated with a user. The computing device classifies using a remedy classify an identified self-treatment remedy as an input and outputs a self-treatment model. The computing device retrieves a biological extraction associated with a user and calculates a self-treatment model using a machine-learning model. The computing device generates a remedy label and determines using the remedy label if an identified self-treatment remedy is safe for user consumption.

Abstract: A system is provided that is capable of processing clinical trial data from one or more sources, both structured and unstructured data, and integrate such data in a database for access by users. Further, such trial data is combined with other types of data to train an AI-based model, for the purpose of determining a probability of a particular trial outcome, among other insights. Further, the system can provide insights into optimizing this probability, for example by optimizing elements of the clinical trial design. Further, the system stores the acquired data from multiple sources and provides search, comparison, and reporting capability within a single interface.

Abstract: Embodiments for providing clinical practice guidelines by a processor. One or more clinical practice guidelines (CPGs) may be automatically generated according for one or more cohorts using evidence data and patient data from one or more data sources.

Abstract: Computer-implemented tools, techniques, and algorithms are provided for meeting financial targets in connection with medical procedures (e.g., surgical operations) performed by a healthcare entity. A method for restructuring classification of multiple medical procedures for a healthcare entity may be provided in which medical procedure data including charge related data are collected in association with multiple patient encounters for a medical procedure. A data model is created from the collected data and a predetermined number of procedure charge levels are generated each in association with an acuity resource value range. Each medical procedure is then reclassified into one of the procedure charge levels in accordance with at least one level assignment rule. This reclassification can then be processed to determine whether it results in a predetermined desired data distribution and/or meets financial targets for the medical procedure charge data.

Abstract: A system and method for generating a dynamic weighted combination includes a computing device configured to gain a nourishment metric, determine a nourishment vector as a function of the nourishment metric, generate a nourishment programs relating to a plurality of aliments as a function of the nourishment vector, determine a quantitative signature as a function of the nourishment programs, and generate a dynamic weighted combination as a function of the quantitative signature, wherein generating further comprises identifying, for each dynamic weighted combination, a degree of refinement according to the refinement criterion, comparing the degree of refinement for each dynamic weighted combination to the degree of refinement for at least one other dynamic weighted combination, and generate a dynamic weighted combination as a function of the comparison.

Abstract: A method, system and computer program product are provided. A computing system collects information related to a patient's preparation for a medical procedure and provides information regarding the medical procedure and a medical condition of the patient to at least one member of a medical procedure team. Data pertaining to performance of the medical procedure in an operating room is collected from multiple sensors. Progress of the medical procedure is monitored in real time by analyzing the collected data, a procedure reference library, and a physician history to determine a status of the medical procedure. A schedule of one or more subsequent medical procedures for the operating room is adjusted in real time based on the monitored progress. Notifications of the adjusted schedule may be transmitted to participants of the one or more subsequent medical procedures.

Abstract: 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.

Abstract: The present disclosure relates to systems and methods for managing hospital operating room turnover. One exemplary method includes communicating a request associated with a task to one or more users, receiving an acceptance of the request at an acceptance time by the one or more users, determining if the acceptance is on time, starting the task at a task start time, determining if the acceptance is on time based on comparing the task start time with the start due time to determine if the task start time is earlier or equal to the task start due time, indicating task completion by one or more users, recording task completion, and communicating availability of a room.

Abstract: 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.

Abstract: A method and system for identifying individuals in a healthcare dataset who are likely deceased without exposing protected health information to users. The present invention assembles mortality data from different sources, de-identifies this data by removing or modifying all elements regarded as protected health information, and adds a unique encrypted person token to each record. The tokenized mortality data is merged with other healthcare data sets that have likewise been de-identified and tokenized by matching the unique person tokens in data sets against each other. The resulting merged data sets include an indicator of mortality, a uniqueness score giving the likelihood that the person token is unique, and a death validity score giving a measure of confidence that the person is actually deceased.

Abstract: There is provided a method of computing computed pathological indication(s), comprising: receiving an indication of values of current laboratory test results calculated based on an automated analysis of laboratory sample(s) collected from a target individual, selecting classifier(s) according to an analysis of the indication of values of the current laboratory test results, determining additional laboratory test(s) according to the analysis of the indication of values of the current laboratory test results and/or the selected classifier(s), generating instructions for automatic execution of second automatic laboratory testing device(s) on the laboratory sample(s) to obtain a second indication of a second value of the additional laboratory test(s), and evaluating computed pathological indication(s) by applying the selected classifier(s) to the indication of values of the current laboratory test results and the second indication of the second value of the additional laboratory test result(s).