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 intelligent data retrieval, caching, and display. In some embodiments, the system generates a (GUI) in response to a request from a user device. The system may receive the location of the user device and identification of an initiative from the user device. The system may identify a metric correlated with the initiative based on an analysis of previously received initiatives. The system may identify and retrieve a value of the metric from a data source storing a plurality of metrics. The system may store the metric value at a local data store closer in proximity to the user device than the plurality of metrics at the data source. The system may then generate an updated graphical user interface (GUI) with the initiative, metric, and metric value.

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: Disclosed herein are system, method, and computer program product embodiments for intelligent data retrieval, caching, and display. In some embodiments, the system generates a (GUI) in response to a request from a user device. The system may receive the location of the user device and identification of an initiative from the user device. The system may identify a metric correlated with the initiative based on an analysis of previously received initiatives. The system may identify and retrieve a value of the metric from a data source storing a plurality of metrics. The system may store the metric value at a local data store closer in proximity to the user device than the plurality of metrics at the data source. The system may then generate an updated graphical user interface (GUI) with the initiative, metric, and metric value.