Four Steps to Success in Implementing AI Within Healthcare Organizations

The following is a guest article by Ben Cushing, Chief Architect of Health and Life Sciences at Red Hat Artificial intelligence (AI) is showing up in more healthcare contexts, from drug discovery to diagnostic imaging to robot-assisted surgery. But one area where AI can have a tremendous impact is in the reduction of provider burden. For instance, the Veterans Health Administration (VHA) is exploring automated, AI-assisted documentation of patient visits with clinicians. The system records the conversation between clinician and patient, and then automatically produces a clinical note. The AI system can apply accurate medical terminology, follow the Subjective, Objective, Assessment, and Plan (SOAP) format, and make best-practice treatment recommendations. It produces the clinical note in near real-time, so the clinician can review it and make any necessary edits at the time of the encounter. AI can then translate the clinical concepts into standard formats. These formats include Systematized Nomenclature of Medicine – Clinical Terms (SNOMED CT), used for the electronic exchange of clinical health information, and Logical Observation Identifiers Names and Codes (LOINC), which provides a common language for laboratory tests and clinical measures. Such AI use cases can save clinicians and administrative staff significant time. They can also improve patient experiences and contribute to better patient outcomes. It’s no wonder 94% of healthcare companies say they’re investin...
Source: EMR and HIPAA - Category: Information Technology Authors: Tags: AI/Machine Learning Ambulatory C-Suite Leadership Health IT Company Healthcare IT Hospital - Health System IT Infrastructure and Dev Ops LTPAC AI Implementation Artificial Intelligence Ben Cushing Healthcare AI Process Automation Source Type: blogs