Identifying ownership of a healthcare clinic based on enrollment information. A method includes identifying a plurality of practitioners associated with a clinic and calculating a proportion of the plurality of practitioners that are enrolled under a healthcare group based on enrollment information stored in an enrollment database. The method includes determining whether the proportion satisfies a threshold proportion of practitioners enrolled under the healthcare group and imputing a group ID associated with the healthcare group to the clinic if the proportion satisfies the threshold proportion.

Identifying relationships between healthcare practitioners and healthcare facilities based on billed claims. A method includes assessing facility claims billed by a facility over a time period to identify a practitioner that performed a procedure at the facility within the time period and assessing carrier claims billed by the practitioner over the time period to identify all facilities in which the practitioner performed a procedure within the time period. The method includes calculating a total quantity of the facility claims billed over the time period that indicate a procedure was performed at the facility and a total quantity of the carrier claims billed by the practitioner over the time period that indicate the practitioner performed a procedure.

Identifying and quantifying referral patterns between healthcare entities based on billed claims. A method includes determining a plurality of billed claims and identifying one or more unique performing healthcare entities included in at least one of the plurality of billed claims. The method includes identifying one or more unique referring identifiers included in at least one of the plurality of billed claims. The method includes, for at least one of the one or more unique performing healthcare entities, calculating a proportion of referrals coming from each of the one or more unique referring identifiers over the plurality of billed claims.

A water prescribing system 1 is provided with a state storage unit 21 which stores a state variable relating to a physical constitution and health condition for each of a plurality of persons including a user, and a prescription unit 22 which creates a prescription of water suitable for the user based on the state variable.

A water prescribing system 1 is provided with a state storage unit 21 which stores a state variable relating to a physical constitution and health condition for each of a plurality of persons including a user, and a prescription unit 22 which creates a prescription of water suitable for the user based on the state variable.

Systems and methods presented herein relate to cancelling a modification of medicament delivery initiated by a user. A display of a therapy control element can be generated on an interface, such as a touchscreen. The therapy control element permits a user to modify a control parameter used in a control algorithm for generating a dose control signal for delivering medicament to a subject. Responsive to receiving the modification, the control parameter may be modified at a first time from a first setting to a second setting based on an indication of the modification to the therapy control element. Responsive to receiving a restore gesture on the touchscreen at a second time, the control parameter may be restored back to the first setting. This restore gesture may be a swipe gesture performed by a user.

Disclosed embodiments provide techniques that alleviate the challenges healthcare leaders face in the administration and management of provider compensation. Disclosed embodiments provide systems and methods that automate calculating and adjudicating, and monitoring provider compensation while providing real-time feedback to administrators and providers on performance under the compensation methodology. The automation allows organizations to increase transparency while providing secure access to information, allowing for more robust discussion of alternatives and alignment between the enterprise and providers. Thus, disclosed embodiments serve to ensure compliance, enable transparency, and empower validation throughout the provider compensation process.

Disclosed embodiments provide techniques that alleviate the challenges healthcare leaders face in the administration and management of provider compensation. Disclosed embodiments provide systems and methods that automate calculating and adjudicating, and monitoring provider compensation while providing real-time feedback to administrators and providers on performance under the compensation methodology. The automation allows organizations to increase transparency while providing secure access to information, allowing for more robust discussion of alternatives and alignment between the enterprise and providers. Thus, disclosed embodiments serve to ensure compliance, enable transparency, and empower validation throughout the provider compensation process.

A system and method are provided for generating a predictive model for predicting a surgery duration. The system and method use a feature selection technique to identify a set of features in training data, which set of features is predictive of the surgery duration, train a number of predictive models using the set of features as input and the surgery duration as prediction target, wherein the predictive models include at least a linear predictive model and a non-linear predictive model, and generate an ensemble model which combines at least two of the predictive models. Such an ensemble model may optimally combine linear and non-linear predictions and therefore allow linear and non-linear relationships between features and the surgery duration to be taken into account. Advantageously, more accurate surgery planning may safeguard the health of patients, for example by ensuring that there are sufficient resources available for acute surgeries, or by avoiding that elective surgeries have to be postponed due to a presumed lack of resources.

A system and method are provided for generating a predictive model for predicting a postoperative bed type to be used by a patient after surgery. The predictive model is trained on features extracted from medical data and using a postoperative bed type as prediction target in the training. The predictive model is configured to output a probability on a scale 400 which corresponds to, at its lower end, a prediction of a first postoperative bed type and, at its upper end, a prediction of a second postoperative bed. A hybrid model is generated which applies a lower 410 and an upper threshold 420 to the probability scale. If the output probability of the predictive model is in between both thresholds, an expert selection of the bed type is recommended, while otherwise, the prediction of the predictive model is output. The values of the thresholds are optimized using a performance metric.