Abstract: The present disclosure pertains to a system configured to prepare and use prediction models for socioeconomic data and missing value prediction. Some embodiments may: extract, from received population segment data, a training set of socioeconomic parameter values for each population segment; provide, to a prediction model as input, first parameter values of the respective training set for the prediction of additional parameter values of the training set such that the prediction of the additional parameter values is performed without reliance on the additional parameter values; provide, for each of the training sets, the additional parameter values to the prediction model as reference feedback for the prediction model's prediction of the additional parameter values to train the prediction model; and predict, based on a working set of parameter values for a population segment, additional values for the working set using the prediction model subsequent to its training.

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 system is configured to display distributions of predicted health outcome information for patient populations in geographical areas. The system is configured to obtain demographic, social (e.g., including environmental), and prior health outcome information for a patient population in a geographical area. The system is configured to train a prediction model based on the demographic, social, and prior health outcome information, which outputs weighted features of the demographic and social information that are predictive of health outcomes for the patient population. The system is configured to cause display of a distribution of predicted health outcome information for the patient population in the geographical area based on the weighted features.

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 for generating and presenting a patient readmission risk using a readmission risk analysis system, comprising: (i) receiving information about the patient, wherein the information comprises a plurality of readmission prediction features; (ii) extracting the plurality of readmission prediction features from the received information; (iii) analyzing the readmission prediction features to determine whether each of a predetermined list of readmission prediction features are present; (iv) replacing one or more identified missing readmission prediction features with a null value to generate a complete set of readmission prediction features for the patient; (v) analyzing the complete set of readmission prediction features for the patient to generate a readmission risk score; (vi) determining, using a populated lookup table of the readmission risk analysis system, an AUC score; and (vii) displaying the generated readmission risk score and the determined AUC score.

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 presenting a patient frequent readmission recommendation, comprising: (i) receiving patient information comprising a plurality of demographic and/or medical features; (ii) extracting the features from the information; (iii) analyzing the features to determine whether the patient is a frequent readmission patient or is at risk of being a frequent readmission patient; (iv) estimating, if the patient is determined to be a frequent readmission patient, whether the frequent readmission is due to a medical condition and/or a socioeconomic condition, or predicting a frequent readmission risk level if the patient is determined to be at risk of being a frequent readmission patient; (v) generating a recommendation based at least in part on the estimated condition or the frequent readmission risk level, wherein the recommendation comprises a medical intervention and/or a socio-behavioral intervention; and (vi) providing (180) the recommendation via a user interface.

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 scheduling patients for medical appointments, including: predicting the no-show risk and no-show cost for the patients; forecasting the cost of a transportation assistance service for the patients; optimizing the scheduling of patients based upon cost of the transportation assistance service, the no-show risk, and the no-show cost; selecting a population of patients to receive the transportation assistance service; and scheduling the population of patients for their medical appointment and transportation assistance service.

Abstract: A care plan tool for defining care plans for patients with constrained care plan resources, including: a patient clustering and risk stratification module configured to cluster a group of patients into patient cohorts and configured to produce a machine learning model to predict the risk of a medical condition for each patient cohort based upon medical data for the patients in the cohort; an optimization module configured to determine an optimized care plan for each patient cohort based upon the machine learning models for each patient cohort and the constrained care plan resources; a matching care plan module configured to receive patient data for new patients and configured to match the new patients to a patient cohort and associated care plan; and a new care plan optimization module configured to receive patient specific constraints for new patients from a care manager and configured to determine a new optimized care plan for each new patient based upon the machine learning models for each patient cohort,

Abstract: A care plan system, including: a key performance indicator (KPI) model configured to predict the value of a KPI for a specific patient based upon patient data and care plan elements; cost data indicating the cost of the care plan elements; a graphical user interface (GUI) configured to receive first suggested care plan elements, to provide a first predicted value of the KPI and a first care plan cost associated with the first suggested care plan elements using the KPI model, and to present the first predicted KPI value and first care plan cost.

Abstract: In certain embodiments, graphical representations of factors for risk adjustment of a key performance indicator may be presented, and a user selection of a factor subset may be received. Training information may be provided as input to a machine learning model to predict values of the key performance indicator for the selected factor subset. The training information may indicate values of the factor subset associated with a provider. Reference feedback may then be provided to the machine learning model, the reference feedback comprising historic values of the key performance indicator for the provider based on the values of the factor subset that are associated with the provider. The machine learning model may then update portions of the machine learning model based on the reference feedback. The values of the factor subset may then be provided to the updated machine learning model to obtain predicted values of the key performance indicator.

Abstract: The present disclosure pertains to a system configured to prepare and use prediction models for socioeconomic data and missing value prediction. Some embodiments may: extract, from received population segment data, a training set of socioeconomic parameter values for each population segment; provide, to a prediction model as input, first parameter values of the respective training set for the prediction of additional parameter values of the training set such that the prediction of the additional parameter values is performed without reliance on the additional parameter values; provide, for each of the training sets, the additional parameter values to the prediction model as reference feedback for the prediction model's prediction of the additional parameter values to train the prediction model; and predict, based on a working set of parameter values for a population segment, additional values for the working set using the prediction model subsequent to its training.

Abstract: A patient selection tool for selecting patients for a social-behavioral determinants of health (SBDoH) program, including: a graphical user interface (GUI) module configured to present a GUI to a user, receive inputs from the user including a SBDoH factor, and to select patient cohort data based upon the inputs received from the user, a machine-learning model configured to predict a key performance indicator (KPI) for each patient based upon the patient cohort data and the SBDoH factor, a success rate module configured to predict the probability of success of the SBDoH program for each patient in the patient cohort; a return on investment (ROI) module configured to determine the cost savings associated with the SBDoH program for each patient in the patient cohort based upon the cost associated with the KPI, the probability of success of the SBDoH program, and a change in the KPI associated with the SBDoH factor, and a patient selection module configured to select patients for the SBDoH program based upon the

Abstract: The present disclosure pertains to a system for providing model-based predictions of patient-related metrics based on location-based determinants of health. In some embodiments, the system (i) obtains (a) one or more patient-related features and (b) one or more location-related features associated with an individual; (ii) performs one or more queries based on the one or more patient-related features associated with the individual to obtain one or more location-related features associated with similar individuals; and (iii) provides the one or more location-related features associated with the similar individuals and the one or more location-related features associated with the individual to the machine learning model to predict (a) one or more metric values for the patient-related metrics associated with the individual and (b) at least one location-related feature associated with the individual likely to contribute to the one or more metric values.

Abstract: The present disclosure pertains to a system for providing model-based patient assignment to care managers. In some embodiments, the system (i) receives a collection of health information related to a plurality of individuals residing in a predetermined region and known to have similar social determinants of health; (ii) extracts and provides one or more care management-related features of the plurality of individuals and one or more care management activities provided to the individuals to a machine learning model to train the machine learning model; (iii) obtains and provides health information of an individual residing in the predetermined region to the machine learning model to predict an amount of care management time for the individual; (iv) assigns, based on the predicted amount of care management time, the individual to a care manager; and (v) effectuates presentation of a list of assigned individuals to the care manager.

Abstract: A method for KPI forecasting, comprising: (i) receiving an identification of one or more KPI to be forecast and a forecast horizon; (ii) extracting data received from a database for KPI forecasting; (iii) aggregating the extracted data; (iv) optionally removing one or more outliers from the aggregated data by identifying one or more possible outliers, presenting the outliers to a user, receiving information from the user comprising an identification of outliers, and removing the outliers; (v) fitting training data to a plurality of forecasting models; (vi) identifying a best fit forecasting model using test data; (vii) forecasting, using the best fit model, to generate KPI forecast data; (viii) evaluating the KPI forecast data for accuracy; and (ix) presenting the generated KPI forecast data to the user via a user interface.

Abstract: The present system is configured to display distributions of predicted health outcome information for patient populations in geographical areas. The system is configured to obtain demographic, social (e.g., including environmental), and prior health outcome information for a patient population in a geographical area. The system is configured to train a prediction model based on the demographic, social, and prior health outcome information, which outputs weighted features of the demographic and social information that are predictive of health outcomes for the patient population. The system is configured to cause display of a distribution of predicted health outcome information for the patient population in the geographical area based on the weighted features.