Abstract: A risk-adjusted assessment of a target facility's quality measures (e.g. mortality rate, length of stay, readmission rate, complications rate, etc.) is determined with respect to the quality measures of a broader population base. Patient cohorts are identified corresponding to particular ailments or treatments, and the target facility's risk-adjusted quality measures are determined for each cohort. When a particular quality measure for a target cohort indicates poor performance, factors that are determined to be relevant to the patients' outcomes are identified and used to create a control group of patients in the broader population who exhibit similar factors but had better outcomes than the patients of the target cohort. The care process (treatments, medications, interventions, etc.) that each of the target patients received is compared to the care process that each of the control patients received, to identify potential root-causes of the poorer performance.

Abstract: A non-transitory computer readable medium (26) stores a schedule (32) of patient appointments; and instructions executable by at least one electronic processor to perform a patient appointment notification method (100) including, for a patient having a patient appointment on the schedule: providing a messaging system via which patient appointment notifications are pushed to the patient; converting data related to the patient and the patient appointment into features; analyzing the features to determine a score (40) indicative of likelihood of the patient no-showing the appointment; adjusting, based on the score, at least one of (i) times at which the messaging system pushes patient appointment notifications (50) to the patient and/or (ii) content of the patient appointment notifications pushed to the patient and/or (iii) an interactivity of the patient appointment notifications pushed to the patient.

Abstract: A non-transitory computer readable medium (26) stores instructions readable and executable by at least one electronic processor (20) to perform medical procedure billing rate optimization method (100).

Abstract: An apparatus (10) for generating benchmarking metrics of current operational workflow performance of a hospital department includes at least one electronic processor (20) programmed to: generate a department profile (34) identifying resources of the hospital department including at least an active medical equipment inventory and a personnel profile; retrieve current statistics for the hospital department including at least one of patient arrival timeliness, patient no-show, and emergency department (ED) arrival statistics; compute values of one or more key performance indicator (KPI) metrics (40) for the current statistics; generate an executable workflow model (44) for workflow processes of the hospital department including temporal aspects of the workflow processes, the workflow model having variables representing at least patient arrival timeliness, patient no-show, and ED arrival; simulate a best case scenario (50) by executing the workflow model on inputs including the department profile and best case va

Abstract: Abstract: A non-transitory computer-readable medium stores instructions readable and executable by at least one electronic processor (20) to perform a workflow schedule monitoring method (100). The method includes: simulating (42) a workflow schedule (46) using data including workflow timestamps and a planned schedule; detecting (44) non-compliance of the workflow schedule with constraint data (52); in response to the detection of non-compliance, determining one or more workflow schedule adjustment options (48) for adjusting the workflow schedule to comply with the constraint data; and controlling a display device (24) of the workstation to display the workflow schedule and the one or more workflow schedule adjustment options.

Abstract: A non-transitory computer-readable medium stores instructions readable and executable by a mobile device (32) with a display (34) and including at least one electronic processor (35) to perform a patient appointment timeline tracking method (100, 200). The method includes: receiving, via a wireless communication path (36), information at the mobile device about a patient appointment at a medical facility including estimated time information for events in the patient appointment; controlling the display to display a timeline (40) of the events in the patient appointment, the events being displayed as icons (42), the displayed time-line including the time information for the events; detecting, via one or more user inputs, a selection of one of the icons; and in response to detecting the user inputs, controlling the display to present information related to the selected icon.

Abstract: A healthcare intervention assessment apparatus (10) includes at least one processor (16, 18, 20, 22, 24) programmed to: retrieve data associated with healthcare services provided to patients; build a plurality of utilization vectors representing patients, each utilization vector corresponding to a patient, each utilization vector having vector dimensions representing different types of healthcare services, and each utilization vector being annotated with patient attributes of the patient represented by the utilization vector; scale values of the dimensions of the utilization vectors using scaling factors for a chosen analysis type; perform an analysis of the chosen analysis type on the scaled utilization vectors to determine at least one of a dominant scaled utilization vector, at least one outlier, or at least one range of the scaled values of the dimensions of the utilization vectors; and a display (26) to display a quantitative result of the analysis.

Abstract: Data analysis of altered data includes analyzing (64) a test data set (14) with a data analysis technique using one or more configured processors (30) which create one or more analytical measures, and the test data set selected from an altered data set (12) according to a confidence score. At least one reliability measure of the one or more analytical measure is calculated using the configured one or more processors based on similarity of the one or more analytical measures and same analytic measures created from the data analysis technique applied to one or more reliability test data sets (16, 18) selected from the altered data set according to different confidence scores.

Abstract: A risk-adjusted assessment of a target facility's quality measures (e.g. mortality rate, length of stay, readmission rate, complications rate, etc.) is determined with respect to the quality measures of a broader population base. Patient cohorts are identified corresponding to particular ailments or treatments, and the target facility's risk-adjusted quality measures are determined for each cohort. When a particular quality measure for a target cohort indicates poor performance, factors that are determined to be relevant to the patients' outcomes are identified and used to create a control group of patients in the broader population who exhibit similar factors but had better outcomes than the patients of the target cohort. The care process (treatments, medications, interventions, etc.) that each of the target patients received is compared to the care process that each of the control patients received, to identify potential root-causes of the poorer performance.

Abstract: A medical system includes a modeling unit (10) which generates a plurality of tree structured classifiers based on a collection of demographic, socio-econometric, diagnoses, procedure, hospital, and logistical data elements, learns patient discharge risk factors based on the plurality of tree structured classifiers and data corresponding to prior patient discharges, and creates a predictive model of readmission based on the learned patient discharge risk factors which scores the identified patient discharge risk factors for one or more patient discharges.