Abstract: Methods, systems, and computer-storage media are provided for facilitating the management of population health. A parallel processing architecture receives patient population health data from healthcare facilities along with any updated data. A high-level clinical logic is executed against the data to identify, among other things, patients in the population who qualify for health intervention programs.

Abstract: Methods, systems, and computer-storage media are provided for facilitating the management of population health. A parallel processing architecture receives patient population health data from healthcare facilities along with any updated data. A high-level clinical logic is executed against the data to identify, among other things, patients in the population who qualify for health intervention programs.

Abstract: Methods, systems, and computer-storage media are provided for facilitating the management of population health. A parallel processing architecture receives patient population health data from healthcare facilities along with any updated data. A high-level clinical logic is executed against the data to identify, among other things, patients in the population who qualify for health intervention programs. Using this information, healthcare facilities can implement management programs to help care for these patients.

Abstract: Methods, systems, and computer-storage media are provided for facilitating the management of population health. A parallel processing architecture receives patient population health data from healthcare facilities along with any updated data. A high-level clinical logic is executed against the data to identify, among other things, patients in the population who qualify for health intervention programs.

Abstract: Methods, systems, and computer-storage media are provided for providing explainability to an end-user on why a particular patient failed to qualify for a health-related measure. High-level clinical logic explainability rules are generated on top of high-level clinical logic used to identify members of a population who qualify and who do not qualify for the health-related measure. The explainability rules are designed to identify which components in the high-level clinical logic are not satisfied by the patient's health data, and, more specifically, which of the components' sub-criteria disqualified the patient from the health-related measure. The output of the explainability rules is structured into an easy-to-understand format that is presented on a user interface associated with an end-user such as a clinician.

Abstract: Methods, systems, and computer-storage media are provided for providing explainability to an end-user on why a particular patient failed to qualify for a health-related measure. High-level clinical logic explainability rules are generated on top of high-level clinical logic used to identify members of a population who qualify and who do not qualify for the health-related measure. The explainability rules are designed to identify which components in the high-level clinical logic are not satisfied by the patient's health data, and, more specifically, which of the components' sub-criteria disqualified the patient from the health-related measure. The output of the explainability rules is structured into an easy-to-understand format that is presented on a user interface associated with an end-user such as a clinician.

Abstract: A base member mounted on a projectile with a movable member mounted thereon for radially outwardly sliding movement in response to spinning of the projectile, the movable member having a center of gravity located at a first point which prevents the sliding movement thereof and the center of gravity being shiftable to a second point by the controlled flow of a predetermined mass, e.g. steel balls, which shifting of the center of gravity allows the movable member to slide from the safe to the arm position.