Abstract: A system and method is provided for allocating a patient to one of a plurality of COPD remote patient monitoring programs, each remote patient monitoring program being tailored to a different level of disease acuity.

Abstract: A system for detecting clinical deterioration in a patient having a health condition during one or more post-discharge patient follow-up periods is provided. The system includes a computer system that has one or more physical processors programmed with computer program instructions which, when executed cause the computer system to: determine a clinical deterioration risk detection model for each of the post-discharge patient follow-up periods, and determine a clinical deterioration risk detection value for the patient in the post-discharge patient follow-up period using the corresponding clinical deterioration risk detection model. The clinical deterioration risk detection model is a function of health information of the patient in the corresponding post-discharge patient follow-up period. The clinical deterioration risk detection value is configured to predict the likelihood of the clinical deterioration in the patient in the post-discharge patient follow-up period.

Abstract: The present disclosure relates to a method and non-transitory machine-readable storage medium encoded with instructions for using prediction models for predicting values, the medium comprising instructions for receiving a plurality of entry identifiers, instructions for receiving a value for each of the plurality of entry identifiers, instructions for determining whether the value for each of the plurality of entry identifiers has changed and a magnitude of the change, instructions for building a model for predicting a time-to-value change, instructions for building a model for predicting a future magnitude of change, instructions for performing a simulation using the model for predicting the time-to-value change and the model for predicting the future magnitude of change and instructions for outputting a confidence interval.

Abstract: The method of reproducing a human perceptual signal comprises the steps of selecting (7) a human perceptual signal in dependency of a pace of a person (3), adapting (8) the human perceptual signal in dependency of a physical state of the person (3), and reproducing (9) the human perceptual signal. An adaptation of the human perceptual signal is limited to a certain maximum. The electronic device of the invention comprises electronic circuitry operative to perform the method of the invention. The software of the invention enables a programmable device to perform the method of the invention.

Abstract: Various embodiments relate to a method for managing healthcare resources including receiving information selecting a first outcome perspective, calculating first impactibility scores for the first outcome perspective, determining a first subarea based on the first impactibility scores, and designating an allocation of healthcare resources and cost for the first subarea based on the first outcome perspective. The first impactibility scores are calculated for respective subareas including the first subarea, and the first outcome perspective corresponds to a first ratio of healthcare resources and cost.

Abstract: A method for managing healthcare costs includes generating a first set of event logs based on first information, extracting traces from the first set of event logs for different episodes of care, generating a second set of event logs based on second information, determining a plurality of clusters based on the second set of event logs, grouping the traces to form sets of traces linked to the plurality of clusters, and inputting the sets of traces into a process mining module.

Abstract: According to an aspect, there is provided a computer implemented method of displaying to a user a risk score associated with a risk of a patient requiring a medical intervention. The method comprises obtaining the risk score for the patient; determining a format in which to display the risk score to the user based on a numerical literacy of the user; and sending an instruction to a user display to instruct the user display to display the risk score to the user in the determined format.

Abstract: The present disclosure pertains to a system for providing model-based predictions of beneficiaries receiving out-of-network care. In some embodiments, the system (i) obtains, from one or more databases, a collection of information related to care utilization and expenditures for a plurality of beneficiaries; (ii) extracts, from the collection of information, information related to healthcare services rendered to the beneficiaries within a predetermined time period; (iii) provides the extracted healthcare services information to a machine learning model to train the machine learning model; (iv) obtains characteristics information related to a current beneficiary and a corresponding healthcare provider; and (v) provides, subsequent to the training of the machine learning model, the current patient and corresponding healthcare provider characteristics information to the machine learning model to predict a likelihood of a future healthcare service provided to the current beneficiary to be rendered out-of-network.

Abstract: The method of reproducing a human perceptual signal comprises the steps of selecting (7) a human perceptual signal in dependency of a pace of a person (3), adapting (8) the human perceptual signal in dependency of a physical state of the person (3), and reproducing (9) the human perceptual signal. An adaptation of the human perceptual signal is limited to a certain maximum. The electronic device of the invention comprises electronic circuitry operative to perform the method of the invention. The software of the invention enables a programmable device to perform the method of the invention.

Abstract: In a Personal Emergency Response System (PERS) service, activation of a wearable call device by a PERS client causes a speakerphone console to connect with a PERS call center to enable the PERS client to converse with a call center operator while a PERS client profile is retrieved from a PERS database and displayed on a display component at the PERS call center. From the profile, values of a set of features are generated for the PERS client. An emergency dispatch risk prediction or other risk prediction is computed over a future time horizon for the PERS client based on the generated values of the set of features, and is displayed on the display component. The risk prediction may be wirelessly sent to a mobile device for display on the mobile device.

Abstract: Example embodiments include a method for managing healthcare services including receiving information selecting an episode of care, identifying a plurality of care pathways for the episode of care, generating a plurality of clusters corresponding to the plurality of care pathways, respectively, and performing a clustering analysis to determine a first care pathway of the plurality of care pathways, and outputting information indicative of the first care pathway. Each of the care pathways corresponds to services from a combination of different healthcare personnel with associated costs, and the first care pathway corresponds to one of the plurality of clusters satisfying a selected outcome measure for a bundled payment reimbursement contract.

Abstract: The method of reproducing a human perceptual signal comprises the steps of selecting (7) a human perceptual signal in dependency of a pace of a person (3), adapting (8) the human perceptual signal in dependency of a physical state of the person (3), and reproducing (9) the human perceptual signal. An adaptation of the human perceptual signal is limited to a certain maximum. The electronic device of the invention comprises electronic circuitry operative to perform the method of the invention. The software of the invention enables a programmable device to perform the method of the invention.

Abstract: The present disclosure pertains to a system for facilitating configuration modifications for a patient interface computer system based on a patient-specific risk alert model. In some embodiments, the system obtains (i) lifestyle information associated with a patient, (ii) disease information associated with the patient, and (iii) one or more physiological measurements of the patient. The system monitors the patient for one or more threshold levels of exacerbation based on a risk alert model, the lifestyle information, the disease information, and the one or more physiological measurements. The system causes a configuration of the patient interface computer system to be modified based on the monitoring for the one or more threshold levels of exacerbation.

Abstract: A threshold value or range of values for results of a test used to assign patients to a particular level of treatment for a clinical condition is determined based on historical information on a plurality of patients having the clinical condition. The historical information may include values for the test performed on the patients, information on the treatment level for the clinical condition provided to the patients, information on the outcome of the clinical condition for the patients, and information on the cost associated with providing each of the treatment levels to the patients. The threshold value or range of values for results of the test is determined from the historical information, with the threshold value or range of values indicating the most cost effective treatment level for a given test result.

Abstract: In some embodiments, a patient dataset including digital medical images and other patient data may be obtained. The other patient data may include specific patient health data associated with a patient and historical patient data derived from a population related to the patient. The historical patient data may indicate medical inventions provided to patients of the related population, health effects of the medical interventions, and costs of the medical interventions. In some embodiments, a neural network specific to the patient may be configured for a user application using at least part of the patient dataset. As an example, the user application may include neural network. Based on the specific patient health data, health effects and intervention costs related to individual interventions for the patient may be predict via the neural network of the user application.

Abstract: The present system is configured to generate an ensemble prediction model to provide a care services combination for a user. The ensemble prediction model is configured to predict the effectiveness of individual care services for users. The ensemble prediction model accounts for effects of feature combinations on outcomes for the users. The present system is configured such that output from the ensemble prediction model is used during a single agent search to determine optimal combinations of services that minimize the risk of emergency re-hospitalization and/or other negative patient outcomes.

Abstract: There is provided a computer-implemented method and apparatus for determining a likelihood of occurrence of a medical event for a subject. A risk profile for the subject is acquired and a plurality of risk profiles for other subjects are obtained from a database. The acquired subject risk profile is compared to the obtained plurality of other subject risk profiles. At least one risk profile is selected from the obtained plurality of other subject risk profiles that most closely matches the acquired subject risk profile. The likelihood of occurrence of a medical event for the subject is determined based on the selected at least one risk profile. A signal indicative of the determined likelihood of occurrence of the medical event for the subject is output.

Abstract: A system, method and tangible computer readable storage medium for storing a computer-implemented guideline, wherein the computer-implemented guideline includes a plurality of device independent nodes, determining a current state of the computer-implemented guideline, wherein the current state relates to one of the device independent nodes, retrieving device features relating to a device on which the one of the device independent nodes of the computer-implemented guideline is to be performed, modifying the one of the device independent nodes based on the device features to generate a device specific node and instantiating the device specific node on the device.

Abstract: In a Personal Emergency Response System (PERS) service, activation of a wearable call device by a PERS client causes a speakerphone console to connect with a PERS call center to enable the PERS client to converse with a call center operator while a PERS client profile is retrieved from a PERS database and displayed on a display component at the PERS call center. From the profile, values of a set of features are generated for the PERS client. An emergency dispatch risk prediction or other risk prediction is computed over a future time horizon for the PERS client based on the generated values of the set of features, and is displayed on the display component. The risk prediction may be wirelessly sent to a mobile device for display on the mobile device.

Abstract: A method of generating an algorithm for determining a condition of a patient. The algorithm is generated based on medical characteristics that are shared by a group of data sources. The group of data sources is obtained by clustering a plurality of data sources into groups based on the medical characteristics associated with each data source.