November 25, 2019 | 06.30 PM EDT
Healthcare has always been a process-heavy endeavor with a heavy dependency on documenting said processes for ease of replication. As civilization evolved, so did the documentation processes themselves, and with the advent of modern healthcare, the emphasis on documentation reached unprecedented levels both in terms of accuracy, volume, and complexity.
Clinical documentation helps automate pre-defined processes and enhances healthcare delivery across hospitals. Artificial Intelligence (AI) is now making huge strides in automating the clinical documentation process across its lifecycle. AI-powered automation can impact clinical documentation at a granular level and deliver exciting results across the board.
AI can be seamlessly integrated into the clinical documentation workflow, benefitting various players involved. A provider's dictation notes mark the beginning of the clinical documentation process in the healthcare workflow. The provider’s encounter with the patient is the single most important source of patient information. Today, the provider must juggle conversing with the patient, taking notes based on the discussion and observations, analyzing the information at hand, arriving at a diagnosis, and providing treatment options and prescriptions, all this within a profitable time window. Somebody efficiently able to do so much outside a clinical setting would easily enter the Cirque-du-Soleil.
AI-powered speech-to-text conversion applications could help providers offset most of the clerical load during the encounter phase. The provider’s workflow could be integrated into an app that enables the provider to keep track of their patients, record their encounters with them, which can then be converted to text by transcriptionists.
Transcription has been a labor-intensive process that all healthcare providers and institutions have had to allocate generous and resources. AI-driven clinical documentation workflow can reduce the turnaround time for medical transcription and improve the productivity of healthcare professionals. An AI engine such as CascadeMD could take the dictation converted into text, apply Natural Language Processing (NLP) to it and convert the unstructured conversational data into a structured transcript based on a pre-defined template.
Updating Electronic Health Record (EHR) is the last phase of the clinical documentation process and this is also perhaps the most exacting and time-consuming one. Statistically, this is the single task that providers spend most of their time on; time that could be effectively used to provide better care to more patients. AI helps navigate the complex EHR forms and populate the fields with the structured data extracted from the unstructured text of the medical transcript. This would free up the invaluable time for the providers daily, in addition to eliminating human error from this clerical task.
It is not as easy as it sounds, and certainly not a simplified one-size-fits-all implementation. Medical data is complex, unstructured and multi-layered and this demands a robust AI engine to process and understand the data, and to assist with further courses of action. Innovative applications like CascadeMD achieve these complex levels of automation with aplomb. However, the availability of enormous quantities of training data is crucial to this process. And even then, the AI will merely be able to augment the process by speeding things up and assisting by automating mundane processes within the workflow.