Abstract: Disclosed herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for detecting and displaying healthcare outlier data. Some embodiments involve: receiving a claim including a provider, an illness, a treatment, and a risk score based on the illness and the treatment; updating a metric associated with the provider based on the claim; normalizing the metric using the risk score, the illness, and the treatment; determining that the normalized metric is an outlier by: (1) comparing the normalized metric to a threshold and; (2) determining that the normalized metric is associated with a statistically significant number of claims; in response to determining that the normalized metric is an outlier, presenting an indication of the outlier to a user. In some embodiments, the presenting may further include transmitting an alert to user computing device associated with the user.

Abstract: Embodiments relate to training and applying a parallel-structured neural network. In some embodiments, a dataset of a plurality of claims is received, where each claim of the plurality of claims includes multiple fields. Features, including numerical features and a plurality of categorical features, are extracted from the plurality of claims and based on the multiple fields. The parallel-structured neural network is trained by (1) selecting, based on a feature selection algorithm and a loss function, one or more numerical features and one or more categorical features, and (2) generating, based on the selected features, a categorical layer, a fully-connected layer, and a concatenation layer of the parallel-structured neural network. A claim is received including numerical data and categorical data, and a probability prediction is generated by inputting the claim into the parallel-structured neural network. Finally, a submission including the first claim is generated based on the probability prediction.

Abstract: Disclosed herein are system, method, and computer program product embodiments for creating and utilizing machine learning models to generate a provider performance index. In some embodiments, a first and second target may be selected. The first and second targets may respectfully include target values. First and second adjusted target values may be determined by combining the first and second target values with first and second external weights. The external weights may be generated by respective first and second machine learning models. The machine learning models may correspond to the respective targets. First and second error values may be determined based on differences between respective target and adjusted target values. The error values may be normalized and combined to generate an index value.

Abstract: Disclosed herein are system, apparatus, device, method and/or computer program product embodiments, and/or combinations and sub-combinations thereof, for detecting and displaying healthcare outlier data. Some embodiments involve: receiving a claim including a provider, an illness, a treatment, and a risk score based on the illness and the treatment; updating a metric associated with the provider based on the claim; normalizing the metric using the risk score, the illness, and the treatment; determining that the normalized metric is an outlier by: (1) comparing the normalized metric to a threshold and; (2) determining that the normalized metric is associated with a statistically significant number of claims; in response to determining that the normalized metric is an outlier, presenting an indication of the outlier to a user. In some embodiments, the presenting may further include transmitting an alert to user computing device associated with the user.

Abstract: A test buck, a test buck system and related method are disclosed. The test buck system may comprise a controller and a test buck. The test buck may include a portable bed, a seat module mounted to the bed, and a first OIM disposed on the bed. At least a portion of the first OIM may be selectively moveable in at least four degrees of freedom, up to six degrees of freedom. The controller may be electrically connected to a first Operator Interface Module (OIM). The controller may be configured to selectively move at least a portion of the first OIM in at least four to six degrees of freedom.

Abstract: A test buck, a test buck system and related method are disclosed. The test buck system may comprise a controller and a test buck. The test buck may include a portable bed, a seat module mounted to the bed, and a first OIM disposed on the bed. At least a portion of the first OIM may be selectively moveable in at least four degrees of freedom, up to six degrees of freedom. The controller may be electrically connected to a first Operator Interface Module (OIM). The controller may be configured to selectively move at least a portion of the first OIM in at least four to six degrees of freedom.

Abstract: A test buck, a test buck system and related method are disclosed. The test buck system may comprise a controller and a test buck. The test buck may include a portable bed, a seat module mounted to the bed, and a first OIM disposed on the bed. At least a portion of the first OIM may be selectively moveable in at least four degrees of freedom, up to six degrees of freedom. The controller may be electrically connected to a first Operator Interface Module (OIM). The controller may be configured to selectively move at least a portion of the first OIM in at least four to six degrees of freedom.