Journal:Clinical note creation, binning, and artificial intelligence
| Full article title | Clinical note creation, binning, and artificial intelligence |
|---|---|
| Journal | JMIR Medical Informatics |
| Author(s) | Deliberato, Rodrigo Octávio; Celi, Leo Anthony; Stone, David J. |
| Author affiliation(s) |
Massachusetts Institute of Technology, Hospital Israelita Albert Einstein, Beth Israel Deaconess Medical Center, University of Virginia School of Medicine |
| Primary contact | Email: lceli at mit dot edu; Phone: 1 6172537937 |
| Editors | Eysenbach, G. |
| Year published | 2017 |
| Volume and issue | 5 (3) |
| Page(s) | e24 |
| DOI | 10.2196/medinform.7627 |
| ISSN | 2291-9694 |
| Distribution license | Creative Commons Attribution 4.0 International |
| Website | http://medinform.jmir.org/2017/3/e24/ |
| Download | http://medinform.jmir.org/2017/3/e24/pdf (PDF) |
Abstract
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.
Keywords: electronic health records, artificial Intelligence, clinical informatics
Introduction
Many doctors find the creation of the same note more onerous in an electronic health record (EHR) than on paper.[1] The following quote from a senior physician reflects the dissatisfaction doctors have with EHRs: “My experience with the EHR is that it is the biggest waste of time, interferes with patient care, forces the physician to collect thousands of pieces of useless information, and produces marginal improvements in quality.” For this and many other reasons, the quality of EHR documentation has ranged from suboptimal to dismal.[2][3] This paper explores and envisions how artificial intelligence (AI), which is increasingly transforming facets of daily living, could support the currently burdensome process of gathering and organizing the elements necessary for the creation of a clinical note.
Finding the right pieces
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 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 (Figure 1). While the freedom involved in creating paper notes might represent a positive, nostalgic memory, the healthcare system is not going to abandon EHRs with all the manifold advantages that they represent and provide.
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.[4] In creating a note, the user identifies and captures the necessary data pieces, analyzes and reassembles the pieces to assess the clinical situation at the level of complexity required, and develops a plan of action, thereby recreating a kind of clinical data system in itself each time a note is completed and entered.[5] But instead of rummaging around in a variety of bins for the right pieces, how could the de-binning ordeal be circumvented, and even improved, by a technical solution? We propose that a carefully engineered implementation of AI into the note creation software elements of the EHR would not only reduce the required rummaging through bins of pieces, but it could assist in the assembly of those pieces into the desired output (i.e., a useful, readable, and cogent note that meets all the necessary requirements for clinical documentation).
References
- ↑ Hingle, S. (2016). "Electronic Health Records: An Unfulfilled Promise and a Call to Action". Annals of Internal Medicine 165 (11): 818-819. doi:10.7326/M16-1757. PMID 27595501.
- ↑ Markel, A. (2010). "Copy and paste of electronic health records: A modern medical illness". American Journal of Medicine 123 (5): e9. doi:10.1016/j.amjmed.2009.10.012. PMID 20399309.
- ↑ Hirschtick, R.E. (2012). "A piece of my mind: John Lennon's elbow". JAMA 308 (5): 463–4. doi:10.1001/jama.2012.8331. PMID 22851112.
- ↑ Csete, M.E.; Doyle, J.C. (2002). "Reverse engineering of biological complexity". Science 295 (5560): 1664-9. doi:10.1126/science.1069981. PMID 11872830.
- ↑ Celi, L.A.; Csete, M.; Stone, D. (2014). "Optimal data systems: the future of clinical predictions and decision support". Current Opinion in Critical Care 20 (5): 573-80. doi:10.1097/MCC.0000000000000137. PMC PMC4215932. PMID 25137399. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4215932.
Abbreviations
AI: artificial intelligence
EHR: electronic health record
Notes
This presentation is faithful to the original, with only a few minor changes to presentation. In several cases the PubMed ID was missing and was added to make the reference more useful.
Per the distribution agreement, the following copyright information is also being added:
©Rodrigo Octávio Deliberato, Leo Anthony Celi, David J Stone. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 03.08.2017.
