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The creation of medical notes in software applications poses an intrinsic problem in workflow as the technology inherently intervenes in the processes of collecting and assembling information, as well as the production of a data-driven note that meets both individual and healthcare system requirements. In addition, the note writing applications in currently available electronic health records (EHRs) do not function to support decision making to any substantial degree. We suggest that artificial intelligence (AI) could be utilized to facilitate the workflows of the data collection and assembly processes, as well as to support the development of personalized, yet data-driven assessments and plans.
Many doctors find the creation of the same note more onerous in an electronic health record (EHR) than on paper [
Part of the issue involves the user interface, where many users are not terribly facile with the keyboard and typing. It would probably be worthwhile for the creators of EHRs to design their user interfaces to be as similar as possible to the Internet-based applications, such as Web browsers, that even those who are unsophisticated with computers use every day. But the fundamental reason for this discomfort is that electronic note writers are not able to pull information seamlessly and freely from their own minds to create the contents of the kind of notes they wish to create. In contrast to the historic paper-based documentation workflow, the EHR user must painfully search through the bins of items buried in the software to extract the correct “pieces” of information necessary to complete the entry, requiring click after click after click in that process (
In the Lego system, the myriad individual pieces (or modules) are assembled together by the rules (or protocols) dictated by the snap connections to create the toy version of an engineered system [
An assorted bin and 5 (mostly) color-coded bins of Lego toy pieces. The color-coded Legos may represent items that clearly and cleanly fall into a particular section of the note, depending on how the note is organized (ie, SOAP versus systems-oriented).
In the context of EHRs, how can the natural, direct brain-to-hand workflow of paper note creation process be digitally recreated to simulate the free and seamless flow of information that historically emanates from the clinician’s brain directly onto paper? How could the obstructive middleman of technology be enhanced to support, rather than clog the process of clinical documentation? And could this be done in a fashion that makes utilization as intuitive as current Web browsers are to use? Furthermore, in addition to supplying the pieces, can this support also be applied to the assembly of the assessment and plan to assist in the production of a note that preserves the personal character—or the signature or “human”-ess—of the note writer? Optimally, in contrast to today’s copied and pasted rote entries, the production of a note that is more interesting and easier to read than current electronic notes would also be a goal of this redesign process.
We will progressively need to introduce important note information from other sources (eg, personal device and patient-entered data, population databases, even genomics) that supplement what is now available to the clinicians creating or reviewing the note at a later time [
AI has the potential to assist users in extracting the right information from the different information systems (ie, previous electronic notes and bedside monitors, and imaging, laboratory and pharmacy systems), assembling this information into the proper places in the note to assist in the formulation of the assessment with some bounds of certainty, and to analyze that assessment to develop a data-driven plan of action. There are many tools in the AI armamentarium—machine learning, natural language processing, computer vision, constraint satisfaction—but in essence, AI would power a learning interface between the human user and digital health information system to produce a note that would be highly, and increasingly over use, similar to that note-writer’s mental representation of what a clinical note should be.
We do realize that AI cannot analyze and repackage data until the latter has been incorporated into the system. The current history and physical examination, whether taken at the bedside or the office examining room, cannot be leveraged for note writing until they are so entered. Better, easier means for this must be devised: this might involve free text entry by voice recognition or keyboard, natural language processing of free text to enter structured data into the system, or new AI modalities as this exploding field develops.
Based on that current user input, as well as all available automatic data sources (eg, prior electronic notes, interfaced data like labs, and vital signs), AI would provide helpful suggestions to the user about what information is available and how it might influence the next course of action. AI could also function to emphasize or deemphasize certain elements of the record, based on previous results, external databases, and knowledge networks [
At the heart of note writing is communicating important clinical events and decisions between different providers, and with the advent of patient portals, between providers and patients. AI is not the panacea to every problem in healthcare, but for a relatively repetitive and clearly defined task such as clinical note creation, it seems to provide a fairly ideal solution. It also bestows an opportunity to support an interdisciplinary care environment by learning from inter-specialty communication specifics and facilitating shared decision making by mining patient input and feedback. The final note would be the product of the user, but a user who is not exhausted by painful de-binning and endless clicking to insert the right data in the right places. An AI-enhanced system would boost the clinical workflow element of documentation, and maybe even inject some fun into the process of note writing. Such technology is upon us: 1 in 10 communications to the AI-powered personal assistant Amy (or Andrew) Ingram is a note of thanks, a testament to the 21st century computer passing the Turing test [
artificial intelligence
electronic health record
None declared.