Abstract: A method of assessment of a joint may include receiving image data related to one or more images of the joint; determining a B-score, osteophyte volume, and/or a joint-space width based on the image data; generating a first artificial model of the joint based on the determined score, osteophyte volume, and/or joint-space width; and displaying on an electronic display a graphical user interface (GUI). The GUI may include a display of the first artificial model of the joint.

Abstract: Aspects disclosed herein may provide a method for determining a duration of a medical procedure. The method may include receiving imaging data including at least one image acquired of a patient's anatomy, determining at least one parameter of the patient's anatomy based on the imaging data, predicting a duration for the medical procedure based on the determined at least one parameter, and outputting the predicted duration on an electronic display. The at least one parameter may include at least one of a B-score, a joint-space width, an osteophyte position or volume, an alignment, or a deformity based on the imaging data.

Abstract: A method for generating an electronic display of guidance for treating a target patient with a disease or disorder may comprise receiving, by one or more processors, a plurality of prior data sets, including: i) at least one data set specific to the target patient from the patient, ii) at least one data set specific to the patient from a health care service provider, and iii) at least one data set specific to a patient population with at least one common attribute with the target patient from a health care service provider; executing an algorithm, stored in a non-transitory computer-readable storage medium, to identify a pattern across the plurality of prior data sets, the pattern describing a probability of success for each of a plurality of potential treatment options for the target patient; automatically generating, by the one or more processors, a treatment pathway recommendation for the target patient.

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: 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: Disclosed herein is a balancer algorithm to perform joint balancing calculations to identify target solutions based on surgeon preference. The balancer algorithm can generate a suggested final implant plan from a predetermined range. The balancer algorithm can be used in a knee arthroplasty procedure to generate bone resection depths, bone gaps, implant angulations, required soft tissue release, etc. Input to the balancer algorithm can include preoperative data, intraoperative data, and surgeon preference data.

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: 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: According to an aspect there is provided a computer-implemented method of monitoring a person, the method comprising (i) receiving medication schedule information regarding a person, the medication schedule information indicating the timing of medication doses for treating or suppressing one or more symptoms of a medical condition; (ii) setting a first monitoring interval for a first monitoring event for the person based on the received medication schedule, wherein the first monitoring interval starts before a first medication dose is scheduled to be taken in which a first monitoring event can be initiated; (iii) initiating a first monitoring event for the person in the first monitoring interval and monitoring the person for one or more characteristics associated with the one or more symptoms of the medical condition during the first monitoring event; (iv) setting a second monitoring interval for a second monitoring event for the person, wherein the second monitoring interval starts before a second medication

Abstract: The present disclosure pertains to a system for providing prediction models for predicting a health determinant category contribution in savings generated by a clinical program. In some embodiments, the system obtains healthcare data including (i) historical and financial data corresponding to one or more clinical programs, (ii) demographic, clinical, and behavioral data of one or more patients, and (iii) environmental factors associated with the one or more clinical programs; defines one or more health determinant categories; generates prediction models related to a contribution of one or more constituents of the one or more health determinant categories to savings generated by the one or more clinical programs; generates one or more predictions related to a contribution of the one or more health determinant categories to the savings generated by the one or more clinical programs; and effectuates, via a user interface, presentation of the one or more predictions.

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: 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 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: There is provided a computer-implemented method (100) for generating a patient care plan. The method comprises acquiring (102) a plurality of single-condition care plans for a patient suffering from multiple medical conditions; enriching (104) at least one of the single-condition care plans; combining (106) the plurality of single-condition care plans into a multiple-condition care plan; and evaluating (108) a quality of the multiple-condition care plan relative to the plurality of single-condition care plans. A computer program product, an apparatus and a system are also disclosed.

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.