JMIR MEDICAL INFORMATICS

Koonce et al

Viewpoint

Using a Natural Language Processing Approach to Support Rapid
Knowledge Acquisition
Taneya Y Koonce1*, MSLS, MPH; Dario A Giuse2*, MS, Dr Ing; Annette M Williams1, MLS; Mallory N Blasingame1,
MA, MSIS; Poppy A Krump1, MSIS; Jing Su1, MS, MSIS, MD; Nunzia B Giuse1,2, MLS, MD
1

Center for Knowledge Management, Vanderbilt University Medical Center, Nashville, TN, United States

2

Department of Biomedical Informatics, Vanderbilt University School of Medicine, Vanderbilt University Medical Center, Nashville, TN, United States*these authors contributed equally

Corresponding Author:
Taneya Y Koonce, MSLS, MPH
Center for Knowledge Management
Vanderbilt University Medical Center
3401 West End
Suite 304
Nashville, TN, 37203
United States
Phone: 1 6159365790
Email: taneya.koonce@vumc.org

Abstract
Implementing artificial intelligence to extract insights from large, real-world clinical data sets can supplement and enhanceknowledge management efforts for health sciences research and clinical care. At Vanderbilt University Medical Center (VUMC),
the in-house developed Word Cloud natural language processing system extracts coded concepts from patient records in VUMC’selectronic health record repository using the Unified Medical Language System terminology. Through this process, the Word
Cloud extracts the most prominent concepts found in the clinical documentation of a specific patient or population. The Word
Cloud provides added value for clinical care decision-making and research. This viewpoint paper describes a use case for how
the VUMC Center for Knowledge Management leverages the condition-disease associations represented by the Word Cloud to
aid in the knowledge generation needed to inform the interpretation of phenome-wide association studies.
(JMIR Med Inform 2024;12:e53516) doi: 10.2196/53516
KEYWORDS
natural language processing; electronic health records; machine learning; data mining; knowledge management; NLP

Introduction
The rapid advancement and availability of artificial intelligence(AI) approaches provide biomedical informatics groups with
opportunities for exploring and generating insights from internaland external data at scale to enhance health sciences researchand clinical care [1,2]. One such opportunity is using natural
language processing (NLP) to extract usable knowledge fromthe vast amounts of structured and unstructured clinical data
captured daily via the electronic health record (EHR). Insightsfrom this process can be used to inform patient care, target
information provision, and generate research hypotheses. This
paper presents some of the activities that such usable knowledge
makes possible.

https://medinform.jmir.org/2024/1/e53516

XSL• FO
RenderX

Vanderbilt University Medical Center (VUMC) maintains anelectronic health repository containing data for over 4.6 million
individuals, going back to 1995, which includes structured data(eg, laboratory results and vital signs), textual data (eg, provider
notes and radiology interpretations), reports (eg,electrocardiograms and pulmonary function test results), andimage data. Included in this vendor-agnostic repository are all
VUMC patient data captured from the in-house developed
StarPanel EHR (VUMC) dating back to 2001 [3] and VUMC’scurrent vendor-based EHR (Epic; Epic Systems Corporation),
which was implemented in 2017 [4]. Roughly 850,000 new
documents are added daily.
To identify and quickly represent the most critical information
about a particular patient or population from this large data set,
VUMC established the Word Cloud, a real-time and at-scaleconcept extraction tool that uses NLP to create a visual,
JMIR Med Inform 2024 | vol. 12 | e53516 | p. 1
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
time-oriented representation of clinical data [5-7]. The Word
Cloud NLP uses a rules-based, finite-state machine approachto process all nonimage incoming documents in real time andextract coded concepts using the Unified Medical Language
System (UMLS) terminology [8]. With a processing speed ofmore than 50,000 documents per minute, the Word Cloud NLP
is faster than currently available concept extraction NLP tools
such as Apache cTakes (50,000 documents per hour; Apache
Software Foundation, Mayo Clinic) [9] and MetaMap (22citations per minute; National Library of Medicine) [10]. The
rapid speed allows for better integration into the clinicalworkflow as real time–generated Word Cloud concepts are
immediately presented to health care providers as they accessthe feature in the medical chart. The system handles all linguistic
phenomena in clinical text, including acronyms, abbreviations,
misspellings, negation, family history, uncertainty, anddifferential diagnosis. Excluding image data, the entire EHR
repository is included in the Word Cloud NLP database, whichuses close to 14,000 UMLS concepts to index 1.7 billion
documents. In addition to the individual patient concepts, which
include pointers to the original documents, the database also
includes population-level associations of any pair of concepts.
The original purpose of the Word Cloud data was to provide auser interface that displays all concepts extracted from apatient’s clinical documents in a word cloud display, with thesize of each concept indicating how often the concept was
documented for the patient. This interface is available to allusers of the EHR. The Word Cloud data have been used since2019 to generate clinical alerts for a variety of situations, suchas flagging patients with implanted cardiac devices and apositive blood culture, patients with signs of serious
inflammation due to immune checkpoint inhibitors, or patientswith Andersen-Tawil syndrome who might be candidates forenrollment into a research study. The Word Cloud data drive
real-time decision support by injecting detected concepts backinto the VUMC EHR [11]. Because all the concepts extracted
by the Word Cloud NLP are stored in the enterprise data lake,these data are also available for retrospective research and canbe easily combined with other data such as the International
Statistical Classification of Diseases codes or coded medications
data [11].
The Center for Knowledge Management (CKM) has explored
how the Word Cloud can be leveraged by information scientists
engaged in EHR projects. The CKM facilitates the discovery
and integration of external knowledge into medical practice andpromotes curation, archiving, and reuse of internal knowledge
across VUMC [12-15]. This viewpoint paper details how the
CKM’s innovative application of the Word Cloud enhances
knowledge generation processes and describes future directions
for NLP in knowledge management.

Case Description
Collaborations with medical center researchers comprise themajority of the CKM’s partnership activities. A recent project

https://medinform.jmir.org/2024/1/e53516

XSL• FO
RenderX

Koonce et al
to inform the interpretation of phenome-wide association studies(PheWASs) using evidence-linked knowledge bases illustrates
these types of partnerships [16]. PheWASs examine
relationships between markers (genetic or nongenetic) andphenotypes, producing extensive lists of possibly relevantmarker-phenotype associations [17,18]. A methodologicalapproach to compare known associations with PheWAS results
can make it easier to identify potentially novel PheWAS
outcomes [16]. Knowledge bases—created in part fromsynthesized evidence sources and primary literature
documenting disease causes, risks, and complications—can beused for these comparisons.
For this research collaboration, the CKM created a “condition
flowchart” with the causes, risk factors, and complications ofa given medical condition. The sources consulted to create theflowchart include evidence synthesis resources (eg, UpToDate;
UpToDate, Inc), medical textbooks (eg, Goldman-Cecil
Medicine), and consumer health websites (eg, MedlinePlus;
National Library of Medicine). From each source, the CKM
team identified all causes, risk factors, and complications forthe condition of interest and added them to the flowchart. Our
collaborators then used the flowchart to create a knowledge
base of phecodes for the PheWAS analysis. During flowchart
creation, the CKM leveraged the Word Cloud to identify
meaningful disease-condition associations—based on real-world
population-level data—and target appropriate primary literature
to substantiate the observed linkages.

Identifying Meaningful Condition
Associations From the EHR
Each flowchart focuses on a specific clinical condition (eg,hypertension and hypotension), which is searched against theWord Cloud. Using a population-level analysis feature, theWord Cloud returns a list of all UMLS concepts represented in
the EHR records of patients with the specified condition. The
expected value is calculated for each UMLS concept [19] andthe ratio of actual-to-expected patient cases (ie, strength ofassociation) is then used to rank the list of causes, risk factors,
and complications on the flowchart. This ranking thus provides
our team with rapid knowledge acquisition of what is associatedwith the condition of interest. The actual-to-expected ratio forconcept 1 and concept 2 is computed as follows:

where T=total population size, a1,2=number of patients withboth concept 1 and concept 2, n1=number of patients withconcept 1, and n2=number of patients with concept 2.
A strength of association ratio of 15 or higher indicates that theconcept occurs more often than expected by chance and signifies
a meaningful relationship between the UMLS concept and thecondition. Figure 1 provides an example of the UMLS concepts
most associated with orthostatic hypotension in 71,996 patients.

JMIR Med Inform 2024 | vol. 12 | e53516 | p. 2
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Koonce et al

Figure 1. Snapshot of the Word Cloud population-level list of UMLS concepts associated with orthostatic hypotension. Concepts are listed in descendingorder by the strength-of-association ratio, that is, the ratio of actual to expected number of cases in the VUMC EHR with the pairwise association of
UMLS concepts. The population frequency of each term is also displayed. The ratio is used to rank the condition flowchart. CUI: concept unique
identifier; EHR: electronic health record; Misc.: miscellaneous; MsgBskts: message baskets; NewRes: new results; Pop. Freq.: population frequency;
Pt.Chart: patient chart; Pt.Lists: patient lists; StNotes: Star Notes; UMLS: Unified Medical Language System; VUMC: Vanderbilt University Medical
Center; WhBoards: white boards.

The Word Cloud also aids in identifying concepts mostapplicable to guide the ranking by displaying each term’s UMLS
semantic type. In the UMLS Metathesaurus, each concept term
is assigned to 1 or more of 127 types in the vocabulary’s
hierarchical semantic network [20]. Semantic types most
relevant for comparison with the condition flowchart include
disease or syndrome, injury or poisoning, mental or behavioral
dysfunction, sign or symptom, finding, and congenitalabnormality. The Word Cloud provides a filter to exclude
concepts with semantic types nonrelevant to this task (eg,procedures).
The Word Cloud often lists multiple UMLS concepts that canbe grouped to correspond with a single term on the condition
flowchart. For example, the Word Cloud concepts associatedwith orthostatic hypotension include Shy-Drager syndrome,
multisystem degeneration of autonomic nervous system, andmultisystem atrophy (Figure 1). In 1998, Shy-Drager syndrome
was newly categorized as a multisystem atrophy and is no longer
the preferred term [21]; the UMLS also lists it as a narrower
concept of the term “multiple system atrophy” [8]. In the UMLS,
the relationship between “multiple system atrophy” and“multisystem degeneration of autonomic nervous system” isvaguely and imprecisely defined as an “RO” relationship type.
RO relationships are described as “other than synonymous,
narrower, or broader,” however, in this case, the RO
determination in the UMLS lacks the relationship attribute thatis normally included [8]. The phecodeX map, the term mapping
https://medinform.jmir.org/2024/1/e53516

XSL• FO
RenderX

table used for the CKM collaborator’s PheWAS, matches“multisystem atrophy” to the phecode “multi-systemdegeneration of the autonomic nervous system” [22]. Given theevolution of the Shy-Drager syndrome terminology, the UMLS,
and the phecodeX mapping, we subsequently considered all 3of the Word Cloud concepts as a group of related terms; thehighest ratio within the group was then used to rank order thecondition flowchart.
Through the combined processes of documenting
actual-to-expected case ratios of the Word Cloud’s relevant
UMLS concepts, excluding nonrelevant semantic types, andgrouping related concepts, our team creates rank-ordered lists
of disease causes, risk factors, and complications reflecting our
medical center’s real-world clinical data.

Substantiating Disease-Condition
Associations With Evidence
Providing primary literature to substantiate the associations on
the condition flowchart is a key component of our researchcollaboration. CKM information scientists derive synonyms
from the Word Cloud to strengthen the search strategy. When
conducting a search, they first compile controlled vocabulary
and synonyms from Medical Subject Headings and Emtree[23,24]. Next, they brainstorm additional permutations andextract terms from a scan of the literature; these keywords aresubsequently checked for inclusion in the PubMed phrase index.
JMIR Med Inform 2024 | vol. 12 | e53516 | p. 3
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
Figure 2 shows an example of this process for the UMLS
concept “gastrointestinal bleeding.” Consulting the Word Cloud
identified 4 phrases that were not in the initial list of search

Koonce et al
strategy terms; 3 were found in the PubMed phrase index.
Additional terminology was found by scanning collocated terms
in the phrase index.

Figure 2. Word Cloud concepts leading to supplemental terminology for a search strategy on gastrointestinal hemorrhage. An asterisk denotes thetruncation of a term or phrase to capture permutations. GI: gastrointestinal; MeSH: Medical Subject Headings; UGI: upper gastrointestinal.

In addition to aiding with identifying terminology and concepts
to build upon our search strategies, we increasingly realize theimportance of the Word Cloud’s actual-to-expected patient caseratio for locating appropriate evidence. When creating thecondition flowchart, our team may encounter associations forwhich it is difficult to locate substantiating evidence. In thesecases, a Word Cloud ratio that is nonexistent, or lower than 15,
can aid in validating literature scarcity. For example, snake bites
can lead to nonseptic distributive shock [25]. In the Word Cloud,the association between snake bite and distributive shock has
a low ratio of 5.38. Substantiating evidence for the association
was found only in case reports and case series (ie, studies withfew patients). Similarly, searching for literature to support
hypertrophic cardiomyopathy as a cause of obstructive shockyielded only case reports as the best available evidence. A
review of the Word Cloud UMLS concepts revealed a ratio of
8.7. In these instances, the evidence may still be used, but the
low ranking, due to the low ratio, aids in understanding thestrength of association when compared with other causes, risk
factors, and complications listed on the condition flowchart.

Conclusions
This viewpoint paper describes a novel use of an institution’s
AI-driven, large-scale aggregation of condition-specific patient
data extracted from free-text clinical documents. The Word
Cloud NLP system can inform and guide knowledge generation
processes by enhancing our ability to represent, substantiate,
and prioritize condition associations for use in PheWAS
interpretation.
The VUMC Word Cloud NLP is a valuable resource thatprovides real-time concept extraction from all clinicaldocumentation and makes the resulting data viewableinteractively, available for real-time decision support andhttps://medinform.jmir.org/2024/1/e53516

XSL• FO
RenderX

alerting, and available as a rich source of coded data forresearch. An important limitation, however, is that this type ofresource would be expensive and difficult to port directly toother institutions, thus limiting its generalizability. The
emergence of generative AI, and in particular large language
models, makes it conceivable that some of these limitations
might be reduced in the near future; for example, large language
models might be used to perform a significant portion of theconcept extraction task, turning clinical free text into sets ofterms which might then be mapped to coded terminologies (suchas the UMLS). This possibility is still largely hypothetical andwill need to be investigated to evaluate whether it is feasible,
performant, and economically viable.
It is also worth noting that in addition to the population-level
analysis features offered by the Word Cloud as described in our
research collaboration for PheWAS analysis, the CKM alsouses its capability of providing summary views of individual
patient charts for other projects, such as our synthesized
evidence provision services [14,26]. In response to providers’complex clinical questions, information scientists consult thevisual display of the Word Cloud to gain a holistic understandingof each patient’s comorbidities, medications, and otherprominent clinical history. This greatly facilitates our ability togenerate tailored syntheses of the published evidence that are
personalized to each specific patient case [26]. Additional
applications of the Word Cloud and other AI tools are also under
exploration at our center, including the use of AI for scaling themaintenance of evidence syntheses over time [27-29]. Throughboth of these approaches—leveraging the Word Cloud NLP for
population-level concept analysis and individual patient-levelassessment—the CKM achieves the rapid knowledge acquisition
strategy critical for informing clinical health care and researchat our institution.
JMIR Med Inform 2024 | vol. 12 | e53516 | p. 4
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS

Koonce et al

Acknowledgments
This project did not receive any specific external funding.

Authors' Contributions
DAG and NBG wholly developed the work’s concept and design. TYK, DAG, AMW, and PAK contributed to the conduct of
the case study methods. TYK, DAG, AMW, MNB, PAK, JS, and NBG participated in the writing, editing, and critical reviewof this paper. AMW and MNB helped visualize the case study details. NBG provided oversight of case study activities.

Conflicts of Interest
None declared.

References
1.

2.
3.
4.
5.

6.

7.
8.
9.
10.
11.

12.

13.
14.

15.

16.
17.

18.

Hossain E, Rana R, Higgins N, Soar J, Barua PD, Pisani AR, et al. Natural language processing in electronic health records
in relation to healthcare decision-making: a systematic review. Comput Biol Med. 2023;155:106649. [doi:10.1016/j.compbiomed.2023.106649] [Medline: 36805219]
Robinson PN, Haendel MA. Ontologies, knowledge representation, and machine learning for translational research: recent
contributions. Yearb Med Inform. 2020;29(1):159-162. [FREE Full text] [doi: 10.1055/s-0040-1701991] [Medline: 32823310]
Giuse DA. Supporting communication in an integrated patient record system. AMIA Annu Symp Proc. 2003;2003:1065.
[FREE Full text] [Medline: 14728568]
Johnson KB, Ehrenfeld JM. An EPIC switch: preparing for an electronic health record transition at Vanderbilt University
Medical Center. J Med Syst. 2017;42(1):6. [FREE Full text] [doi: 10.1007/s10916-017-0865-6] [Medline: 29164347]
Madani S, Giuse D, McLemore M, Weitkamp A. Augmenting NLP results by leveraging SNOMED CT relationships for
identification of implantable cardiac devices from patient notes. SNOMED International; Presented at: SNOMED CT Expo;
Oct 31-Nov 1, 2019, 2019; Kuala Lumpur, Malaysia. URL: http://tinyurl.com/5awcneyr
Tan H, Giuse D, Kumah-Crystal Y. Usability of a word cloud visualization of the problem list. Washington, DC. American
Medical Informatics Association; Presented at: AMIA Clinical Informatics Conference; May 19-21, 2020, 2020; Virtual.
URL: https://brand.amia.org/m/1b1f63ea67b0c099/original/CIC2020-Visual-Abstract-Collection-FINAL.pdf
Krause KJ, Shelley J, Becker A, Walsh C. Exploring risk factors in suicidal ideation and attempt concept cooccurrence
networks. AMIA Annu Symp Proc. 2023;2022:644-652. [FREE Full text] [Medline: 37128429]
Bodenreider O. The Unified Medical Language System (UMLS): integrating biomedical terminology. Nucleic Acids Res.
2004;32(Database issue):D267-D270. [FREE Full text] [doi: 10.1093/nar/gkh061] [Medline: 14681409]
Apache cTAKES. Apache Software Foundation. URL: https://ctakes.apache.org/ [accessed 2023-12-13]
MetaMap speed. National Library of Medicine. URL: https://lhncbc.nlm.nih.gov/ii/tools/MetaMap/Docs/Speed.pdf [accessed2023-12-13]
Albert D, Weitkamp AO, Giuse D, Wright A. Building a pipeline for clinical alerts generated via natural language processing.
Presented at: American Medical Informatics Association Annual Symposium; 2022 Nov, 2022; Washington, DC. URL:
https://knowledge.amia.org/event-data/
esearch?q=Building%20a%20pipeline%20for%20clinical%20alerts%20generated%20via%20natural%20language%20processing&size=n_20_n
Giuse NB, Kusnoor SV, Koonce TY, Ryland CR, Walden RR, Naylor HM, et al. Strategically aligning a mandala ofcompetencies to advance a transformative vision. J Med Libr Assoc. 2013;101(4):261-267. [FREE Full text] [doi:10.3163/1536-5050.101.4.007] [Medline: 24163597]
Giuse NB, Koonce TY, Jerome RN, Cahall M, Sathe NA, Williams A. Evolution of a mature clinical informationist model.
J Am Med Inform Assoc. 2005;12(3):249-255. [FREE Full text] [doi: 10.1197/jamia.M1726] [Medline: 15684125]
Blasingame MN, Williams AM, Su J, Naylor HM, Koonce TY, Epelbaum MI, et al. Bench to bedside: detailing the catalyticroles of fully integrated information scientists. Mount Laurel, NJ. Special Libraries Association; Presented at: Special
Libraries Association Annual Meeting; June 18, 2019, 2019; Cleveland, OH. URL: https://www.sla.org/learn-2/research/sla-contributed-papers/2019-contributed-papers/
Giuse NB, Williams AM, Giuse DA. Integrating best evidence into patient care: a process facilitated by a seamless integration
with informatics tools. J Med Libr Assoc. 2010;98(3):220-222. [FREE Full text] [doi: 10.3163/1536-5050.98.3.009][Medline: 20648255]
Stead WW, Lewis A, Giuse NB, Koonce TY, Bastarache L. Knowledgebase strategies to aid interpretation of clinicalcorrelation research. J Am Med Inform Assoc. 2023;30(7):1257-1265. [doi: 10.1093/jamia/ocad078] [Medline: 37164621]
Denny JC, Ritchie MD, Basford MA, Pulley JM, Bastarache L, Brown-Gentry K, et al. PheWAS: demonstrating thefeasibility of a phenome-wide scan to discover gene-disease associations. Bioinformatics. 2010;26(9):1205-1210. [FREEFull text] [doi: 10.1093/bioinformatics/btq126] [Medline: 20335276]
Bastarache L, Denny JC, Roden DM. Phenome-wide association studies. JAMA. 2022;327(1):75-76. [FREE Full text] [doi:10.1001/jama.2021.20356] [Medline: 34982132]

https://medinform.jmir.org/2024/1/e53516

XSL• FO
RenderX

JMIR Med Inform 2024 | vol. 12 | e53516 | p. 5
(page number not for citation purposes)

JMIR MEDICAL INFORMATICS
19.
20.
21.
22.
23.
24.
25.

26.

27.

28.

29.

Koonce et al

Bland M. An Introduction to Medical Statistics, 4th Edition. Oxford, UK. Oxford University Press; 2015.
Semantic network. UMLS® Reference Manual. Bethesda, MD. National Library of Medicine; 2009. URL: https://www.
ncbi.nlm.nih.gov/books/NBK9679/ [accessed 2023-12-13]
Fecek C, Nagalli S. Shy-Drager Syndrome. Treasure Island, FL. StatPearls Publishing; 2023. URL: https://www.
ncbi.nlm.nih.gov/books/NBK560502/ [accessed 2023-12-13]
PhecodeX (Extended). PheWAS Resources. URL: https://phewascatalog.org/phecode_x [accessed 2023-12-13]
Medical Subject Headings (MeSH). National Library of Medicine. 2023. URL: https://www.nlm.nih.gov/mesh/meshhome.
html [accessed 2023-12-13]
Emtree. Elsevier. URL: https://www.elsevier.com/products/embase/emtree [accessed 2024-01-10]
Gaieski DF, Mikkelsen ME. Definition, classification, etiology, and pathophysiology of shock in adults. UpToDate. 2023.
URL: https://www.uptodate.com/contents/definition-classification-etiology-and-pathophysiology-of-shock-in-adults[accessed 2023-12-13]
Koonce TY, Giuse DA, Blasingame MN, Su J, Williams AM, Biggerstaff PL, et al. The personalization of evidence: using
intelligent datasets to inform the process. Washington, DC. American Medical Informatics Association; Presented at: AMIA
Fall Symposium; November 2020, 2020; Virtual.
Koonce TY, Blasingame MN, Williams AW, Clark JD, DesAutels SJ, Giuse DA, et al. Building a scalable knowledge
management approach to support evidence provision for precision medicine. Washington, DC. American Medical Informatics
Association; Presented at: AMIA Informatics Summit; March 2022, 2022; Chicago, IL.
Blasingame MN, Su J, Zhao J, Clark JD, Koonce TY, Giuse NB. Using a semi-automated approach to update clinicalgenomics evidence summaries. Chicago, IL. Medical Library Association; Presented at: Medical Library Association andSpecial Libraries Association Annual Meeting; May 2023, 2023; Detroit, MI.
Su J, Blasingame MN, Zhao J, Clark JD, Koonce TY, Giuse NB. Using a performance comparison to evaluate four distinct
AI-assisted citation screening tools. Chicago, IL. Medical Library Association; Presented at: Medical Library Association
Annual Meeting; May 2024, 2024; Portland, OR.

Abbreviations
AI: artificial intelligence
CKM: Center for Knowledge Management
EHR: electronic health record
NLP: natural language processing
PheWAS: phenome-wide association study
UMLS: Unified Medical Language System
VUMC: Vanderbilt University Medical Center

Edited by G Eysenbach, C Lovis; submitted 10.10.23; peer-reviewed by P Han, D Chrimes; comments to author 08.12.23; revised
version received 15.12.23; accepted 04.01.24; published 30.01.24
Please cite as:
Koonce TY, Giuse DA, Williams AM, Blasingame MN, Krump PA, Su J, Giuse NB
Using a Natural Language Processing Approach to Support Rapid Knowledge Acquisition
JMIR Med Inform 2024;12:e53516
URL: https://medinform.jmir.org/2024/1/e53516
doi: 10.2196/53516
PMID: 38289670©Taneya Y Koonce, Dario A Giuse, Annette M Williams, Mallory N Blasingame, Poppy A Krump, Jing Su, Nunzia B Giuse.
Originally published in JMIR Medical Informatics (https://medinform.jmir.org), 30.01.2024. This is an open-access article
distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which
permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR
Medical Informatics, is properly cited. The complete bibliographic information, a link to the original publication on
https://medinform.jmir.org/, as well as this copyright and license information must be included.

https://medinform.jmir.org/2024/1/e53516

XSL• FO
RenderX

JMIR Med Inform 2024 | vol. 12 | e53516 | p. 6
(page number not for citation purposes)