Abstract: A method for predicting simulated patient admissions, comprising: receiving healthcare records for a plurality of patients; adapting the received healthcare records to a common data format; parameterizing the adapted healthcare records to generate a plurality of patient parameters comprising for each patient a day of the week admission parameter, a time of day admission parameter, and a patient type parameter; generating a length of stay parameter for each of the plurality of different patient types; generating a transition probability for each of the plurality of different patient types; predicting, for a time period in the healthcare environment, patient admissions; predicting a care pathway for some or all of the predicted patient admissions during the time period; and reporting, via a user interface, the predicted patient admissions and predicted care pathways.

Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

Abstract: A method (100) for training a scoring system (600) comprising the steps of: (i) providing (110) a scoring system comprising a scoring module (606); (ii) receiving (120) a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (130), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (140), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (160), using the scoring module, one or more features of at least one of the plurality of clusters, and generating, based on the identified one or more features, one or more inclusion criteria for the at least one of the plurality of clusters.

Abstract: A method for generating and presenting a patient readmission risk using a readmission risk analysis system, comprising: (i) receiving information about the patient comprising a plurality of readmission prediction features; (ii) extracting the plurality of readmission prediction features; (iii) generating an initial readmission risk for the patient for each of a first plurality of different future time periods; (iv) updating the plurality of readmission prediction features with one or more new readmission prediction features; (v) generating, by the trained readmission risk model using the one or more new readmission prediction features, an updated readmission risk; (vi) generating an intervention recommendation based on either the initial readmission risk or on the updated readmission risk for one or more of the plurality of different future time periods; and (vii) displaying a generated readmission risk and/or generated intervention recommendation.

Abstract: A method and system for generating real time workload balancing recommendations comprising receiving transition data, medical data, and staffing data; determining a transition probability for each of a plurality of patients; determining a predicted workload to be generated by each of the plurality of patients; simulating the predicted workload to be generated by each of the plurality of patients, the future workload for each of a plurality of units in the hospital; generating staffing recommendations; and displaying the generated staffing recommendations on a user display of the workload balancing system.

Abstract: A method for performing a demand analysis for a hospital, including: (i) receiving hospital capacity information; (ii) receiving hospital data, the hospital data comprising information on patient admissions, patient discharges, and patient transfers for a previous period of time for each of a plurality of patient types; (iii) adapting parameters of a machine learning algorithm based on the hospital data; (iv) receiving clinical information about patients currently admitted in the hospital; and (v) determining, based on output from the adapted machine learning algorithm and using the current clinical information and the hospital capacity information, a predicted patient flow for the hospital in real-time. The method further includes displaying, to at least one user in real-time, the predicted patient flow for the ward and at least one suggested rearrangement of resources within the hospital.

Abstract: A method for performing a demand analysis for a hospital, including: (i) receiving hospital capacity information; (ii) receiving hospital data, the hospital data comprising information on patient admissions, patient discharges, and patient transfers for a previous period of time; (iii) adapting parameters of a machine learning algorithm based on the hospital data; (iv) receiving clinical information about patients currently admitted in the hospital; (v) determining, based on output from the adapted machine learning algorithm and clinical information about the patients currently admitted in the hospital and the hospital capacity information a predicted patient flow for the hospital in real-time; (vi) detecting a deviation between the predicted patient flow and at least one actual data point; and (vii) displaying to at least one user in real-time, the detected deviation for the hospital.

Abstract: A method for performing, using a patient disposition system, a disposition analysis of a plurality of patients to optimize a discharge planning process for each of the plurality of patients, including: (i) receiving electronic medical record information about each of the plurality of patients; (ii) identifying one of a plurality of different patient types for each of the plurality of patients based on the received electronic medical record information; (iii) selecting a trained multi-state model for each identified patient type; and (iv) determining, based on the selected trained multi-state model, a disposition state for each of the plurality of patients in real-time, wherein each disposition state includes a location to which the patient is to be discharged. The method further includes determining at least one service or assessment that can be deferred to the location to which the patient is to be discharged.

Abstract: A method for identifying patients for discharge from a general ward in a hospital, including: calculating a transition score of a patient based upon patient vital sign information; computing a TS upper bound value and a TS lower bound value based upon a set of TS values in a TS time window; determining if a length of stay of the patient is greater than a first time window, greater than an expected length of stay, and greater than a lower evaluation window; determining if a current TS lower bound value is less than a lower threshold; and producing an indication that that the patient is to be evaluated for discharge from the general ward when it is determined that the length of stay of the patient is greater than the first time window, greater than the expected length of stay, and greater than the lower evaluation window and that the current TS lower bound value is less than the lower threshold.

Abstract: A method of determining patient subtyping from disease progression trajectories, including: extracting patient data and related time stamps from patient record data related to a disease, wherein the extracted patient data is incomplete and irregular; building a continuous-time disease progression model based upon the extracted patient data; and building a mixture model for clustering of patient disease trajectory subtypes.

Abstract: A method for processing medical information includes identifying a first patient in a first state, identifying a second patient in a second state, calculating a first risk score for the first patient, calculating a first risk score for the second patient, and determining a risk prone area in a medical facility based on the first risk score for the first patient and the first risk score for the second patient. The first state is an infected state and the second state is different from the first state. The first risk score of the first patient provides an indication of a severity of the infected state of the first patient, and the first risk score of the second patient provides an indication of the second patient being infected by the first patient.

Abstract: A method for generating a telemetry indication score for a patient using a telemetry analysis system, comprising: (i) receiving, by the telemetry analysis system, medical information about the patient comprising one or more patient demographics, one or more physiological measurements, and/or a patient diagnosis; (ii) analyzing the received medical information using a decision support tool, wherein the decision support tool utilizes telemetry guidelines; (iii) determining, by a trained machine learning algorithm using the results of the decision support tool, a telemetry indication score for the patient comprising a probability of whether the patient is likely to meet the telemetry guidelines; and (iv) providing, via a user interface, a telemetry indication report for the patient, wherein the telemetry indication report comprises the telemetry indication score and further wherein the telemetry indication report comprises evidence supporting the telemetry indication score.

Abstract: A method for allocating resources comprising: (i) receiving information about a plurality of patients being monitored by a plurality of healthcare professionals; (ii) receiving information about a monitoring load for each of the plurality of healthcare professionals; (iii) classifying, by a trained monitoring liability classifier, each of the plurality of patients into one of a plurality of monitoring liability classes; (iv) determining a distribution of the plurality of patients for monitoring among the plurality of healthcare professionals based on both the received monitoring load for each of the plurality of healthcare professionals and the monitoring liability class for each of the plurality of patients, wherein the distribution optimizes the monitoring load for each of the plurality of healthcare professionals; and (v) redistributing the plurality of patients for monitoring among the plurality of healthcare professionals according to the determined distribution.

Abstract: Various embodiments of the present disclosure are directed to a general statistical classifier (40) and a personal statistical classifier (50) for executing a patient risk prediction method. In operation, the general statistical classifier (40) may render a singular general independent vital sign risk score for a singular vital sign and/or may render plural general independent vital sign risk scores for plural vital signs.

Abstract: A system for diagnosing pathogenic infection of a person, the system comprising a processor configured for: receiving a trigger comprising data indicative of a possible pathogenic infection; determining, using a risk classifier and medical information about the patient, a risk score for the patient comprising a likelihood that one or more body systems is infected; determining, using a likelihood classifier and the medical information, a likelihood score for the patient comprising an identification of one or more pathogens or pathogen categories that could be causing an infection; determining a relevance score using a relevance classifier and the determined risk and likelihood scores, the relevance score comprising one or more clinical tests relevant to confirming or rejecting the possible pathogenic infection of the person; and reporting, via a user interface, the determined relevance score.

Abstract: The present disclosure is directed to systems and methods for developing an individual-specific patient baseline for a target patient. An exemplary method involves: determining one or more acuity scores for the target patient; identifying patient health data corresponding to one or more low acuity time periods; storing retrospective clinical data from a group of patients in a second database; comparing the patient health data corresponding to the one or more low acuity time periods with retrospective clinical data from a group of patients by identifying one or more patient subgroups; determining the individual-specific patient baseline using an adaptive baseline selection algorithm, wherein the adaptive baseline selection algorithm is used to determine whether to determine the individual-specific patient baseline using patient health data or using retrospective clinical data from one or more patient subgroups; and displaying, using a user interface, the individual-specific patient baseline.

Abstract: A method (100) for training a scoring system (600) comprising the steps of: (i) providing (110) a scoring system comprising a scoring module (606); (ii) receiving (120) a training dataset comprising a plurality of patient data and treatment outcomes; (iii) analyzing (130), using a clinical decision support algorithm, the training dataset to generate a plurality of clinical decision support recommendations; (iv) clustering (140), using the scoring module, the plurality of clinical decision support recommendations into a plurality of clusters; and (v) identifying (160), using the scoring module, one or more features of at least one of the plurality of clusters, and generating, based on the identified one or more features, one or more inclusion criteria for the at least one of the plurality of clusters.