Abstract: A method performed by a first microservice processor includes receiving a first user indication from a first computing device from a plurality of computing devices of a second user that indicates that a first user requested a service of the second user. A first user-specific data container object is generated that includes first user-specific data elements associated with the service provided to the first user. First user-specific service-necessity information data objects are obtained from electronic resources. The first user-specific data elements and the first user-specific service-necessity information data objects are inputted into a machine learning engine to obtain ranked reference-claim data objects. A chosen reference-claim data object from the ranked reference-claim data objects is chosen by the second user to justify providing the service to the first user.

Abstract: Embodiments of systems and methods of the present disclosure include a database layer queries patient-related raw data from data tables of distinct data collection services, normalizes the patient-related raw data to produce common format normalized patient-related data, and stores the common format normalized patient-related data. An algorithmic processing layer, in real-time, analyzes the common format normalized patient-related data based on patient-related data metrics, a healthcare facility-related data metrics, or both, to determine patient-related data points, where each patient-related data point is associated with a respective threshold condition and a respective ranking score, ranks the patient-related data points based on the threshold condition and the ranking score to determine a priority, and generates actionable medical directives related to patients, where the actionable medical directives cause real-time operational changes in the way that the healthcare facility services the patients.