# US 2026/0100286 A1 · Machine learning based smart array for healthcare providers > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 2026/0100286 A1 - Document ID: US20260100286A1 - Status: Published application - Kind: A1 - Application number: 19/211,431 - Filed: 2025-05-19 - Published: 2026-04-09 - Earliest claimed: 2024-10-09 - Assignees: Cerner Innovation, Inc. - Applicants: Cerner Innovation, Inc. - Inventors: Praveen Bhat GURPUR; Suchitra Joyce PHILLIPS - Web page: https://patentgrep.com/patent/US20260100286A1 ## CPC - G16H 50/70: for mining of medical data, e.g. analysing previous cases of other patients - G16H 10/60: for patient-specific data, e.g. for electronic patient records - G16H 20/10: relating to drugs or medications, e.g. for ensuring correct administration to patients - G16H 40/20: for the management or administration of healthcare resources or facilities, e.g. managing hospital staff or surgery rooms - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - H04W 12/037: of the control plane, e.g. signalling traffic - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA - H04W: WIRELESS COMMUNICATION NETWORKS (broadcast communication H04H; communication systems using wireless links for non-selective communication, e.g. wireless extensions H04M1/72) ## Abstract Embodiments provide healthcare information to a plurality of healthcare providers. Embodiments receive first historical information corresponding to the plurality of healthcare providers and receive second historical information corresponding to external health care information sources. Embodiments train an artificial intelligence (“AI”) model using the first historical information and the second historical information. Embodiments receive current information corresponding to the plurality of healthcare providers and/or the external health care information sources. In response to the current information, embodiments generate one or more healthcare suggestions by the AI model and deliver the suggestions to one or more of the plurality of healthcare providers. ## Claims ### Claim 1 A method of providing healthcare information to a plurality of healthcare providers, the method comprising: receiving first historical information corresponding to the plurality of healthcare providers; receiving second historical information corresponding to external health care information sources; training an artificial intelligence (AI) model using the first historical information and the second historical information; receiving current information corresponding to the plurality of healthcare providers and/or the external health care information sources; and in response to the current information, generating one or more healthcare suggestions by the AI model and delivering the suggestions to one or more of the plurality of healthcare providers. ### Claim 2 (depends on 1) The method of claim 1, wherein the first historical information comprises one or more of: data on the healthcare providers; information related to healthcare personnel; patient demography; clinical information or patient outcomes. ### Claim 3 (depends on 1) The method of claim 1, wherein the second historical information comprises one or more of: latest therapeutic protocols and drugs; opportunities for continuing medical education; local conditions; disease outbreaks and accidents; or natural disasters and calamities. ### Claim 4 (depends on 1) The method of claim 1, wherein the suggestions comprise one or more of: latest approved protocols for treatment; new generation of drugs available; or contact information of other physicians who have treated similar conditions successfully. ### Claim 5 (depends on 1) The method of claim 1, wherein the suggestions comprise one or more of: details of available training; recommended associations of doctors to join; or research articles to review. ### Claim 6 (depends on 1) The method of claim 1, wherein the suggestions comprise one or more of: an indication of drugs to immediately procure; latest information regarding a disease outbreak; or resources for emergency procedure training. ### Claim 7 (depends on 1) The method of claim 1, wherein the AI model comprises a generative AI model, further comprising: retraining the AI model in response to actions taken by the plurality of healthcare providers in response to the suggestions. ### Claim 8 (depends on 1) The method of claim 1, further comprising: using a cloud infrastructure for providing healthcare information, the cloud infrastructure comprising a first virtual cloud network (VCN) comprising a local peering gateway (LPG) communicatively coupled to a secure shell (SSH) VCN via the LPG; wherein the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN. ### Claim 9 A computer readable medium having instructions stored thereon that, when executed by one or more processors, cause the processors to provide healthcare information to a plurality of healthcare providers, the providing healthcare information comprising: receiving first historical information corresponding to the plurality of healthcare providers; receiving second historical information corresponding to external health care information sources; training an artificial intelligence (AI) model using the first historical information and the second historical information; receiving current information corresponding to the plurality of healthcare providers and/or the external health care information sources; and in response to the current information, generating one or more healthcare suggestions by the AI model and delivering the suggestions to one or more of the plurality of healthcare providers. ### Claim 10 (depends on 9) The computer readable medium of claim 9, wherein the first historical information comprises one or more of: data on the healthcare providers; information related to healthcare personnel; patient demography; clinical information or patient outcomes. ### Claim 11 (depends on 9) The computer readable medium of claim 9, wherein the second historical information comprises one or more of: latest therapeutic protocols and drugs; opportunities for continuing medical education; local conditions; disease outbreaks and accidents; or natural disasters and calamities. ### Claim 12 (depends on 9) The computer readable medium of claim 9, wherein the suggestions comprise one or more of: latest approved protocols for treatment; new generation of drugs available; or contact information of other physicians who have treated similar conditions successfully. ### Claim 13 (depends on 9) The computer readable medium of claim 9, wherein the suggestions comprise one or more of: details of available training; recommended associations of doctors to join; or research articles to review. ### Claim 14 (depends on 9) The computer readable medium of claim 9, wherein the suggestions comprise one or more of: an indication of drugs to immediately procure; latest information regarding a disease outbreak; or resources for emergency procedure training. ### Claim 15 (depends on 9) The computer readable medium of claim 9, wherein the AI model comprises a generative AI model, the providing healthcare information further comprising: retraining the AI model in response to actions taken by the plurality of healthcare providers in response to the suggestions. ### Claim 16 (depends on 1) The method of claim 1, the providing healthcare information further comprising: using a cloud infrastructure for providing healthcare information, the cloud infrastructure comprising a first virtual cloud network (VCN) comprising a local peering gateway (LPG) communicatively coupled to a secure shell (SSH) VCN via the LPG; wherein the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN. ### Claim 17 A cloud based system for providing healthcare information to a plurality of healthcare providers, the system comprising: an artificial intelligence (AI) model; one or more processors coupled to the AI model and configured to: receive first historical information corresponding to the plurality of healthcare providers; receive second historical information corresponding to external health care information sources; train the AI model using the first historical information and the second historical information; receive current information corresponding to the plurality of healthcare providers and/or the external health care information sources; and in response to the current information, generate one or more healthcare suggestions by the AI model and delivering the suggestions to one or more of the plurality of healthcare providers. ### Claim 18 (depends on 17) The cloud based system of claim 17, wherein the first historical information comprises one or more of: data on the healthcare providers; information related to healthcare personnel; patient demography; clinical information or patient outcomes. ### Claim 19 (depends on 17) The cloud based system of claim 17, wherein the second historical information comprises one or more of: latest therapeutic protocols and drugs; opportunities for continuing medical education; local conditions; disease outbreaks and accidents; or natural disasters and calamities. ### Claim 20 (depends on 17) The cloud based system of claim 17, wherein the system is executed on a cloud infrastructure, the cloud infrastructure comprising a first virtual cloud network (VCN) comprising a local peering gateway (LPG) communicatively coupled to a secure shell (SSH) VCN via the LPG; wherein the LPG is contained in a control plane VCN and the SSH VCN is communicatively coupled to a data plane VCN.