Abstract: A method (100) for analyzing and predicting network leakage for a healthcare network using a network leakage analysis system (200), comprising: (i) receiving (120), at the network leakage analysis system, information about one or more patients; (ii) extracting (130), from the received information, one or more patient leakage features about each of the one or more patients; (iii) analyzing (150), by a trained leakage prediction model of the network leakage analysis system, the one or more patient leakage features about each of the one or more patients to generate a leakage prediction for each of the one or more patients; and (iv) providing (160), via a user interface of the network leakage analysis system, a report comprising the leakage prediction for each of the one or more patients.

Abstract: Presented are concepts for monitoring a health status of a subject. One such concept employs determining a trend in a health parameter of the subject based input data relating to a plurality of event occurrences for the subject. One or more irregularities are detected in the determined trend and health status of the subject is then determined based on the detected irregularities.

Abstract: A system and method are provided for generating a predictive model for predicting a surgery duration. The system and method use a feature selection technique to identify a set of features in training data, which set of features is predictive of the surgery duration, train a number of predictive models using the set of features as input and the surgery duration as prediction target, wherein the predictive models include at least a linear predictive model and a non-linear predictive model, and generate an ensemble model which combines at least two of the predictive models. Such an ensemble model may optimally combine linear and non-linear predictions and therefore allow linear and non-linear relationships between features and the surgery duration to be taken into account.

Abstract: A system and method are provided for generating a predictive model for predicting a postoperative bed type to be used by a patient after surgery. The predictive model is trained on features extracted from medical data and using a postoperative bed type as prediction target in the training. The predictive model is configured to output a probability on a scale 400 which corresponds to, at its lower end, a prediction of a first postoperative bed type and, at its upper end, a prediction of a second postoperative bed. A hybrid model is generated which applies a lower 410 and an upper threshold 420 to the probability scale. If the output probability of the predictive model is in between both thresholds, an expert selection of the bed type is recommended, while otherwise, the prediction of the predictive model is output. The values of the thresholds are optimized using a performance metric.

Abstract: A display having different segments that correspond to different categories of a care plan for a user may be provided. A dimension for the display segment is determined. The dimension indicates a relative importance of a care plan category that corresponds to the display segment compared to other care plan categories. A brightness is determined for the display segment based on the user's compliance with the care for the care plan category that corresponds to the display segment. The display segment is overlaid over at least a portion of a user-selected image for the display segment. A configuration of the overlay is based on the determined dimension and the determined brightness.

Abstract: Presented are concepts for monitoring a health status of a subject. One such concept employs determining a trend in a health parameter of the subject based input data relating to a plurality of event occurrences for the subject. One or more irregularities are detected in the determined trend and health status of the subject is then determined based on the detected irregularities.

Abstract: A computing system (204) includes a memory device (208) configured to store missing outcome instructions (218) and a processor (206) configured to execute the missing outcome instructions. The instructions cause the processor to: evaluate records stored on a storage device (114) of a call center (106) of a system (102), wherein the storage device is configured to store electronic records of calls from a user site (104) to the call center, and each record includes at least a situation attribute field and an outcome attribute field, identify records of the stored records which include, at least one of, a value in the situation attribute or case note text in a case note database, but no value in the outcome attribute, and impute an outcome to the outcome attribute field of each identified record using at least one of the situation attribute and the case note text to predict the missing outcome.

Abstract: A display having different segments that correspond to different categories of a care plan for a user may be provided. A dimension for the display segment is determined. The dimension indicates a relative importance of a care plan category that corresponds to the display segment compared to other care plan categories. A brightness is determined for the display segment based on the user's compliance with the care for the care plan category that corresponds to the display segment. The display segment is overlaid over at least a portion of a user-selected image for the display segment. A configuration of the overlay is based on the determined dimension and the determined brightness.

Abstract: In a transient ischemic attack (TIA) alerting device, a motion sensor (24) is configured to attach to or be worn by a subject. A wearable electronic data processing device (10) includes a speaker (14), a microphone (16), a radio transmitter (26), and an electronic processor (40). The processor is programmed to perform a TIA alerting method including: detecting a TIA-symptomatic gait type by processing subject motion data acquired by the motion sensor; responsive to detecting the TIA-symptomatic gait type, executing a TIA assessment questionnaire (70) by outputting questions using the speaker and receiving spoken responses using the microphone; determining a TIA assessment based at least on the spoken responses to the questions of the TIA assessment questionnaire; and conditional on the TIA assessment indicating a possible TIA, outputting a TIA alert radio signal using the radio transmitter.

Abstract: A non-transitory computer-readable storage medium storing a set of instructions executable by a processor, the set of instructions, when executed by the processor, causing the processor to perform operations including receiving a self-care assessment from a patient having a medical condition. The self-care assessment assesses the patient's level of ability for each of a plurality of determinants. The operations also include assigning a weight to each of the plurality of determinants. The operations also include creating an ordered list including the plurality of determinants. The determinants are ordered based on the corresponding weights. The operations also include determining a plurality of behavior-specific contributions. Each of the behavior-specific contributions corresponds to one of the determinants and to one of a plurality of self-care behaviors relevant to the medical condition.

Abstract: A healthcare system for creating a personalized care plan for a user comprises a processor and a computer-readable storage medium, wherein the computer-readable storage medium contains instructions for execution by the processor. The instructions cause the processor to perform the steps of receiving life goal information defined by a user, said life goal information indicating the user's life goals, mapping the received life goal information into care plan goals categories including two or more categories, and defining a care plan including a care plan schedule for the care plan goals categories onto which the received life goal information has been mapped, said schedule includes, per care plan goals category, one or more care plan content elements representing elements of the care plan.

Abstract: The exemplary embodiments are related to systems and methods for ordering self-care behaviors according to an exemplary embodiment described herein. One embodiment relates to a method comprising receiving a desired outcome for a patient having a condition; retrieving information relating to a plurality of self-care behaviors; generating, from the information and the desired outcome, a population-specific ordered list of the self-care behaviors; receiving a self-care behavior assessment for the patient; and generating, from the self-care behavior assessment and the population-specific ordered list, a patient-specific ordered list of the self-care behaviors.

Abstract: A system and method for evaluating a discharge planning process. The system and method perform the steps of determining whether a current set of discharge criteria matches current clinical practices, generating a matched set of discharge criteria by adapting the current set of discharge criteria to reflect the current clinical practices, generating a matching quality indicator value indicating a level of matching between the current set of discharge criteria and the current clinical practices, determining whether a user adheres to the matched set of discharge criteria, generating an adherence quality indicator value indicating a level to which the user adheres to the matched set of discharge criteria, determining whether the matched set of discharge criteria satisfies a target outcome and generating a satisfaction quality indicator value indicating a level to which the matched set of discharge criteria satisfies the target outcome.

Abstract: The exemplary embodiments are related to systems and methods for prioritizing risk models and suggesting services tailored to a patient profile according to an exemplary embodiment described herein. One embodiment relates to a method comprising retrieving risk model and parameter data from a risk database, retrieving hospital profile data from a records database, determining a recommendation value for the model and parameter data based on the hospital profile data, determining at least one recommended service for a patient based on the recommendation value, and outputting the at least one recommended service to a user.

Abstract: A method includes determining a food product price multiplier for a food product identified for purchase by an individual based on a health status for the individual and a product health value for the food product and generating an electronic signal indicative thereof. A system includes a multiplier determiner (116) that determines a food product price multiplier for a food product identified for purchase by an individual based on a health status for the individual and a product health value for the food product and generates an electronic signal indicative thereof.

Abstract: A method and computer readable storage medium for identifying context attributes and generating a context-based list of tasks for a user with respect to the identified context attributes. A device having a processor identifying context attributes and generating a context-based list of tasks for a user with respect to the identified context attributes. The device further including a memory storing at least one of clinical guidelines and patient data, wherein the processor generates a master list of tasks with respect to the at least one of the clinical guidelines and patient data.

Abstract: A system and method for patient discharge planning. The system and method include evaluating a patient record including patient data parameters of a patient, predicting a change in the patient record for all possible treatment options, generating a discharge recommendation based on at least one of the patient record and the predicted change in the patient record and displaying the discharge recommendation to a user.

Abstract: A computer-implemented method for creating a care plan for a patient, the method comprising the steps of (a) receiving an intervention goal, (b) creating a behavioral determinants list using the intervention goal, (c) assigning a ranking to each behavioral determinant in the list of behavioral determinates using patient data descriptive of the patient, (d) receiving a subset of the behavioral determinant list, wherein the subset is determined by using the ranking of each behavioral determinant in the list of behavioral determinates; and (f) creating the care plan using the subset.