Abstract: A peer network may include nodes corresponding to different clinicians. An edge may interconnect the two nodes based on the corresponding clinicians sharing at least one common attribute such as for example, treating the same patients and/or interacting with the same medical devices. A machine-learning model may be applied to identify, in the peer network, one or more peer communities of clinicians. The activity pattern of a clinician may be compared to the activity patterns of other clinicians in the same peer community to determine whether that clinician exhibits anomalous behavior. An investigative workflow may be triggered when the clinician is determined to exhibit anomalous behavior. The investigative workflow may include generating an alert, activating surveillance devices, and/or isolating medication accessed by the clinician.

Abstract: A method may include identifying a shift associated with a clinician by applying a machine learning model trained to identify, based on a series of transaction records associated with the clinician, one or more shifts associated with the clinician. The clinician may be identified as likely to engage in a hazardous behavior based at least on the shift associated with the clinician. In response to determining that the clinician as likely to engage in the hazardous behavior, activating a protective workflow. The protective workflow may be configured to prevent the clinician from engaging in the hazardous behavior as well as to collect evidence associated with the hazardous behavior. Related methods and articles of manufacture are also disclosed.

Abstract: A method may include receiving, from one or more data systems, a message. A machine learning model may be applied to the message to determine whether the message is an actionable message or a non-actionable message. In response to the message being the actionable message, the machine learning model may be applied to extract, from the message, a clinically significant data. One or more tasks may be performed based on the clinically significant data. The one or more tasks may include performing, based on the clinically significant data, a resource allocation for a clinical workflow associated with the data systems. The one or more tasks may also include detecting systematic inefficiencies and bottlenecks associated with the clinical workflow. Related methods and articles of manufacture, including computer program products, are also disclosed.

Abstract: A peer network may include nodes corresponding to different clinicians. An edge may interconnect the two nodes based on the corresponding clinicians sharing at least one common attribute such as for example, treating the same patients and/or interacting with the same medical devices. A machine-learning model may be applied to identify, in the peer network, one or more peer communities of clinicians. The activity pattern of a clinician may be compared to the activity patterns of other clinicians in the same peer community to determine whether that clinician exhibits anomalous behavior. An investigative workflow may be triggered when the clinician is determined to exhibit anomalous behavior. The investigative workflow may include generating an alert, activating surveillance devices, and/or isolating medication accessed by the clinician.

Abstract: A method may include identifying a shift associated with a clinician by applying a machine learning model trained to identify, based on a series of transaction records associated with the clinician, one or more shifts associated with the clinician. The clinician may be identified as likely to engage in a hazardous behavior based at least on the shift associated with the clinician. In response to determining that the clinician as likely to engage in the hazardous behavior, activating a protective workflow. The protective workflow may be configured to prevent the clinician from engaging in the hazardous behavior as well as to collect evidence associated with the hazardous behavior. Related methods and articles of manufacture are also disclosed.

Abstract: A method for detecting diversion may include identifying an activity pattern associated with a clinician as being an infrequent activity pattern that occurs below a threshold frequency. Whether the infrequent activity pattern corresponds to an anomalous behavior may be determined based at least on one or more data models. The infrequent activity pattern may include a series of transaction records, which may be matched to the reference transaction values included in each of the one or more data models. An investigative workflow may be triggered in response to the infrequent activity pattern being determined to correspond to the anomalous behavior. Related methods and articles of manufacture are also disclosed.

Abstract: A method may include receiving a first transaction record indicating a first interaction with a first raw quantity of a first medication and a second transaction record indicating a second interaction with a second raw quantity of a second medication. The first transaction record and the second transaction record may be normalized by generating, based on an equivalent unit, a first normalized quantity of the first medication and a second normalized quantity of the second medication. A machine learning model may be applied to the normalized first transaction record and second transaction record to detect, based on the first transaction record and the second transaction record, an anomalous behavior. An investigative workflow may be triggered in response to the machine learning model detecting the anomalous behavior. Related systems and articles of manufacture, including computer program products, are also provided.

Abstract: A peer network may include nodes corresponding to different clinicians. An edge may interconnect the two nodes based on the corresponding clinicians sharing at least one common attribute such as for example, treating the same patients and/or interacting with the same medical devices. A machine-learning model may be applied to identify, in the peer network, one or more peer communities of clinicians. The activity pattern of a clinician may be compared to the activity patterns of other clinicians in the same peer community to determine whether that clinician exhibits anomalous behavior. An investigative workflow may be triggered when the clinician is determined to exhibit anomalous behavior. The investigative workflow may include generating an alert, activating surveillance devices, and/or isolating medication accessed by the clinician.

Abstract: A peer network may include nodes corresponding to different clinicians. An edge may interconnect the two nodes based on the corresponding clinicians sharing at least one common attribute such as for example, treating the same patients and/or interacting with the same medical devices. A machine-learning model may be applied to identify, in the peer network, one or more peer communities of clinicians. The activity pattern of a clinician may be compared to the activity patterns of other clinicians in the same peer community to determine whether that clinician exhibits anomalous behavior. An investigative workflow may be triggered when the clinician is determined to exhibit anomalous behavior. The investigative workflow may include generating an alert, activating surveillance devices, and/or isolating medication accessed by the clinician.

Abstract: A method for detecting diversion may include receiving, from a dispensing cabinet including medication, data associated with a plurality of individuals accessing the dispensing cabinet to retrieve and/or return the medication. Diversion of the medication may be detected by at least applying, to at least a portion of the data received from the dispensing cabinet, a machine learning model trained to detect diversion. An identity of a first individual responsible for the diversion may be determined based on the data received from the dispensing cabinet. In response to the determination of the first individual as being responsible for the diversion, an investigative workflow may be triggered at the dispensing cabinet. Related systems and articles of manufacture, including apparatuses and computer program products, are also disclosed.

Abstract: A method for detecting diversion may include receiving, from a dispensing cabinet including medication, data associated with a plurality of individuals accessing the dispensing cabinet to retrieve and/or return the medication. Diversion of the medication may be detected by at least applying, to at least a portion of the data received from the dispensing cabinet, a machine learning model trained to detect diversion. An identity of a first individual responsible for the diversion may be determined based on the data received from the dispensing cabinet. In response to the determination of the first individual as being responsible for the diversion, an investigative workflow may be triggered at the dispensing cabinet. Related systems and articles of manufacture, including apparatuses and computer program products, are also disclosed.

Abstract: A method may include identifying a shift associated with a clinician by applying a machine learning model trained to identify, based on a series of transaction records associated with the clinician, one or more shifts associated with the clinician. The clinician may be identified as likely to engage in a hazardous behavior based at least on the shift associated with the clinician. In response to determining that the clinician as likely to engage in the hazardous behavior, activating a protective workflow. The protective workflow may be configured to prevent the clinician from engaging in the hazardous behavior as well as to collect evidence associated with the hazardous behavior. Related methods and articles of manufacture are also disclosed.

Abstract: A peer network may include nodes corresponding to different clinicians. An edge may interconnect the two nodes based on the corresponding clinicians sharing at least one common attribute such as for example, treating the same patients and/or interacting with the same medical devices. A machine-learning model may be applied to identify, in the peer network, one or more peer communities of clinicians. The activity pattern of a clinician may be compared to the activity patterns of other clinicians in the same peer community to determine whether that clinician exhibits anomalous behavior. An investigative workflow may be triggered when the clinician is determined to exhibit anomalous behavior. The investigative workflow may include generating an alert, activating surveillance devices, and/or isolating medication accessed by the clinician.

Abstract: A method for detecting diversion may include receiving, from a dispensing cabinet including medication, data associated with a plurality of individuals accessing the dispensing cabinet to retrieve and/or return the medication. Diversion of the medication may be detected by at least applying, to at least a portion of the data received from the dispensing cabinet, a machine learning model trained to detect diversion. An identity of a first individual responsible for the diversion may be determined based on the data received from the dispensing cabinet. In response to the determination of the first individual as being responsible for the diversion, an investigative workflow may be triggered at the dispensing cabinet. Related systems and articles of manufacture, including apparatuses and computer program products, are also disclosed.

Abstract: A system and method for detecting billing errors using predictive models is provided. The system includes a computer system and a billing error detection engine capable of detecting billing errors using predictive modeling techniques. The system receives and pre-processes billing information. The system then applies one or more predictive models to the information to identify billing errors. The results could be optionally sent to, and reviewed by, third party auditors, whereby their feedback could be incorporated into the results. A final report is generated by the system which indicates billing errors that require correction, thereby allowing an entity to correct such errors and prevent revenue leakage.

Abstract: Aspects of the present disclosure are directed to systems and methods that facilitate the recommendation of an item to a user. One particular aspect includes a method including the steps of: a) receiving a user request for a new recommendation including rating information for previously-selected items, b) retrieving a mean vector and a covariance matrix representing estimates of prior ratings for a plurality of items by other users, c) calculating a selection likelihood statistics as a function of the estimated mean vector, the estimated covariance matrix and the rating information provided by the, d) calculating ranking statistics as a function of the selection likelihood statistics, and e) transmitting a response to the user recommending at least one of the items not previously selected as a function of the ranking statistics. The recommended item may, for example, be a movie.

Abstract: A system and method for recommending items to a user is provided. The system could combine recommendations provided by multiple recommenders by: a) calculating for each recommender j a maximum score Pj for the recommended n items as a function (e.g., sum) of stored recommender ratings for the n items, b) calculating a minimum acceptable score for each recommender system j as a function of the maximum score Pj and a predetermined tradeoff factor ?j such that the minimum acceptable score for at least one recommender system j is less than the maximum score Pj, c) selecting at least one set of items from the plurality of items, such that scores Pj (and/or sum of scores Pj) calculated for the selected set of items for each recommender system j are greater than the respective minimum acceptable score for that recommender system j, and d) identifying selected set of items to the user.

Abstract: A system and method for detecting billing errors using predictive models is provided. The system includes a computer system and a billing error detection engine capable of detecting billing errors using predictive modeling techniques. The system receives and pre-processes billing information. The system then applies one or more predictive models to the information to identify billing errors. The results could be optionally sent to, and reviewed by, third party auditors, whereby their feedback could be incorporated into the results. A final report is generated by the system which indicates billing errors that require correction, thereby allowing an entity to correct such errors and prevent revenue leakage.