Abstract: Disclosed herein is a system for generating real time staffing recommendations for a hospital. The system receives data, including transition data for multiple patients, medical data for the multiple patients, and staffing data. Using the data, the system determines a transition probability for each patient and a predicted workload for each patient. The system simulates unit workloads for multiple units in the hospital using the transition probability and predicted workload for each patient. The system generates staffing recommendations using the simulated unit workloads and displays the staffing recommendations on a user display.

Abstract: A method (100) for predicting a sepsis status of a patient using a sepsis analysis system (200) comprising: (i) receiving (120) demographic information about the patient; (ii) receiving (130) observation data from a patient monitoring device; (iii) receiving (140) one or more patient responses provided by the patient in response to one or more queries or commands posed to the patient by the sepsis analysis system, and wherein the one or more queries or commands are configured to elicit, in the one or more patient responses, information relevant to the patient's sepsis status; (iv) analyzing (150), by a trained patient sepsis status model of the sepsis analysis system, the received demographic information, the received observation data, and the one or more patient responses to predict a sepsis status for the patient; and (v) reporting (170), via a user interface of the sepsis analysis system, the predicted sepsis status.

Abstract: Generally, in one aspect a process for providing adaptive suggestions in the CPOE is provided. The process includes: (i) generating a rule set from historic patient data (ii) receiving data specific to a patient (iii) determining, by a processor of the adaptive suggestion system containing a set of created rules, the most likely entry for that component of the CPOE based on the rule set and received patient data (iv) displaying via user interface the suggested entry for the component of the CPOE.

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: 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; (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 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, 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 for performing, using a victim triage system, triage analysis of victims of an incident, comprising: (i) receiving a location of the incident, medical information, hospital capability information for hospitals in a predetermined vicinity of the location, and transport information relative to the location; (ii) determining, by a trained triage machine learning algorithm using the received information, a triage decision for the victims, wherein the triage decision for a victim comprises: (1) a probability of the victim's survival over time; (2) a recommendation to transport or not transport the victim to a hospital; and (3) to which of the two or more hospitals the victim should be transported; (iii) generating (140) a triage report comprising the determined triage decision for each of the plurality of victims; and (iv) displaying the triage report on a user display of the victim triage system.

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.