Similar Risk of Kidney Failure among Patients with Blinding Diseases Who Receive Ranibizumab, Aflibercept, and Bevacizumab: An Observational Health Data Sciences and Informatics Network Study.

PURPOSE: To characterize the incidence of kidney failure associated with intravitreal anti-VEGF exposure; and compare the risk of kidney failure in patients treated with ranibizumab, aflibercept, or bevacizumab. DESIGN: Retrospective cohort study across 12 databases in the Observational Health Data Sciences and Informatics (OHDSI) network. SUBJECTS: Subjects aged ≥ 18 years with ≥ 3 monthly intravitreal anti-VEGF medications for a blinding disease (diabetic retinopathy, diabetic macular edema, exudative age-related macular degeneration, or retinal vein occlusion). METHODS: The standardized incidence proportions and rates of kidney failure while on treatment with anti-VEGF were calculated. For each comparison (e.g., aflibercept versus ranibizumab), patients from each group were matched 1:1 using propensity scores. Cox proportional hazards models were used to estimate the risk of kidney failure while on treatment. A random effects meta-analysis was performed to combine each database's hazard ratio (HR) estimate into a single network-wide estimate. MAIN OUTCOME MEASURES: Incidence of kidney failure while on anti-VEGF treatment, and time from cohort entry to kidney failure. RESULTS: Of the 6.1 million patients with blinding diseases, 37 189 who received ranibizumab, 39 447 aflibercept, and 163 611 bevacizumab were included; the total treatment exposure time was 161 724 person-years. The average standardized incidence proportion of kidney failure was 678 per 100 000 persons (range, 0-2389), and incidence rate 742 per 100 000 person-years (range, 0-2661). The meta-analysis HR of kidney failure comparing aflibercept with ranibizumab was 1.01 (95% confidence interval [CI], 0.70-1.47; P = 0.45), ranibizumab with bevacizumab 0.95 (95% CI, 0.68-1.32; P = 0.62), and aflibercept with bevacizumab 0.95 (95% CI, 0.65-1.39; P = 0.60). CONCLUSIONS: There was no substantially different relative risk of kidney failure between those who received ranibizumab, bevacizumab, or aflibercept. Practicing ophthalmologists and nephrologists should be aware of the risk of kidney failure among patients receiving intravitreal anti-VEGF medications and that there is little empirical evidence to preferentially choose among the specific intravitreal anti-VEGF agents. FINANCIAL DISCLOSURES: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Prediction of multiclass surgical outcomes in glaucoma using multimodal deep learning based on free-text operative notes and structured EHR data.

OBJECTIVE: Surgical outcome prediction is challenging but necessary for postoperative management. Current machine learning models utilize pre- and post-op data, excluding intraoperative information in surgical notes. Current models also usually predict binary outcomes even when surgeries have multiple outcomes that require different postoperative management. This study addresses these gaps by incorporating intraoperative information into multimodal models for multiclass glaucoma surgery outcome prediction. MATERIALS AND METHODS: We developed and evaluated multimodal deep learning models for multiclass glaucoma trabeculectomy surgery outcomes using both structured EHR data and free-text operative notes. We compare those to baseline models that use structured EHR data exclusively, or neural network models that leverage only operative notes. RESULTS: The multimodal neural network had the highest performance with a macro AUROC of 0.750 and F1 score of 0.583. It outperformed the baseline machine learning model with structured EHR data alone (macro AUROC of 0.712 and F1 score of 0.486). Additionally, the multimodal model achieved the highest recall (0.692) for hypotony surgical failure, while the surgical success group had the highest precision (0.884) and F1 score (0.775). DISCUSSION: This study shows that operative notes are an important source of predictive information. The multimodal predictive model combining perioperative notes and structured pre- and post-op EHR data outperformed other models. Multiclass surgical outcome prediction can provide valuable insights for clinical decision-making. CONCLUSIONS: Our results show the potential of deep learning models to enhance clinical decision-making for postoperative management. They can be applied to other specialties to improve surgical outcome predictions.

The Impact of Documentation Workflow on the Accuracy of the Coded Diagnoses in the Electronic Health Record.

Objective: To determine the impact of documentation workflow on the accuracy of coded diagnoses in electronic health records (EHRs). Design: Cross-sectional study. Participants: All patients who completed visits at the Casey Eye Institute Retina Division faculty clinic between April 7, 2022 and April 13, 2022. Main Outcome Measures: Agreement between coded diagnoses and clinical notes. Methods: We assessed the rate of agreement between the diagnoses in the clinical notes and the coded diagnosis in the EHR using manual review and examined the impact of the documentation workflow on the rate of agreement in an academic retina practice. Results: In 202 visits by 8 physicians, 78% (range, 22%-100%) had an agreement between the coded diagnoses and the clinical notes. When physicians integrated the diagnosis code entry and note composition, the rate of agreement was 87.9% (range, 62%-100%). For those who entered the diagnosis codes separately from writing notes, the agreement was 44.4% (22%-50%, P < 0.0001). Conclusion: The visit-specific agreement between the coded diagnosis and the progress note can vary widely by workflow. The workflow and EHR design may be an important part of understanding and improving the quality of EHR data. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Guidance for reporting analyses of metadata on electronic health record use.

INTRODUCTION: Research on how people interact with electronic health records (EHRs) increasingly involves the analysis of metadata on EHR use. These metadata can be recorded unobtrusively and capture EHR use at a scale unattainable through direct observation or self-reports. However, there is substantial variation in how metadata on EHR use are recorded, analyzed and described, limiting understanding, replication, and synthesis across studies. RECOMMENDATIONS: In this perspective, we provide guidance to those working with EHR use metadata by describing 4 common types, how they are recorded, and how they can be aggregated into higher-level measures of EHR use. We also describe guidelines for reporting analyses of EHR use metadata-or measures of EHR use derived from them-to foster clarity, standardization, and reproducibility in this emerging and critical area of research.

Advancing Toward a Common Data Model in Ophthalmology: Gap Analysis of General Eye Examination Concepts to Standard Observational Medical Outcomes Partnership (OMOP) Concepts.

Purpose: Evaluate the degree of concept coverage of the general eye examination in one widely used electronic health record (EHR) system using the Observational Health Data Sciences and Informatics Observational Medical Outcomes Partnership (OMOP) common data model (CDM). Design: Study of data elements. Participants: Not applicable. Methods: Data elements (field names and predefined entry values) from the general eye examination in the Epic foundation system were mapped to OMOP concepts and analyzed. Each mapping was given a Health Level 7 equivalence designation-equal when the OMOP concept had the same meaning as the source EHR concept, wider when it was missing information, narrower when it was overly specific, and unmatched when there was no match. Initial mappings were reviewed by 2 graders. Intergrader agreement for equivalence designation was calculated using Cohen's kappa. Agreement on the mapped OMOP concept was calculated as a percentage of total mappable concepts. Discrepancies were discussed and a final consensus created. Quantitative analysis was performed on wider and unmatched concepts. Main Outcome Measures: Gaps in OMOP concept coverage of EHR elements and intergrader agreement of mapped OMOP concepts. Results: A total of 698 data elements (210 fields, 488 values) from the EHR were analyzed. The intergrader kappa on the equivalence designation was 0.88 (standard error 0.03, P < 0.001). There was a 96% agreement on the mapped OMOP concept. In the final consensus mapping, 25% (1% fields, 31% values) of the EHR to OMOP concept mappings were considered equal, 50% (27% fields, 60% values) wider, 4% (8% fields, 2% values) narrower, and 21% (52% fields, 8% values) unmatched. Of the wider mapped elements, 46% were missing the laterality specification, 24% had other missing attributes, and 30% had both issues. Wider and unmatched EHR elements could be found in all areas of the general eye examination. Conclusions: Most data elements in the general eye examination could not be represented precisely using the OMOP CDM. Our work suggests multiple ways to improve the incorporation of important ophthalmology concepts in OMOP, including adding laterality to existing concepts. There exists a strong need to improve the coverage of ophthalmic concepts in source vocabularies so that the OMOP CDM can better accommodate vision research. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Identifying and Addressing Barriers to Implementing Core Electronic Health Record Use Metrics for Ambulatory Care: Virtual Consensus Conference Proceedings.

Precise, reliable, valid metrics that are cost-effective and require reasonable implementation time and effort are needed to drive electronic health record (EHR) improvements and decrease EHR burden. Differences exist between research and vendor definitions of metrics. PROCESS: We convened three stakeholder groups (health system informatics leaders, EHR vendor representatives, and researchers) in a virtual workshop series to achieve consensus on barriers, solutions, and next steps to implementing the core EHR use metrics in ambulatory care. CONCLUSION: Actionable solutions identified to address core categories of EHR metric implementation challenges include: (1) maintaining broad stakeholder engagement, (2) reaching agreement on standardized measure definitions across vendors, (3) integrating clinician perspectives, and (4) addressing cognitive and EHR burden. Building upon the momentum of this workshop's outputs offers promise for overcoming barriers to implementing EHR use metrics.

Development of an Open-Source Annotated Glaucoma Medication Dataset From Clinical Notes in the Electronic Health Record.

Purpose: To describe the methods involved in processing and characteristics of an open dataset of annotated clinical notes from the electronic health record (EHR) annotated for glaucoma medications. Methods: In this study, 480 clinical notes from office visits, medical record numbers (MRNs), visit identification numbers, provider names, and billing codes were extracted for 480 patients seen for glaucoma by a comprehensive or glaucoma ophthalmologist from January 1, 2019, to August 31, 2020. MRNs and all visit data were de-identified using a hash function with salt from the deidentifyr package. All progress notes were annotated for glaucoma medication name, route, frequency, dosage, and drug use using an open-source annotation tool, Doccano. Annotations were saved separately. All protected health information (PHI) in progress notes and annotated files were de-identified using the published de-identifying algorithm Philter. All progress notes and annotations were manually validated by two ophthalmologists to ensure complete de-identification. Results: The final dataset contained 5520 annotated sentences, including those with and without medications, for 480 clinical notes. Manual validation revealed 10 instances of remaining PHI which were manually corrected. Conclusions: Annotated free-text clinical notes can be de-identified for upload as an open dataset. As data availability increases with the adoption of EHRs, free-text open datasets will become increasingly valuable for "big data" research and artificial intelligence development. This dataset is published online and publicly available at https://github.com/jche253/Glaucoma_Med_Dataset. Translational Relevance: This open access medication dataset may be a source of raw data for future research involving big data and artificial intelligence research using free-text.

Artificial intelligence at the national eye institute.

PURPOSE OF REVIEW: This review highlights the artificial intelligence, machine learning, and deep learning initiatives supported by the National Institutes of Health (NIH) and the National Eye Institute (NEI) and calls attention to activities and goals defined in the NEI Strategic Plan as well as opportunities for future activities and breakthroughs in ophthalmology. RECENT FINDINGS: Ophthalmology is at the forefront of artificial intelligence-based innovations in biomedical research that may lead to improvement in early detection and surveillance of ocular disease, prediction of progression, and improved quality of life. Technological advances have ushered in an era where unprecedented amounts of information can be linked that enable scientific discovery. However, there remains an unmet need to collect, harmonize, and share data in a machine actionable manner. Similarly, there is a need to ensure that efforts promote health and research equity by expanding diversity in the data and workforce. SUMMARY: The NIH/NEI has supported the development artificial intelligence-based innovations to advance biomedical research. The NIH/NEI has defined activities to achieve these goals in the NIH Strategic Plan for Data Science and the NEI Strategic Plan and have spearheaded initiatives to facilitate research in these areas.

Medical Scribes Have a Variable Impact on Documentation Workflows.

Physicians can reduce their documentation time by working with a scribe. However, what scribes document and how their actions affect existing documentation workflows is unclear. This study leverages electronic health record (EHR) audit logs to observe how scribes affected the documentation workflows of seven physicians and their staff across 13,000 outpatient ophthalmology visits. In addition to editing progress notes, scribes routinely edited exam findings and diagnoses. Scribes with clinical training also edited items such as vital signs that a scribe without clinical training did not. Every physician edited patient records later in the day when working with a scribe and those who deferred their editing the most had some of the largest reductions in EHR time. These results suggest that what scribes document, how physicians work with scribes, and scribe impact on documentation time are all highly variable, highlighting the need for evidence-based best practices.

Pediatric Ophthalmology Provider and Staff Attitudes and Patient Satisfaction in Telehealth Implementation During COVID-19.

Purpose:Describe a comprehensive overview of a telehealth implementation process that highlights attitudes and satisfaction scores toward telehealth from patients, providers, and staff in an academic pediatric ophthalmology practice during the early months of the coronavirus disease 2019 (COVID-19) pandemic.Methods:The electronic medical record data for telehealth and in-person visits, as well as a patient experience survey in pediatric ophthalmology were retrospectively reviewed for March 1 to July 31, 2020 and March 1 to July 31, 2019. Patient experience survey results were retrospectively reviewed. All current providers and staff were invited to participate in an anonymous and voluntary survey focused on attitudes at the time of telehealth implementation.Results:During March 1 to July 31, 2020, there was significant increase in telehealth visits (n = 1,006) compared with the same period in 2019 (n = 22). Evaluation and management (E & M) codes (n = 527) were the most commonly used billing codes, and strabismus, nystagmus, and irregular eye movement (n = 496) were the most common telehealth primary diagnoses. The telehealth attitudes survey showed more positive responses from providers than staff. The patient experience survey showed more favorable scores for telehealth visits compared with clinic visits. However, only about 50% of the respondents were satisfied with the technology in terms of ease and quality of connection during their telehealth visits.Conclusions:Telehealth was a satisfactory alternative to clinic visits in our academic pediatric ophthalmology practice during the early phase of the COVID-19 pandemic. Providers and staff had largely positive attitudes toward telehealth; however, future efforts should include strategies to increase staff buy in. Patients had high satisfaction scores with telehealth visits despite connection challenges.

Frequent but fragmented: use of note templates to document outpatient visits at an academic health center.

Recent changes to billing policy have reduced documentation requirements for outpatient notes, providing an opportunity to rethink documentation workflows. While many providers use templates to write notes-whether to insert short phrases or draft entire notes-we know surprisingly little about how these templates are used in practice. In this retrospective cross-sectional study, we observed the templates that primary providers and other members of the care team used to write the provider progress note for 2.5 million outpatient visits across 52 specialties at an academic health center between 2018 and 2020. Templates were used to document 89% of visits, with a median of 2 used per visit. Only 17% of the 100 230 unique templates were ever used by more than one person and most providers had their own full-note templates. These findings suggest template use is frequent but fragmented, complicating template revision and maintenance. Reframing template use as a form of computer programming suggests ways to maintain the benefits of personalization while leveraging standardization to reduce documentation burden.

Human Factors and Organizational Issues Section Synopsis IMIA Yearbook 2021.

OBJECTIVE: To select the best papers that made original and high impact contributions in the area of human factors and organizational issues in biomedical informatics in 2020. METHODS: A rigorous extraction process based on queries from Web of Science® and PubMed/Medline was conducted to identify the scientific contributions published in 2020 that address human factors and organizational issues in biomedical informatics. The screening of papers on titles and abstracts independently by the two section editors led to a total of 1,562 papers. These papers were discussed for a selection of 12 finalist papers, which were then reviewed by the two section editors, two chief editors, and by three external reviewers from internationally renowned research teams. RESULTS: The query process resulted in 12 papers that reveal interesting and rigorous methods and important studies in human factors that move the field forward, particularly in clinical informatics and emerging technologies such as brain-computer interfaces. This year three papers were clearly outstanding and help advance in the field. They provide examples of applying existing frameworks together in novel and highly illuminating ways, showing the value of theory development in human factors. Emerging themes included several which discussed physician burnout, mobile health, and health equity. Those concerning the Corona Virus Disease 2019 (Covid-19) were included as part of that section. CONCLUSION: The selected papers make important contributions to human factors and organizational issues, expanding and deepening our knowledge of how to apply theory and applications of new technologies in health.

Electronic health record note review in an outpatient specialty clinic: who is looking?

Note entry and review in electronic health records (EHRs) are time-consuming. While some clinics have adopted team-based models of note entry, how these models have impacted note review is unknown in outpatient specialty clinics such as ophthalmology. We hypothesized that ophthalmologists and ancillary staff review very few notes. Using audit log data from 9775 follow-up office visits in an academic ophthalmology clinic, we found ophthalmologists reviewed a median of 1 note per visit (2.6 ± 5.3% of available notes), while ancillary staff reviewed a median of 2 notes per visit (4.1 ± 6.2% of available notes). While prior ophthalmic office visit notes were the most frequently reviewed note type, ophthalmologists and staff reviewed no such notes in 51% and 31% of visits, respectively. These results highlight the collaborative nature of note review and raise concerns about how cumbersome EHR designs affect efficient note review and the utility of prior notes in ophthalmic clinical care.

Length and Redundancy of Outpatient Progress Notes Across a Decade at an Academic Medical Center.

Importance: There is widespread concern that clinical notes have grown longer and less informative over the past decade. Addressing these concerns requires a better understanding of the magnitude, scope, and potential causes of increased note length and redundancy. Objective: To measure changes between 2009 and 2018 in the length and redundancy of outpatient progress notes across multiple medical specialties and investigate how these measures associate with author experience and method of note entry. Design, Setting, and Participants: This cross-sectional study was conducted at Oregon Health & Science University, a large academic medical center. Participants included clinicians and staff who wrote outpatient progress notes between 2009 and 2018 for a random sample of 200 000 patients. Statistical analysis was performed from March to August 2020. Exposures: Use of a comprehensive electronic health record to document patient care. Main Outcomes and Measures: Note length, note redundancy (ie, the proportion of text identical to the patient's last note), and percentage of templated, copied, or directly typed note text. Results: A total of 2 704 800 notes written by 6228 primary authors across 46 specialties were included in this study. Median note length increased 60.1% (99% CI, 46.7%-75.2%) from a median of 401 words (interquartile range [IQR], 225-660 words) in 2009 to 642 words (IQR, 399-1007 words) in 2018. Median note redundancy increased 10.9 percentage points (99% CI, 7.5-14.3 percentage points) from 47.9% in 2009 to 58.8% in 2018. Notes written in 2018 had a mean value of just 29.4% (99% CI, 28.2%-30.7%) directly typed text with the remaining 70.6% of text being templated or copied. Mixed-effect linear models found that notes with higher proportions of templated or copied text were significantly longer and more redundant (eg, in the 2-year model, each 1% increase in the proportion of copied or templated note text was associated with 1.5% [95% CI, 1.5%-1.5%] and 1.6% [95% CI, 1.6%-1.6%] increases in note length, respectively). Residents and fellows also wrote significantly (26.3% [95% CI, 25.8%-26.7%]) longer notes than more senior authors, as did more recent hires (1.8% for each year later [95% CI, 1.3%-2.4%]). Conclusions and Relevance: In this study, outpatient progress notes grew longer and more redundant over time, potentially limiting their use in patient care. Interventions aimed at reducing outpatient progress note length and redundancy may need to simultaneously address multiple factors such as note template design and training for both new and established clinicians.

Clinical Documentation During Scribed and Non-scribed Ophthalmology Office Visits.

PURPOSE: Observe the impact of employing scribes on documentation efficiency in ophthalmology clinics. DESIGN: Single-center retrospective cohort study. PARTICIPANTS: A total of 29,997 outpatient visits conducted by seven attending ophthalmologists between 1/1/2018 and 12/31/2019 were included in the study; 18,483 with a scribe present during the encounter and 11,514 without a scribe present. INTERVENTION: Use of a scribe. MAIN OUTCOME MEASURES: Total physician documentation time, physician documentation time during and after the visit, visit length, time to chart closure, note length, and percent of note text edited by physician. RESULTS: Total physician documentation time was significantly less when working with a scribe (mean ± SD, 4.7 ± 2.9 vs. 7.6 ± 3.8 minutes/note, P<.001), as was documentation time during the visit (2.8 ± 2.2 vs. 5.9 ± 3.1 minutes/note, P<.001). Physicians also edited scribed notes less, deleting 1.9 ± 4.4% of scribes' draft note text and adding 14.8 ± 11.4% of the final note text, compared to deleting 6.0 ± 9.1%(P<.001) of draft note text and adding 21.2 ± 15.3%(P<.001) of final note text when not working with a scribe. However, physician after-visit documentation time was significantly higher with a scribe for 3 of 7 physicians (P<.001). Scribe use was also associated with an office visit length increase of 2.9 minutes (P<.001) per patient and time to chart closure of 3.0 hours (P<.001), according to mixed-effects linear models. CONCLUSIONS: Scribe use was associated with increased documentation efficiency through lower total documentation time and less note editing by physicians. However, the use of a scribe was also associated with longer office visit lengths and time to chart closure. The variability in the impact of scribe use on different measures of documentation efficiency leaves unanswered questions about best practices for the implementation of scribes, and warrants further study of effective scribe use.

Extraction of Active Medications and Adherence Using Natural Language Processing for Glaucoma Patients.

Accuracy of medication data in electronic health records (EHRs) is crucial for patient care and research, but many studies have shown that medication lists frequently contain errors. In contrast, physicians often pay more attention to the clinical notes and record medication information in them. The medication information in notes may be used for medication reconciliation to improve the medication lists' accuracy. However, accurately extracting patient's current medications from free-text narratives is challenging. In this study, we first explored the discrepancies between medication documentation in medication lists and progress notes for glaucoma patients by manually reviewing patients' charts. Next, we developed and validated a named entity recognition model to identify current medication and adherence from progress notes. Lastly, a prototype tool for medication reconciliation using the developed model was demonstrated. In the future, the model has the potential to be incorporated into the EHR system to help with realtime medication reconciliation.

Comparing Scribed and Non-scribed Outpatient Progress Notes.

Working with scribes can reduce provider documentation time, but few studies have examined how scribes affect clinical notes. In this retrospective cross-sectional study, we examine over 50,000 outpatient progress notes written with and without scribe assistance by 70 providers across 27 specialties in 2017-2018. We find scribed notes were consistently longer than those written without scribe assistance, with most additional text coming from note templates. Scribed notes were also more likely to contain certain templated lists, such as the patient's medications or past medical history. However, there was significant variation in how working with scribes affected a provider's mix of typed, templated, and copied note text, suggesting providers adapt their documentation workflows to varying degrees when working with scribes. These results suggest working with scribes may contribute to note bloat, but that providers' individual documentation workflows, including their note templates, may have a large impact on scribed note contents.

Measures of electronic health record use in outpatient settings across vendors.

Electronic health record (EHR) log data capture clinical workflows and are a rich source of information to understand variation in practice patterns. Variation in how EHRs are used to document and support care delivery is associated with clinical and operational outcomes, including measures of provider well-being and burnout. Standardized measures that describe EHR use would facilitate generalizability and cross-institution, cross-vendor research. Here, we describe the current state of outpatient EHR use measures offered by various EHR vendors, guided by our prior conceptual work that proposed seven core measures to describe EHR use. We evaluate these measures and other reporting options provided by vendors for maturity and similarity to previously proposed standardized measures. Working toward improved standardization of EHR use measures can enable and accelerate high-impact research on physician burnout and job satisfaction as well as organizational efficiency and patient health.

Registered Nurse Strain Detection Using Ambient Data: An Exploratory Study of Underutilized Operational Data Streams in the Hospital Workplace.

BACKGROUND: Registered nurses (RNs) regularly adapt their work to ever-changing situations but routine adaptation transforms into RN strain when service demand exceeds staff capacity and patients are at risk of missed or delayed care. Dynamic monitoring of RN strain could identify when intervention is needed, but comprehensive views of RN work demands are not readily available. Electronic care delivery tools such as nurse call systems produce ambient data that illuminate workplace activity, but little is known about the ability of these data to predict RN strain. OBJECTIVES: The purpose of this study was to assess the utility of ambient workplace data, defined as time-stamped transaction records and log file data produced by non-electronic health record care delivery tools (e.g., nurse call systems, communication devices), as an information channel for automated sensing of RN strain. METHODS: In this exploratory retrospective study, ambient data for a 1-year time period were exported from electronic nurse call, medication dispensing, time and attendance, and staff communication systems. Feature sets were derived from these data for supervised machine learning models that classified work shifts by unplanned overtime. Models for three timeframes -8, 10, and 12 hours-were created to assess each model's ability to predict unplanned overtime at various points across the work shift. RESULTS: Classification accuracy ranged from 57 to 64% across three analysis timeframes. Accuracy was lowest at 10 hours and highest at shift end. Features with the highest importance include minutes spent using a communication device and percent of medications delivered via a syringe. CONCLUSION: Ambient data streams can serve as information channels that contain signals related to unplanned overtime as a proxy indicator of RN strain as early as 8 hours into a work shift. This study represents an initial step toward enhanced detection of RN strain and proactive prevention of missed or delayed patient care.