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 patient appointment notification method includes: accessing a scheduler of a medical facility to identify patients scheduled for medical appointments; retrieving patient data for the patients from one or more databases; generating a risk score for one or more patients based on at least the retrieved patient data, the risk score being indicative of a likelihood that the one or more patients will no show or late cancel the medical appointment; and for the one or more patients having a risk score exceeding a predetermined risk score threshold, generating a notification based at least on the risk score exceeding a predetermined risk score threshold and outputting the notification to a device.

Abstract: When a patient is referred to a physician or hospital for a multi-encounter medical procedure, a computer retrieves information from a patient's electronic medical record (EMR) stored in an electronic medical record system of a medical facility. If the EMR suggests that the patient is possibly at risk, a computer poses a questionnaire to the patient, the questionnaire being specifically diagnostic for the suggested risk condition and/or appropriateness of care preferences of the patient. A computer evaluates the EMR information and questionnaire answers together to evaluate risk characteristics of the patient. Based on the evaluating, the computer recommends at least one pathway to be implemented by the medical staff in the patient's care.

Abstract: Techniques for near-term data filtering, smoothing and forecasting are described herein. In one example, data is received from supervisory control and data acquisition (SCADA) measurements available in an electrical grid. The data may be filtered according to a two-stage Kalman filter, which may include a ramp rate filter test and a load level filter test. The filtered data may then be smoothed according to an augmented Savitzky-Golay filter. Within the filter, a lift multiplier may correct for bias, which may have been introduced by load changes (e.g., an early morning increase in load). In one example, the lift multiplier may be calculated as a ratio between a smoothed load from a centered Savitzky-Golay moving average and a right hand side constrained Savitzky-Golay moving average. The filtered and smoothed data may be used in forming near-term forecast(s), which may be performed by autoregressive model(s).

Abstract: Techniques for near-term data filtering, smoothing and forecasting are described herein. In one example, data is received from supervisory control and data acquisition (SCADA) measurements available in an electrical grid. The data may be filtered according to a two-stage Kalman filter, which may include a ramp rate filter test and a load level filter test. The filtered data may then be smoothed according to an augmented Savitzky-Golay filter. Within the filter, a lift multiplier may correct for bias, which may have been introduced by load changes (e.g., an early morning increase in load). In one example, the lift multiplier may be calculated as a ratio between a smoothed load from a centered Savitzky-Golay moving average and a right hand side constrained Savitzky-Golay moving average. The filtered and smoothed data may be used in forming near-term forecast(s), which may be performed by autoregressive model(s).

Abstract: Techniques for near-term data filtering, smoothing and forecasting are described herein. In one example, data is received from supervisory control and data acquisition (SCADA) measurements available in an electrical grid. The data may be filtered according to a two-stage Kalman filter, which may include a ramp rate filter test and a load level filter test. The filtered data may then be smoothed according to an augmented Savitzky-Golay filter. Within the filter, a lift multiplier may correct for bias, which may have been introduced by load changes (e.g., an early morning increase in load). In one example, the lift multiplier may be calculated as a ratio between a smoothed load from a centered Savitzky-Golay moving average and a right hand side constrained Savitzky-Golay moving average. The filtered and smoothed data may be used in forming near-term forecast(s), which may performed by autoregressive model(s).

Abstract: Techniques for forming and using a unified framework for electrical load forecasting are described herein. In one example, data is received from supervisory control and data acquisition (SCADA) measurements available in an electrical grid. Data may be validated using ramp rate filter and load level filter tests. An augmented Savistky-Golay filter may be used to smooth the data, which may then be passed to autoregressive near-term forecast model(s). Forecasts are generated using near-term, ramp rate and short-term models. The near-term and ramp rate forecasts may be combined, such as with forecast horizon and ramp rate horizon parameters, to create a stitched forecast. The stitched forecast and short-term forecast are then blended using a blending period and blending weights that may be supplied exogenously. The final blended or unified near-term and short-term forecast may be exported to downstream generation scheduling and dispatching applications, which may calculate electrical production.