A Qualitative Study of Physicians' Views on the Reuse of Electronic Health Record Data for Secondary Analysis.

Electronic health records (EHRs) have become ubiquitous in clinical practice. Given the rich biomedical data captured for a large panel of patients, secondary analysis of these data for health research is also commonplace. Yet, there are many caveats to EHR data that the researchers must be aware of, such as the accuracy of and motive for documentation, and the reason for patients' visits to the clinic. The clinician-the author of the documentation-is thus central to the correct interpretation of EHR data for research purposes. In this study, I interviewed 11 physicians in various clinical specialties to bring attention to their view on the validity of research using EHR data. Qualitative, in-depth, one-on-one interviews were conducted with practicing physicians in inpatient and outpatient medicine. Content analysis using a data-driven, inductive approach to identify themes related to challenges and opportunities in the reuse of EHR data for secondary analysis generated seven themes. Themes that reflected challenges of EHRs for research included (1) audience, (2) accuracy of data, (3) availability of data, (4) documentation practices, and (5) representativeness. Themes that reflected opportunities of EHRs for research included (6) endorsement and (7) enablers. The greatest perceived barriers reflected the intended audience of the EHR, the interpretation and meaning of the data, and the quality of the data for research purposes. Physicians generally expressed more perceived challenges than opportunities in the reuse of EHR data for research purposes; however, they remained optimistic.

Lower comorbidity scores and severity levels in Veterans Health Administration hospitals: a cross-sectional study.

BACKGROUND: Previous studies found that documentation of comorbidities differed when Veterans received care within versus outside Veterans Health Administration (VHA). Changes to medical center funding, increased attention to performance reporting, and expansion of Clinical Documentation Improvement programs, however, may have caused coding in VHA to change. METHODS: Using repeated cross-sectional data, we compared Elixhauser-van Walraven scores and Medicare Severity Diagnosis Related Group (DRG) severity levels for Veterans' admissions across settings and payers over time, utilizing a linkage of VHA and all-payer discharge data for 2012-2017 in seven US states. To minimize selection bias, we analyzed records for Veterans admitted to both VHA and non-VHA hospitals in the same year. Using generalized linear models, we adjusted for patient and hospital characteristics. RESULTS: Following adjustment, VHA admissions consistently had the lowest predicted mean comorbidity scores (4.44 (95% CI 4.34-4.55)) and lowest probability of using the most severe DRG (22.1% (95% CI 21.4%-22.8%)). In contrast, Medicare-covered admissions had the highest predicted mean comorbidity score (5.71 (95% CI 5.56-5.85)) and highest probability of using the top DRG (35.3% (95% CI 34.2%-36.4%)). CONCLUSIONS: More effective strategies may be needed to improve VHA documentation, and current risk-adjusted comparisons should account for differences in coding intensity.

Charting the Future: The Role of AI in Transforming Nursing Documentation.

This editorial delves into the integration of artificial intelligence (AI) into nursing documentation, emphasizing its potential to streamline workflows, reduce human error, and enhance patient care. AI technologies, notably natural language processing and decision support systems, present opportunities to automate tedious documentation tasks and enhance record accuracy. However, their adoption raises ethical considerations, such as privacy, bias, and accountability. Striking a balance between technological advancements and ethical imperatives is pivotal to harnessing the benefits of AI while safeguarding patient safety and upholding professional integrity in nursing practice. Advocating for ongoing evaluation, regulation, and education is crucial to ensure the responsible integration of AI into nursing documentation. This approach aims to improve patient outcomes and maintain the high standards of the nursing profession.

Teaching Research Data Management with DataLad: A Multi-year, Multi-domain Effort.

Research data management has become an indispensable skill in modern neuroscience. Researchers can benefit from following good practices as well as from having proficiency in using particular software solutions. But as these domain-agnostic skills are commonly not included in domain-specific graduate education, community efforts increasingly provide early career scientists with opportunities for organised training and materials for self-study. Investing effort in user documentation and interacting with the user base can, in turn, help developers improve quality of their software. In this work, we detail and evaluate our multi-modal teaching approach to research data management in the DataLad ecosystem, both in general and with concrete software use. Spanning an online and printed handbook, a modular course suitable for in-person and virtual teaching, and a flexible collection of research data management tips in a knowledge base, our free and open source collection of training material has made research data management and software training available to various different stakeholders over the past five years.

Variable Impact of Medical Scribes on Physician Electronic Health Record Documentation Practices: A Quantitative Analysis Across a Large, Integrated Health-System.

BACKGROUND: Medical scribes have been utilized to reduce electronic health record (EHR) associated documentation burden. Although evidence suggests benefits to scribes, no large-scale studies have quantitatively evaluated scribe impact on physician documentation across clinical settings. This study aimed to evaluate the effect of scribes on physician EHR documentation behaviors and performance. METHODS: This retrospective cohort study used EHR audit log data from a large academic health system to evaluate clinical documentation for all ambulatory encounters between January 2014 and December 2019 to evaluate the effect of scribes on physician documentation behaviors. Scribe services were provided on a first-come, first-served basis on physician request. Based on a physician's scribe use, encounters were grouped into 3 categories: never using a scribe, prescribe (before scribe use), or using a scribe. Outcomes included chart closure time, the proportion of delinquent charts, and charts closed after-hours. RESULTS: Three hundred ninety-five physicians (23% scribe users) across 29 medical subspecialties, encompassing 1,132,487 encounters, were included in the analysis. At baseline, scribe users had higher chart closure time, delinquent charts, and after-hours documentation than physicians who never used scribes. Among scribe users, the difference in outcome measures postscribe compared with baseline varied, and using a scribe rarely resulted in outcome measures approaching a range similar to the performance levels of nonusing physicians. In addition, there was variability in outcome measures across medical specialties and within similar subspecialties. CONCLUSION: Although scribes may improve documentation efficiency among some physicians, not all will improve EHR-related documentation practices. Different strategies may help to optimize documentation behaviors of physician-scribe dyads and maximize outcomes of scribe implementation.

Documentation and management of paediatric obesity: Evidence from a paediatric inpatient unit.

INTRODUCTION: The obesity epidemic is a worldwide phenomenon.1 In Australia, the prevalence of paediatric overweight or obesity is 25%.2 Children with obesity present to medical services more frequently than children with a healthy weight.3 Therefore, any hospital admission is an opportunity for clinicians to identify and manage children with overweight or obesity. Previous research has not objectively measured how frequently clinicians document a child as being above the healthy weight range and initiate weight management strategies. This study addresses this gap in the literature by demonstrating the prevalence rate and clinical characteristics of children with overweight/obesity in a non-tertiary paediatric inpatient unit and measuring the rate of clinician recognition, documentation, and initiation of weight management strategies.

Clinical photography and documentation after traumatic dental injuries.

Clinical photographic documentation is recommended as part of the standardized clinical evaluation of traumatized patients according to the most current International Association of Dental Traumatology guidelines for the management of traumatic dental injuries (TDIs), published in 2020. The use of current technology such as mobile smartphones and the emergence of teledentistry for direct communication between dentists and with patients have increased the need to improve the knowledge and skills for contemporary clinical photographic documentation procedures at the dental office as well as at the accident site. The purpose of this review is to include findings from the available literature and discuss modern techniques, contemporary equipment, accessories and developments that can be used by both patients and dental professionals for proper clinical documentation after TDIs. Emphasis is given on the positioning and patient management based on the type and severity of the injury, and the selection of the appropriate technique. Moreover, the number and type of clinical photographs for each dental trauma scenario, the suggested timeline for optimal photographic documentation as well as legal considerations involved are also discussed.

Improving the Prognostic Evaluation Precision of Hospital Outcomes for Heart Failure Using Admission Notes and Clinical Tabular Data: Multimodal Deep Learning Model.

BACKGROUND: Clinical notes contain contextualized information beyond structured data related to patients' past and current health status. OBJECTIVE: This study aimed to design a multimodal deep learning approach to improve the evaluation precision of hospital outcomes for heart failure (HF) using admission clinical notes and easily collected tabular data. METHODS: Data for the development and validation of the multimodal model were retrospectively derived from 3 open-access US databases, including the Medical Information Mart for Intensive Care III v1.4 (MIMIC-III) and MIMIC-IV v1.0, collected from a teaching hospital from 2001 to 2019, and the eICU Collaborative Research Database v1.2, collected from 208 hospitals from 2014 to 2015. The study cohorts consisted of all patients with critical HF. The clinical notes, including chief complaint, history of present illness, physical examination, medical history, and admission medication, as well as clinical variables recorded in electronic health records, were analyzed. We developed a deep learning mortality prediction model for in-hospital patients, which underwent complete internal, prospective, and external evaluation. The Integrated Gradients and SHapley Additive exPlanations (SHAP) methods were used to analyze the importance of risk factors. RESULTS: The study included 9989 (16.4%) patients in the development set, 2497 (14.1%) patients in the internal validation set, 1896 (18.3%) in the prospective validation set, and 7432 (15%) patients in the external validation set. The area under the receiver operating characteristic curve of the models was 0.838 (95% CI 0.827-0.851), 0.849 (95% CI 0.841-0.856), and 0.767 (95% CI 0.762-0.772), for the internal, prospective, and external validation sets, respectively. The area under the receiver operating characteristic curve of the multimodal model outperformed that of the unimodal models in all test sets, and tabular data contributed to higher discrimination. The medical history and physical examination were more useful than other factors in early assessments. CONCLUSIONS: The multimodal deep learning model for combining admission notes and clinical tabular data showed promising efficacy as a potentially novel method in evaluating the risk of mortality in patients with HF, providing more accurate and timely decision support.

Call me Dr Ishmael: trends in electronic health record notes available at emergency department visits and admissions.

Objectives: Numerous studies have identified information overload as a key issue for electronic health records (EHRs). This study describes the amount of text data across all notes available to emergency physicians in the EHR, trended over the time since EHR establishment. Materials and Methods: We conducted a retrospective analysis of EHR data from a large healthcare system, examining the number of notes and a corresponding number of total words and total tokens across all notes available to physicians during patient encounters in the emergency department (ED). We assessed the change in these metrics over a 17-year period between 2006 and 2023. Results: The study cohort included 730 968 ED visits made by 293 559 unique patients and a total note count of 132 574 964. The median note count for all encounters in 2006 was 5 (IQR 1-16), accounting for 1735 (IQR 447-5521) words. By the last full year of the study period, 2022, the median number of notes had grown to 359 (IQR 84-943), representing 359 (IQR 84-943) words. Note and word counts were higher for admitted patients. Discussion: The volume of notes available for review by providers has increased by over 30-fold in the 17 years since the implementation of the EHR at a large health system. The task of reviewing these notes has become commensurately more difficult. These data point to the critical need for new strategies and tools for filtering, synthesizing, and summarizing information to achieve the promise of the medical record.

Sexual History Documentation in Pediatric Patients with Technology Dependence.

Adolescent sexual health documentation reflects the depth of discussion physicians conduct with their patients. Limited studies exist on rates of sexual history documentation in technology-dependent patients. We sought to identify whether treatment gaps exist in a technology-dependent adolescent population. Well-child visits for patients with enterotomy or tracheostomy dependence, 12 to 19 years age, and seen in our urban clinic over a 3-year period (n = 14) were compared with a randomly selected peer group without technology dependence. Documentation of sexual activity, sexual orientation, safe sex and sexually transmitted infection (STI) guidance, and contraception or condom use were noted. Results demonstrate lower rates of documentation in sexual orientation, safe sex and STI guidance, and contraception or condom use in patients with technology dependence when compared with patients without technology dependence. Similar differences were noted in the 16- to 19-year group between the experimental and control groups. No differences were noted between gender and age groups.

Experiences of Electronic Health Records' and Client Information Systems' Use on a Mobile Device and Factors Associated With Work Time Savings Among Practical Nurses: Cross-Sectional Study.

BACKGROUND: The transmission of clinical information in nursing predominantly occurs through digital solutions, such as computers and mobile devices, in today's era. Various technological systems, including electronic health records (EHRs) and client information systems (CISs), can be seamlessly integrated with mobile devices. The use of mobile devices is anticipated to rise, particularly as long-term care is increasingly delivered in environments such as clients' homes, where computers are not readily accessible. However, there is a growing need for more user-centered data to ensure that mobile devices effectively support practical nurses in their daily activities. OBJECTIVE: This study aims to analyze practical nurses' experiences of using EHRs or CISs on a mobile device in their daily practice. In addition, it aims to examine the factors associated with work time savings when using EHRs/CISs on a mobile device. METHODS: A cross-sectional study using an electronic survey was conducted in spring 2022. A total of 3866 practical nurses participated in the survey based on self-assessment. The sample was limited to practical nurses who used EHRs or CISs on a mobile device and worked in home care or service housing within the social welfare or health care sector (n=1014). Logistic regression analysis was used to explore the factors associated with work time savings. RESULTS: The likelihood of perceiving work time savings was higher among more experienced EHR/CIS users compared with those with less experience (odds ratio [OR] 1.59, 95% CI 1.30-1.94). Participants with 0-5 years of work experience were more likely to experience work time savings compared with those who had worked 21 years or more (OR 2.41, 95% CI 1.43-4.07). Practical nurses in home care were also more likely to experience work time savings compared with those working in service housing (OR 1.95, 95% CI 1.23-3.07). A lower grade given for EHRs/CISs was associated with a reduced likelihood of experiencing work time savings (OR 0.76, 95% CI 0.66-0.89). Participants who documented client data in a public area were more likely to experience work time savings compared with those who did so in the nurses' office (OR 2.33, 95% CI 1.27-4.25). Practical nurses who found documentation of client data on a mobile device easy (OR 3.05, 95% CI 2.14-4.34) were more likely to experience work time savings compared with those who did not. Similarly, participants who believed that documentation of client data on a mobile device reduced the need to memorize things (OR 4.10, 95% CI 2.80-6.00) were more likely to experience work time savings compared with those who did not. CONCLUSIONS: To enhance the proportion of practical nurses experiencing work time savings, we recommend that organizations offer comprehensive orientation and regular education sessions tailored for mobile device users who have less experience using EHRs or CISs and find mobile devices less intuitive to use.

On-farm crop diversity, conservation, importance and value: a case study of landraces from Western Ghats of Karnataka, India.

Landraces are important genetic resources that have a significant role in maintaining the long-term sustainability of traditional agro-ecosystems, food, nutrition, and livelihood security. In an effort to document landraces in the on-farm conservation context, Central Western Ghat region in India was surveyed. A total of 671 landraces belonging to 60 crops were recorded from 24 sites. The custodian farmers were found to conserve a variety of crops including vegetables, cereals and pulses, perennial fruits, spices, tuber and plantation crops. The survey indicated a difference in the prevalence of landraces across the sites. A significant difference with respect to the Shannon-diversity index, Gini-Simpson index, evenness, species richness, and abundance was observed among the different survey sites. Computation of a prevalence index indicated the need for immediate intervention in the form of collecting and ex situ conservation of landraces of some crops as a back-up to on-farm conservation. The study also identified the critical determinants of on-farm conservation, including (i) suitability to regional conditions, (ii) relevance in regional cuisine and local medicinal practices, (iii) cultural and traditional significance, and (iv) economic advantage. The information documented in this study is expected to promote the collection and conservation of landraces ex situ. The National Genebank housed at ICAR-NBPGR, New Delhi conserves around 550 accessions of landraces collected from the Central Western Ghats region surveyed in this report. Information collected from custodian farmers on specific uses will be helpful to enhance the utilization of these accessions.

Multi-Sensor Image and Range-Based Techniques for the Geometric Documentation and the Photorealistic 3D Modeling of Complex Architectural Monuments.

The selection of the optimal methodology for the 3D geometric documentation of cultural heritage is a subject of high concern in contemporary scientific research. As a matter of fact, it requires a multi-source data acquisition process and the fusion of datasets from different sensors. This paper aims to demonstrate the workflow for the proper implementation and integration of geodetic, photogrammetric and laser scanning techniques so that high-quality photorealistic 3D models and other documentation products can be generated for a complicated, large-dimensional architectural monument and its surroundings. As a case study, we present the monitoring of the Mehmet Bey Mosque, which is a landmark in the city of Serres and a significant remaining sample of the Ottoman architecture in Greece. The surveying campaign was conducted in the context of the 2022-2023 annual workshop of the Interdepartmental Program of Postgraduate Studies "Protection Conservation and Restoration of Cultural Monuments" of the Aristotle University of Thessaloniki, and it served as a geometric background for interdisciplinary cooperation and decision-making on the monument restoration process. The results of our study encourage the fusion of terrestrial laser scanning and photogrammetric datasets for the 3D modeling of the mosque, as they supplement each other as regards geometry and texture.

Use of the supportive and palliative care indicators tool (SPICT™) for end-of-life discussions: a scoping review.

BACKGROUND: In order to mitigate the distress associated with life limiting conditions it is essential for all health professionals not just palliative care specialists to identify people with deteriorating health and unmet palliative care needs and to plan care. The SPICT™ tool was designed to assist with this. AIM: The aim was to examine the impact of the SPICT™ on advance care planning conversations and the extent of its use in advance care planning for adults with chronic life-limiting illness. METHODS: In this scoping review records published between 2010 and 2024 reporting the use of the SPICT™, were included unless the study aim was to evaluate the tool for prognostication purposes. Databases searched were EBSCO Medline, PubMed, EBSCO CINAHL, APA Psych Info, ProQuest One Theses and Dissertations Global. RESULTS: From the search results 26 records were reviewed, including two systematic review, two theses and 22 primary research studies. Much of the research was derived from primary care settings. There was evidence that the SPICT™ assists conversations about advance care planning specifically discussion and documentation of advance care directives, resuscitation plans and preferred place of death. The SPICT™ is available in at least eight languages (many versions have been validated) and used in many countries. CONCLUSIONS: Use of the SPICT™ appears to assist advance care planning. It has yet to be widely used in acute care settings and has had limited use in countries beyond Europe. There is a need for further research to validate the tool in different languages.

Embracing Artificial Intelligence: Revolutionizing Nursing Documentation for a Better Future.

Nursing documentation stands as a critical aspect of healthcare delivery, ensuring comprehensive patient records and facilitating communication among healthcare providers. However, traditional documentation methods are often time-consuming and prone to errors, diverting nurses' attention from direct patient care. This editorial explores the transformative potential of artificial intelligence (AI) in revolutionizing nursing documentation processes. By leveraging AI-driven technologies, such as natural language processing and machine learning, healthcare organizations can automate data entry, extract key clinical information, and generate personalized care plans, thereby streamlining workflows and improving documentation accuracy. This editorial also examines various AI-powered software applications and platforms that facilitate nursing documentation, highlighting their benefits in terms of efficiency, accuracy, and clinical decision support. Furthermore, it discusses considerations such as privacy, security, and the need for nurse training to effectively integrate AI into nursing practice. By embracing AI in nursing documentation, healthcare organizations can empower nurses to devote more time to patient care while enhancing the quality and safety of healthcare delivery.

Do goals of care documentation reflect the conversation?: Evaluating conversation-documentation accuracy.

BACKGROUND: Documenting goals of care in the electronic health record is meant to relay patient preferences to other clinicians. Evaluating the content and documentation of nurse and social worker led goals of care conversations can inform future goals of care initiative efforts. METHODS: As part of the ADvancing symptom Alleviation with Palliative Treatment trial, this study analyzed goals of care conversations led by nurses and social workers and documented in the electronic health record. Informed by a goals of care communication guide, we identified five goals of care components: illness understanding, goals and values, end of life planning, surrogate, and advance directives. Forty conversation transcripts underwent content analysis. Through an iterative team process, we defined documentation accuracy as four categories: (1) Complete-comprehensive accurate documentation of the conversation, (2) Incomplete-partial documentation of the conversation, (3) Missing-discussed and not documented, and (4) Incorrect-misrepresented in documentation. We also defined-Not Discussed-for communication guide questions that were not discussed nor documented. A constant comparative approach was used to determine the presence or absence of conversation content in the documentation. RESULTS: All five goals of care components were discussed in 67% (27/40) of conversation transcripts. Compared to the transcripts, surrogate (37/40, 93%) and advance directives (36/40, 90%) were often documented completely. Almost 40% of goals and values (15/40, 38%) and half of end of life planning (19/40, 48%) were incomplete. Illness understanding was missing (13/40, 33%), not discussed (13/40, 33%), or incorrect (2/40, 5%). CONCLUSION: Nurse and social worker led goals of care conversations discussed and documented most components of the goals of care communication guide. Further research may guide how best to determine the relative importance of accuracy, especially in the broad setting of incomplete, missing, and incorrect EHR documentation.

A Comparison of Veterans with Problematic Opioid Use Identified through Natural Language Processing of Clinical Notes versus Using Diagnostic Codes.

Opioid use disorder is known to be under-coded as a diagnosis, yet problematic opioid use can be documented in clinical notes, which are included in electronic health records. We sought to identify problematic opioid use from a full range of clinical notes and compare the demographic and clinical characteristics of patients identified as having problematic opioid use exclusively in clinical notes to patients documented through ICD opioid use disorder diagnostic codes. We developed and applied a natural language processing (NLP) tool that combines rule-based pattern analysis and a trained support vector machine to the clinical notes of a patient cohort (n = 222,371) from two Veteran Affairs service regions to identify patients with problematic opioid use. We also used a set of ICD diagnostic codes to identify patients with opioid use disorder from the same cohort. The NLP tool achieved 96.6% specificity, 90.4% precision/PPV, 88.4% sensitivity/recall, and 94.4% accuracy on unseen test data. NLP exclusively identified 57,331 patients; 6997 patients had positive ICD code identifications. Patients exclusively identified through NLP were more likely to be women. Those identified through ICD codes were more likely to be male, younger, have concurrent benzodiazepine prescriptions, more comorbidities, and more care encounters, and were less likely to be married. Patients in both these groups had substantially elevated comorbidity levels compared with patients not documented through either method as experiencing problematic opioid use. Clinicians may be reluctant to code for opioid use disorder. It is therefore incumbent on the healthcare team to search for documentation of opioid concerns within clinical notes.

The Need for Standardized Nomenclature in Electronic Documentation of Female Genital Cutting in Health Records.

To evaluate the quality of Electronic Health Record (EHR) documentation practices of Female Genital Cutting (FGC) by medical providers. A retrospective chart review study of 99 patient encounter notes within the University of Minnesota health system (inclusive of 40 hospitals and clinics) was conducted. Extracted data included but was not limited to patient demographics, reason for patient visit, ICD code used in note, and provider description of FGC anatomy. Data was entered into REDCAP and categorized according to descriptive statistics. Out of 99 encounters, 45% used the unspecified code for FGC. The most common reason for patient visits was sexual pain, though many notes contained several reasons for the visit regarding reproductive, urological, or sexual concerns. 56% of visits discussed deinfibulation. 11 different terms for FGC were used, with "female circumcision" being the most common. 14 different terms for deinfibulation were found within 64 notes. 42% of encounters included a description of introitus size in the anatomical description, and only 38% of these provided a metric measurement. This study found significant variation in the quality of FGC documentation practices. Medical providers often used the unspecified FGC code, subjective and/or seemingly inaccurate descriptions of FGC/anatomy, and several different terms for both FGC and deinfibulation. Clearly, more education is needed in clinical training programs to (1) identify FGC type, (2) use the corresponding ICD code, and (3) use specific, objective descriptions (including presence/absence of structures and infibulation status).

Medical photography and the reconstructive urologist: A 6-month prospective study.

Medical photography has multiple, important roles. The education of medical practitioners, documentation of disease, response to treatment, research, publication, intraoperative recording and trauma documentation all rely on medical photography. Additionally, there are important medicolegal implications pertaining to medical photography across many medical disciplines. Other than specific image use to document cases, there remains a paucity of urological literature regarding the use of medical photography in Urology. The aims of this 6-month study were to document the use of medical photography by a Reconstructive Urological Service in a tertiary referral centre and to assess the range of urological conditions photographed. A secondary aim was to specifically document intraoperative use of the medical photography.

Moving Biosurveillance Beyond Coded Data Using AI for Symptom Detection From Physician Notes: Retrospective Cohort Study.

BACKGROUND: Real-time surveillance of emerging infectious diseases necessitates a dynamically evolving, computable case definition, which frequently incorporates symptom-related criteria. For symptom detection, both population health monitoring platforms and research initiatives primarily depend on structured data extracted from electronic health records. OBJECTIVE: This study sought to validate and test an artificial intelligence (AI)-based natural language processing (NLP) pipeline for detecting COVID-19 symptoms from physician notes in pediatric patients. We specifically study patients presenting to the emergency department (ED) who can be sentinel cases in an outbreak. METHODS: Subjects in this retrospective cohort study are patients who are 21 years of age and younger, who presented to a pediatric ED at a large academic children's hospital between March 1, 2020, and May 31, 2022. The ED notes for all patients were processed with an NLP pipeline tuned to detect the mention of 11 COVID-19 symptoms based on Centers for Disease Control and Prevention (CDC) criteria. For a gold standard, 3 subject matter experts labeled 226 ED notes and had strong agreement (F1-score=0.986; positive predictive value [PPV]=0.972; and sensitivity=1.0). F1-score, PPV, and sensitivity were used to compare the performance of both NLP and the International Classification of Diseases, 10th Revision (ICD-10) coding to the gold standard chart review. As a formative use case, variations in symptom patterns were measured across SARS-CoV-2 variant eras. RESULTS: There were 85,678 ED encounters during the study period, including 4% (n=3420) with patients with COVID-19. NLP was more accurate at identifying encounters with patients that had any of the COVID-19 symptoms (F1-score=0.796) than ICD-10 codes (F1-score =0.451). NLP accuracy was higher for positive symptoms (sensitivity=0.930) than ICD-10 (sensitivity=0.300). However, ICD-10 accuracy was higher for negative symptoms (specificity=0.994) than NLP (specificity=0.917). Congestion or runny nose showed the highest accuracy difference (NLP: F1-score=0.828 and ICD-10: F1-score=0.042). For encounters with patients with COVID-19, prevalence estimates of each NLP symptom differed across variant eras. Patients with COVID-19 were more likely to have each NLP symptom detected than patients without this disease. Effect sizes (odds ratios) varied across pandemic eras. CONCLUSIONS: This study establishes the value of AI-based NLP as a highly effective tool for real-time COVID-19 symptom detection in pediatric patients, outperforming traditional ICD-10 methods. It also reveals the evolving nature of symptom prevalence across different virus variants, underscoring the need for dynamic, technology-driven approaches in infectious disease surveillance.