Abstract: Systems and methods for operationalizing predicted changes in risk based on interventions are presented herein. In an example computer-implemented method, a computing device may store information about patients as analyzed text. The computing device can receive, from a machine learning model, intervention information for a patient. The computing device may generate a prioritized intervention list according to the intervention information and provide remote access to the prioritized intervention list. The computing device may receive updated patient engagement data as freeform text. The computing device can convert the freeform text of the updated patient engagement data into analyzed text. The computing device may receive, from the machine learning model, updated intervention information, wherein the machine learning model is re-trained based on the updated information. The computing device can generate an updated prioritized intervention list according to the intervention information.

Abstract: In some implementations, the device may include receiving a prompt via a graphical user interface of a computing device, where the prompt identifies a target institution of a plurality of institutions, a patient condition, and a treatment. In addition, the device may include providing the prompt as input to ac generative language model, where the generative language model may include a pre-trained machine learning model that was initially trained on a general domain and subsequently trained on a target domain. The device may include receiving a generated pre-authorization letter as output from the generative language model, where the generated pre-authorization letter includes one or more fields identifying information requested from a user of the computing device. Moreover, the device may include presenting the generated pre-authorization letter to the user via the graphical user interface of the computing device.