Abstract: Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients.

Abstract: An optimization methodology is employed to match vibratory inhaler devices having certain characteristics to the particular anatomical and acoustic properties of a patient's vocal tract, in order to achieve the most effective dispersion of a dry powder medicament using inspiratory effort of a user of the inhaler. In embodiments, optimization involves employing one or more measurements of acoustic frequency spectrum properties as well as one or more anatomical/geometric measurements of the structures comprising the particular patient's mouth, pharynx, and upper respiratory tract and matching a vibratory inhalation device that corresponds thereto.

Abstract: A technology is provided for predicting congestion or crowding of services over a future time interval, and may be utilized for forecasting congestion in a hospital emergency department. One embodiment of this technology comprises a decision support tool for resources management to prevent overcrowding and long waiting times, or for mitigating ED congestion by, for example, warning hospital managers that a significant likelihood exists of ED congestion over a future time frame, or automatically initiating mitigative actions. A time series of consecutive ED arrivals timestamps is processed to determine a presence (or absence) of positive autocorrelation or self-similarity and estimate Hurst exponent values to generate a forecast model. The forecast model is utilized to determine future ED demand.

Abstract: Decision support technology is provided for use with patients who may experience respiratory deterioration. A mechanism is provided to determine whether a patient is experiencing (or is likely to experience at a future time) an autoimmune inflammatory event, which may include performing a spectral analysis on a time series of nocturnal or axillary temperatures for the patient to determine a spectrum slope or intercept. The time series may be demeaned, detrended, and apodized before performing the spectral analysis. A comparison of the slope or intercept with a baseline value is used to determine an indication of the patient's likely condition or future condition regarding the autoimmune inflammatory event. Based on the comparison, an intervening action may be invoked, such as alerting a caregiver, providing a recommendation or modified treatment, or determining and recommending a tailored prescription of medicine for the patient.

Abstract: A tool for predicting that a person is likely to be abnormally hydrated over a future time interval, and in some cases to alert the person or a medical professional to intervene. From a series of physiological measurements, a tissue electrical impedance spectrum curve, which comprises a phase spectrum curve in an embodiment, is determined and changes in the shape of the curve are ascertained. The measurements may be received using one or more sensors worn by the person. In some embodiments, current and historic spectrum curvature are applied to an evolutionary algorithm, such as particle-swarm optimization (PSO) or differential evolution (DE), to determine an inference regarding the persons future hydration status. In one embodiment, a statistical forecast for the next epoch immediately beyond the present one, is determined.

Abstract: Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients.

Abstract: Systems, methods and computer-readable media are provided for determining the modality for transferring, lifting, or repositioning (TLR) a human patient in a health care setting contexts. In some cases, a model-based recursive partitioning and Bradley-Terry regression is applied, which may be optionally parallelized so as to determine statistical associations with various factors, such as caregiver attributes, care venue, and patient attributes. One embodiment determines a Bradley-Terry regression model from the recursive partitioning which may be incorporated into a TLR selection decision-support tool or otherwise utilized to identify the optimal modality.

Abstract: Technologies are provided for identifying individuals having a risk of non-adherence to or from a prescribed treatment program; for predicting and the risk, which may be determined as a forecast over a future time span; and evaluating it to further determine or invoke specific actions to mitigate the risk or otherwise improve likelihood of compliance. A singular spectrum analysis (SSA) is utilized to analyze temporal properties of a time series determined from measured or observational data to determine an emergent pattern. Based on this pattern, a risk of non-adherence, including relapse or absconding, over a future time interval by the individual may be determined and utilized to implement an intervening action.

Abstract: Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients.

Abstract: An improved decision support tool is provided for detecting, diagnosing, or treating human patient based on detected changes or trends in microbiota-related activity of the patient. The decision support tool determine a longitudinal pattern of relative abundances or diversity levels of microbiota, and subsequently determine the occurrence of alternations or trends, which may indicate or be related to meaningful clinical events, such as a change in condition for the patient. In one embodiment, a joint determination of statistical significance of change and trend is first detected and then utilized to determine an occurrence of clinically meaningful microbiota activity in a patient.

Abstract: Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients.

Abstract: Systems, methods and computer-readable media are provided for determining and mitigating the aggregate loss risk associated with hospitalization for epidemic or pandemic influenza for health insurers, reinsurers, provider organizations, or public policy-makers. An accurate prediction of this risk may be provided, which may be used to determine parameters for reinsurance underwriting or for issuance and trading of catastrophe bonds (“cat bonds”) or other insurance-linked securities (ILS) and derivatives to lay off substantial amounts of such risk to capital markets investors. In particular, one embodiment uses a novel log-expit transformation of the raw data and non-parametric gradient-boosting machine-learning modeling in order to determine a high-claim right-tail risk. Some embodiments further comprise securitizing epidemic or pandemic influenza acute care health services catastrophe risk.

Abstract: Systems, methods and computer-readable media are provided for determining the modality for transferring, lifting, or repositioning (TLR) a human patient in a health care setting contexts. In some cases, a model-based recursive partitioning and Bradley-Terry regression is applied, which may be optionally parallelized so as to determine statistical associations with various factors, such as caregiver attributes, care venue, and patient attributes. One embodiment determines a Bradley-Terry regression model from the recursive partitioning which may be incorporated into a TLR selection decision-support tool or otherwise utilized to identify the optimal modality.

Abstract: Technologies are provided for identifying individuals having a risk of non-adherence to or from a prescribed treatment program; for predicting and the risk, which may be determined as a forecast over a future time span; and evaluating it to further determine or invoke specific actions to mitigate the risk or otherwise improve likelihood of compliance. A singular spectrum analysis (SSA) is utilized to analyze temporal properties of a time series determined from measured or observational data to determine an emergent pattern. Based on this pattern, a risk of non-adherence, including relapse or absconding, over a future time interval by the individual may be determined and utilized to implement an intervening action.

Abstract: A catastrophe-theoretic approach is provided for predicting an occurrence of an acute inflammatory condition or event (e.g., SIRS or sepsis) for a human patient based on a time series of monitored vital signs values measured from a patient, and in some instances, for providing advanced notice to clinicians or caregivers when such an acute inflammatory event is forecasted or modifying treatment for the patient, according to the predicted likelihood. In particular, an acute inflammatory condition management system is provided for determining a likelihood of near-term future significant acute inflammation in human patients. Embodiments of the disclosure described herein may provide a forecasted risk for future significant acute inflammation within a time horizon comprising a future time interval. In one embodiment, the future time interval is from 30 min to approximately 8 hours into the future, and may be dependent on the frequency of vital signs measurements.

Abstract: Methods are provided for validating theoretical improvements in the decision-support processes facilitating surveillance and monitoring of a patient's risk for developing a particular disease or condition and detecting the disease or condition. Patient information is received from a source and populated into an active risk assessment that monitors the patient's risk for developing Sepsis. At least a first and second set of actionable criteria for determining a patient's risk for developing sepsis are received. For each set of actionable criteria, it is determined that actionable criteria have been met. In some embodiments, software agents, operating in a multi-agent computing platform, perform each determination of whether actionable criteria are met.

Abstract: Systems, methods and computer-readable media are provided for facilitating patient health care through automatic discovery, establishment, and statistical validation of diagnostic-signals in repositories of EMR information, identifying phenotypic diagnostic signals regarding markers or combinations or sequences of patient attributes.

Abstract: Systems, methods and computer-readable media are provided for identifying persons who are likely to benefit from a neurosurgical procedure, such as lobectomy, hemispherectomy, lesionectomy, callosotomy, and the like. Measured values of physiological variables may be combined via a multi-variable predictive model. In some embodiments, this may take the form of a multinomial logistic regression classifier. In other embodiments, the evidence-combining may be implemented via a neural network or support vector machine or similar machine learning or classification methods. In an embodiment, a leading indicator of near-term responsiveness to the regimen may be provided to a caregiver, such as a neurologist, and further may be integrated with case-management software and an electronic health record decision-support system.

Abstract: Methods, systems, and computer-readable media are provided for facilitating record matching and entity resolution and for enabling improvements in record linkage. A power-spectrum-based temporal pattern-specific weight may be incorporated into record linkage methods to enhance the record linkage accuracy and statistical performance. For example, in embodiments, a value-specific weight may be calculated from a population-based frequency of field-specific values and provides an opportunity to capture and measure the relative importance of specific values found in a field. A timeseries-derived Bayesian power spectrum weight may be calculated from the population-based frequency of temporal pattern-specific values in terms of intensities at various frequencies of the power spectrum computed from the timeseries, and further provides an opportunity to capture and measure the relative importance of specific sequences of care episodes.

Abstract: Systems and methods are provided for evaluating an alarm condition for a monitored patient in a population of one or more patients. One or more physiological parameters pertaining to the patient, or physiological variables, are used to form a time series describing the patient status. A quantile threshold is determined for a patient. A monitor is initialized and begins generating a binary raw alarm signal. The binary signal is filtered with a low pass filter, and the filtered data is subjected to a quantile operation to determine if a particular sample exceeds the determined quantile threshold. If the quantile threshold is exceeded, then a check is performed to see if the raw binary alarm signal also indicates an alarm. If both the preliminary alarm indication and the raw binary signal indicate an alarm, then an alarm is emitted; otherwise monitoring continues.