Systems, devices, and technical methods of the present disclosure leverage digital visitor logs from confinement facilities to identify people of interest that may be involved in incidents to which first responders are dispatched. When a phone call is received at phone public safety access point, a first query including a name associated with the call is transmitted to a digital visitor log for a confinement facility. A digital response to the first query identifies a visitee who was visited by a visitor by that name while the visitee was in confinement. The visitee's name is then transmitted in a second query to a digital repository containing release records; a digital response indicates the visitee was released before the call was received. An electronic notification that identifies the visitee is transmitted to a device associated with an agent who is responding to the call.

Systems, devices, computer-implemented methods, and/or computer program products that facilitate artificial intelligence (AI)-assisted curation of non-pharmaceutical intervention (NPI) data from heterogeneous data sources. In one example, a system can comprise a processor that executes computer executable components stored in memory. The computer executable components can comprise an extraction component and a change detection component. The extraction component can extract candidate non-pharmaceutical intervention (NPI) events from data associated with a defined disease. The change detection component can evaluate the candidate NPI events for inclusion in a dataset storing NPI events in a defined format.

A scheme for securely transferring a patient data file to an intended recipient regardless of a transfer mode selected by a sender. Encryption system executing at the sender device is operative to encrypt each plaintext data line of a file, one by one, using a symmetric key and a starting IV that is incremented per each line, resulting in corresponding ciphertext lines added to an encrypted file. A hash is generated based on the encrypted file. An encrypted header containing the symmetric key, starting IV and the hash is generated using a public key of the recipient, which is appended to the encrypted file. The encrypted header and associated encrypted file are transmitted to the recipient in any manner. Upon receipt, the recipient decrypts the encrypted header using a private key to obtain the symmetric key, starting IV and the hash, which are used by the recipient to validate and decrypt the encrypted file on a line-by-line basis.

A scheme for securely transferring a patient data file to an intended recipient regardless of a transfer mode selected by a sender. Encryption system executing at the sender device is operative to encrypt each plaintext data line of a file, one by one, using a symmetric key and a starting IV that is incremented per each line, resulting in corresponding ciphertext lines added to an encrypted file. A hash is generated based on the encrypted file. An encrypted header containing the symmetric key, starting IV and the hash is generated using a public key of the recipient, which is appended to the encrypted file. The encrypted header and associated encrypted file are transmitted to the recipient in any manner. Upon receipt, the recipient decrypts the encrypted header using a private key to obtain the symmetric key, starting IV and the hash, which are used by the recipient to validate and decrypt the encrypted file on a line-by-line basis.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a subset of brain data of a patient. One of the methods includes obtaining data characterizing a brain of a patient; determining a first prompt for presentation to a user; obtaining a first user input characterizing a first response to the first prompt; determining, using the first response to the first prompt, a second prompt for presentation to the user; obtaining a second user input characterizing a second response to the second prompt, wherein at least one of the first prompt or the second prompt seek a response based on a clinical observation of the patient; and determining a subset of the obtained data using the first response to the first prompt and the second response to the second prompt.

Methods, systems, and apparatus, including computer programs encoded on computer storage media, for determining a subset of brain data of a patient. One of the methods includes obtaining data characterizing a brain of a patient; determining a first prompt for presentation to a user; obtaining a first user input characterizing a first response to the first prompt; determining, using the first response to the first prompt, a second prompt for presentation to the user; obtaining a second user input characterizing a second response to the second prompt, wherein at least one of the first prompt or the second prompt seek a response based on a clinical observation of the patient; and determining a subset of the obtained data using the first response to the first prompt and the second response to the second prompt.

In clinical documentation, mere documentation of a condition in a patient's records may not be enough. To be considered sufficiently documented, the patient's record needs to show that no documentation drop-offs (DDOs) have occurred over the course of the patient's stay. However, DDOs can be extremely difficult to detect. To solve this problem, the invention trains time-sensitive deep learning (DL) models on a per condition basis using actual and/or synthetic patient data. Utilizing an ontology, grouped concepts can be generated on the fly from real-time hospital data and used to generate time-series data that can then be analyzed by trained time-sensitive DL models to determine whether a DDO for a condition has occurred during the stay. Non-time-sensitive models can be used to detect all the conditions documented during the stay. Outcomes from the models can be compared to determine whether to notify a user that a DDO has occurred.

In clinical documentation, mere documentation of a condition in a patient's records may not be enough. To be considered sufficiently documented, the patient's record needs to show that no documentation drop-offs (DDOs) have occurred over the course of the patient's stay. However, DDOs can be extremely difficult to detect. To solve this problem, the invention trains time-sensitive deep learning (DL) models on a per condition basis using actual and/or synthetic patient data. Utilizing an ontology, grouped concepts can be generated on the fly from real-time hospital data and used to generate time-series data that can then be analyzed by trained time-sensitive DL models to determine whether a DDO for a condition has occurred during the stay. Non-time-sensitive models can be used to detect all the conditions documented during the stay. Outcomes from the models can be compared to determine whether to notify a user that a DDO has occurred.

Systems, methods and computer-readable media are provided for facilitating clinical decision support and managing patient population health by health-related entities including caregivers, health care administrators, insurance providers, and patients. Embodiments of the invention provide decision support services including providing timely contextual patient information including condition risks, risk factors and relevant clinical information that are dynamically updatable; imputing missing patient information; dynamically generating assessments for obtaining additional patient information based on context; data-mining and information discovery services including discovering new knowledge; identifying or evaluating treatments or sequences of patient care actions and behaviors, and providing recommendations based on this; intelligent, adaptive decision support services including identifying critical junctures in patient care processes, such as points in time that warrant close attention by caregivers; near-real time querying across diverse health records data sources, which may use diverse clinical nomenclatures and ontologies; improved natural language processing services; and other decision support services.

The present disclosure describes clinical workflows, methods, and systems used to perform an indirect to direct subjective refraction to a patient with a mobile smartphone application that works as a encrypted remote administration tool in any portable electronic device and interconnect both systems via by 4G, 5G, 6G, or Wifi. According to various embodiments, an eye doctor may utilize a remote administration tool (RAT) or (RAS) remote access software application on a portable electronic device (PED) (smartphone, tablet, or laptop) to view and control the main control base (MCB) anywhere in the world to interconnect both systems. The eye doctor can perform an on-demand live subjective vision refraction via RAT technology. Furthermore, the eye doctor can control the (MCB) that can control, exam chair, digital phoropter, vision chart software, robotic phoropter arm, exam chair height, exam room lights, and near robotic chart arm anywhere in the world.