AI-Generated Draft Replies Integrated Into Health Records and Physicians' Electronic Communication.

Importance: Timely tests are warranted to assess the association between generative artificial intelligence (GenAI) use and physicians' work efforts. Objective: To investigate the association between GenAI-drafted replies for patient messages and physician time spent on answering messages and the length of replies. Design, Setting, and Participants: Randomized waiting list quality improvement (QI) study from June to August 2023 in an academic health system. Primary care physicians were randomized to an immediate activation group and a delayed activation group. Data were analyzed from August to November 2023. Exposure: Access to GenAI-drafted replies for patient messages. Main Outcomes and Measures: Time spent (1) reading messages, (2) replying to messages, (3) length of replies, and (4) physician likelihood to recommend GenAI drafts. The a priori hypothesis was that GenAI drafts would be associated with less physician time spent reading and replying to messages. A mixed-effects model was used. Results: Fifty-two physicians participated in this QI study, with 25 randomized to the immediate activation group and 27 randomized to the delayed activation group. A contemporary control group included 70 physicians. There were 18 female participants (72.0%) in the immediate group and 17 female participants (63.0%) in the delayed group; the median age range was 35-44 years in the immediate group and 45-54 years in the delayed group. The median (IQR) time spent reading messages in the immediate group was 26 (11-69) seconds at baseline, 31 (15-70) seconds 3 weeks after entry to the intervention, and 31 (14-70) seconds 6 weeks after entry. The delayed group's median (IQR) read time was 25 (10-67) seconds at baseline, 29 (11-77) seconds during the 3-week waiting period, and 32 (15-72) seconds 3 weeks after entry to the intervention. The contemporary control group's median (IQR) read times were 21 (9-54), 22 (9-63), and 23 (9-60) seconds in corresponding periods. The estimated association of GenAI was a 21.8% increase in read time (95% CI, 5.2% to 41.0%; P = .008), a -5.9% change in reply time (95% CI, -16.6% to 6.2%; P = .33), and a 17.9% increase in reply length (95% CI, 10.1% to 26.2%; P < .001). Participants recognized GenAI's value and suggested areas for improvement. Conclusions and Relevance: In this QI study, GenAI-drafted replies were associated with significantly increased read time, no change in reply time, significantly increased reply length, and some perceived benefits. Rigorous empirical tests are necessary to further examine GenAI's performance. Future studies should examine patient experience and compare multiple GenAIs, including those with medical training.

Variations in Electronic Health Record-Based Definitions of Diabetic Retinopathy Cohorts: A Literature Review and Quantitative Analysis.

Purpose: Use of the electronic health record (EHR) has motivated the need for data standardization. A gap in knowledge exists regarding variations in existing terminologies for defining diabetic retinopathy (DR) cohorts. This study aimed to review the literature and analyze variations regarding codified definitions of DR. Design: Literature review and quantitative analysis. Subjects: Published manuscripts. Methods: Four graders reviewed PubMed and Google Scholar for peer-reviewed studies. Studies were included if they used codified definitions of DR (e.g., billing codes). Data elements such as author names, publication year, purpose, data set type, and DR definitions were manually extracted. Each study was reviewed by ≥ 2 authors to validate inclusion eligibility. Quantitative analyses of the codified definitions were then performed to characterize the variation between DR cohort definitions. Main Outcome Measures: Number of studies included and numeric counts of billing codes used to define codified cohorts. Results: In total, 43 studies met the inclusion criteria. Half of the included studies used datasets based on structured EHR data (i.e., data registries, institutional EHR review), and half used claims data. All but 1 of the studies used billing codes such as the International Classification of Diseases 9th or 10th edition (ICD-9 or ICD-10), either alone or in addition to another terminology for defining disease. Of the 27 included studies that used ICD-9 and the 20 studies that used ICD-10 codes, the most common codes used pertained to the full spectrum of DR severity. Diabetic retinopathy complications (e.g., vitreous hemorrhage) were also used to define some DR cohorts. Conclusions: Substantial variations exist among codified definitions for DR cohorts within retrospective studies. Variable definitions may limit generalizability and reproducibility of retrospective studies. More work is needed to standardize disease cohorts. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Generative artificial intelligence responses to patient messages in the electronic health record: early lessons learned.

Background: Electronic health record (EHR)-based patient messages can contribute to burnout. Messages with a negative tone are particularly challenging to address. In this perspective, we describe our initial evaluation of large language model (LLM)-generated responses to negative EHR patient messages and contend that using LLMs to generate initial drafts may be feasible, although refinement will be needed. Methods: A retrospective sample (n = 50) of negative patient messages was extracted from a health system EHR, de-identified, and inputted into an LLM (ChatGPT). Qualitative analyses were conducted to compare LLM responses to actual care team responses. Results: Some LLM-generated draft responses varied from human responses in relational connection, informational content, and recommendations for next steps. Occasionally, the LLM draft responses could have potentially escalated emotionally charged conversations. Conclusion: Further work is needed to optimize the use of LLMs for responding to negative patient messages in the EHR.

Breaking Barriers in Behavioral Change: The Potential of AI-Driven Motivational Interviewing.

Patient outcomes in ophthalmology are greatly influenced by adherence and patient participation, which can be particularly challenging in diseases like glaucoma where medication regimens can be complex. A well-studied and evidence-based intervention for behavioral change is Motivational Interviewing (MI), a collaborative and patient-centered counseling approach, which has been shown to improve medication adherence in glaucoma patients. However, there are many barriers to clinicians being able to provide motivational interviewing in-office, including short visit durations within high-volume ophthalmology clinics and inadequate billing structures for counseling. Recently, Large Language Models (LLMs), a type of artificial intelligence, have advanced such that they can follow instructions and carry coherent conversations, offering novel solutions to a wide range of clinical problems. In this paper, we discuss the potential of LLMs to provide chatbot-driven MI to improve adherence in glaucoma patients, and provide an example conversation as a proof of concept. We discuss the advantages of AI-driven MI, such as demonstrated effectiveness, scalability, and accessibility. We also explore the risks and limitations, including issues of safety and privacy, as well as the factual inaccuracies and hallucinations to which LLMs are susceptible. Domain-specific training may be needed to ensure accuracy and completeness of information provided in subspecialty areas such as glaucoma. Despite the current limitations, AI-driven Motivational Interviewing has the potential to offer significant improvements in adherence, and should be further explored to maximally leverage the potential of artificial intelligence for our patients.

Evaluation of bias and gender/racial concordance based on sentiment analysis of narrative evaluations of clinical clerkships using natural language processing.

There is increasing interest in understanding potential bias in medical education. We used natural language processing (NLP) to evaluate potential bias in clinical clerkship evaluations. Data from medical evaluations and administrative databases for medical students enrolled in third-year clinical clerkship rotations across two academic years. We collected demographic information of students and faculty evaluators to determine gender/racial concordance (i.e., whether the student and faculty identified with the same demographic). We used a multinomial log-linear model for final clerkship grades, using predictors such as numerical evaluation scores, gender/racial concordance, and sentiment scores of narrative evaluations using the SentimentIntensityAnalyzer tool in Python. 2037 evaluations from 198 students were analyzed. Statistical significance was defined as P < 0.05. Sentiment scores for evaluations did not vary significantly by student gender, race, or ethnicity (P = 0.88, 0.64, and 0.06, respectively). Word choices were similar across faculty and student demographic groups. Modeling showed narrative evaluation sentiment scores were not predictive of an honors grade (odds ratio [OR] 1.23, P = 0.58). Numerical evaluation average (OR 1.45, P < 0.001) and gender concordance between faculty and student (OR 1.32, P = 0.049) were significant predictors of receiving honors. The lack of disparities in narrative text in our study contrasts with prior findings from other institutions. Ongoing efforts include comparative analyses with other institutions to understand what institutional factors may contribute to bias. NLP enables a systematic approach for investigating bias. The insights gained from the lack of association between word choices, sentiment scores, and final grades show potential opportunities to improve feedback processes for students.

Disparities in Eye Care Utilization Among Refugee and Migrant Populations.

Purpose: In this cross-sectional study, we examined refugee/migrant participants' health and eye care utilization compared to controls in San Diego County. Methods: Data were collected from electronic health records (EHRs) at UCSD Health-affiliated medical centers. Through a manual review of EHRs, eligibility criteria to identify a cohort were developed. A total of 64 refugee/migrant participants and 95 control participants matched based on country of origin, age, and sex were included in the analysis. Demographic characteristics, insurance type, and vision/eye care utilization were compared between the two groups. Results: A greater proportion of refugee/migrant participants were more likely to be enrolled in government-sponsored insurance programs, predominantly Medicaid when compared to controls (55% vs. 24%, P = < 0.01). When adjusting for age, history of ophthalmic procedure, and surgery, refugee status was associated with fewer encounters with ophthalmologists in a multivariable linear regression model (coefficient = -1.66 [95% confidence interval [CI] = -2.89 to -0.44], P = 0.009). Conclusions: This study highlights disparities in eye care utilization for refugee/migrant populations. When compared to controls, a larger proportion of refugees/migrants had government-funded insurance, and refugee status was associated with fewer encounters with ophthalmologists. These findings underscore the need for further research on this population to better understand potential healthcare barriers these individuals may encounter. Translational Relevance: This analysis of EHR data illustrates disparities in eye care experienced by refugees/migrants, highlighting potential gaps in care in a vulnerable population.

Social determinants of health and health disparities in glaucoma: A review.

Social determinants of health and barriers to care can significantly impact patients' access to glaucoma care and treatment, resulting in disparities within disease presentation, progression, management, and treatment outcomes. The widespread adoption of electronic health record systems has allowed researchers and clinicians to further explore these relationships, identifying factors such as race, ethnicity, and socioeconomic status to be risk factors for more severe disease and lower treatment adherence. These disparities highlight potential targets for interventions to combat these disparities and improve overall patient outcomes. This article provides a summary of the available data on health disparities within glaucoma disease presentation, progression, management, treatment, and outcomes and discusses interventions to improve care delivery and outcomes among patients with glaucoma.

Hidden Flaws Behind Expert-Level Accuracy of GPT-4 Vision in Medicine.

Recent studies indicate that Generative Pre-trained Transformer 4 with Vision (GPT-4V) outperforms human physicians in medical challenge tasks. However, these evaluations primarily focused on the accuracy of multi-choice questions alone. Our study extends the current scope by conducting a comprehensive analysis of GPT-4V's rationales of image comprehension, recall of medical knowledge, and step-by-step multimodal reasoning when solving New England Journal of Medicine (NEJM) Image Challenges - an imaging quiz designed to test the knowledge and diagnostic capabilities of medical professionals. Evaluation results confirmed that GPT-4V performs comparatively to human physicians regarding multi-choice accuracy (81.6% vs. 77.8%). GPT-4V also performs well in cases where physicians incorrectly answer, with over 78% accuracy. However, we discovered that GPT-4V frequently presents flawed rationales in cases where it makes the correct final choices (35.5%), most prominent in image comprehension (27.2%). Regardless of GPT-4V's high accuracy in multi-choice questions, our findings emphasize the necessity for further in-depth evaluations of its rationales before integrating such multimodal AI models into clinical workflows.

Proactive Decision Support for Glaucoma Treatment: Predicting Surgical Interventions with Clinically Available Data.

A longitudinal ophthalmic dataset was used to investigate multi-modal machine learning (ML) models incorporating patient demographics and history, clinical measurements, optical coherence tomography (OCT), and visual field (VF) testing in predicting glaucoma surgical interventions. The cohort included 369 patients who underwent glaucoma surgery and 592 patients who did not undergo surgery. The data types used for prediction included patient demographics, history of systemic conditions, medication history, ophthalmic measurements, 24-2 VF results, and thickness measurements from OCT imaging. The ML models were trained to predict surgical interventions and evaluated on independent data collected at a separate study site. The models were evaluated based on their ability to predict surgeries at varying lengths of time prior to surgical intervention. The highest performing predictions achieved an AUC of 0.93, 0.92, and 0.93 in predicting surgical intervention at 1 year, 2 years, and 3 years, respectively. The models were also able to achieve high sensitivity (0.89, 0.77, 0.86 at 1, 2, and 3 years, respectively) and specificity (0.85, 0.90, and 0.91 at 1, 2, and 3 years, respectively) at an 0.80 level of precision. The multi-modal models trained on a combination of data types predicted surgical interventions with high accuracy up to three years prior to surgery and could provide an important tool to predict the need for glaucoma intervention.

Evaluation and Optimization of Diabetic Retinopathy Screenings for Uninsured Latinx Patients in a Resource-Limited Student-Run Free Clinic.

Background: Diabetic retinopathy (DR) is a sight-threatening condition that causes progressive retina damage. Student-run free clinics represent a valuable opportunity to provide DR screenings to high-risk populations. We characterized the patient population, evaluated the performance, and conducted a needs assessment of DR screenings at the University of California, San Diego Student-Run Ophthalmology Free Clinic, which provides care to predominantly uninsured, Latino patients. Methods: Retrospective chart review was conducted of all patients seen at the free clinic since 2019 with a diagnosis of type II diabetes. Date and outcome of all DR-related screenings or visits from 2015 onward, demographics information, and DR risk factors such as A1c and insulin dependence were recorded. Predictors of diabetic retinopathy and frequency of DR screenings for each patient were analyzed using multiple logistic regression, t-test for equality of means, and Pearson's correlation. Results: Of 179 uninsured diabetic patients receiving care at the free clinic, 71% were female and average age was 59. 83% had hypertension, 93% had hyperlipidemia, and 79% had metabolic syndrome. Prevalence of non-proliferative DR was 34% and that of proliferative DR was 15% in diabetic patients. The free clinic capacity in recent years plateaued at just under 50% of patients seen for DR screening or visit per year, though average wait time was over 2 years between visits. Patients with higher no-show rates had less frequent DR screenings. Chronic kidney disease and poor glycemic control were the strongest predictors of DR. Conclusion: The student-run free ophthalmology clinic has been effective in providing screening and follow-up care for DR patients. Creation of a protocol to identify which patients are at highest risk of DR and should be seen more urgently, addressing no-shows, and implementation of a tele-retina program are potential avenues for improving clinic efficiency in a resource-limited setting for vulnerable populations.

Gender Disparities in Depression, Stress, and Social Support Among Glaucoma Patients.

Purpose: To understand differences in measures of depression, stress, and social support by gender among those diagnosed with glaucoma. Methods: We obtained a cohort of glaucoma patients (any type) ages 18 years and over who answered the COVID-19 Participant Experience (COPE) survey of the NIH All of Us Research Program. We analyzed several measures of depression, stress, and social support by gender. Logistic regression was used to evaluate the association among reported stress associated with social distancing, depression (using Patient Health Questionnaire-9 [PHQ-9] scores), and measures of social support by self-reported gender, with men as the reference group. Multivariable models were adjusted for age, race and ethnicity, health insurance status, education, and income. Results: Of 3633 glaucoma patients, 56.8% were women. Many patients had a PHQ-9 score > 4 (33.3%), indicating mild, moderate, or severe depression. In multivariable models, women were significantly more likely to report a PHQ-9 score > 4 (odds ratio [OR] = 1.40; 95% confidence interval [CI], 1.20-1.62; P < 0.001) and some or a lot of stress (OR = 1.34; 95% CI, 1.14-1.57; P < 0.001) compared with men. Further, women were significantly less likely to report having help all or most of the time if they needed someone to prepare meals (OR = 0.78; 95% CI, 0.67-0.92; P = 0.002) or perform daily chores (OR = 0.79; 95% CI, 0.67-0.91; P = 0.003) than men. Conclusions: Women with glaucoma were more likely to experience depression and stress and were less likely to have social support on some measures than men. Translational Relevance: The disproportionate burden of psychosocial factors among women may complicate glaucoma management.

Evaluation of Publications from the American Academy of Ophthalmology: A 5-Year Analysis of Ophthalmology Literature.

Objective: To analyze recent publications in Ophthalmology, the journal of the American Academy of Ophthalmology. Design: Retrospective review of published articles. Participants: No human participants were involved in the study. Methods: Articles published in Ophthalmology from January 2018 to December 2022 were reviewed and analyzed. Main Outcome Measures: Research and review articles were included and analyzed per the following: total number of published articles based on related subspecialty area, level of evidence using the modified Oxford level of evidence, number of citations, number of listed authors, gender of the corresponding author, country of affiliation of the corresponding and contributing author(s), and involvement of consortium(s), group(s), or committee(s). Results: A total of 965 articles were included. The mean (standard deviation) number of authors per article was 8.6 (5.7) and the majority of corresponding authors were male (665, 70.7%). The greatest number of published articles were related to retina (296, 30.7%) followed by glaucoma (172, 17.8%). The greatest number of Preferred Practice Pattern guidelines were also related to retina (7/24, 29.1%), followed by cornea/dry eye syndrome/external disease (6/24, 25%). Retina (77) had the most level 1 evidence, glaucoma (30) for level 2 evidence, and retina for levels 3 (69) and 4 (65). There were 223 articles contributed by consortia/groups/committees, with most from retina (73, 32.7%) followed by glaucoma (40, 17.9%). The mean number of citations per subspecialty article was highest in retina (45.8/article), followed by uveitis (31.7/article). The United States had the greatest number of affiliated corresponding authors (544, 56.4%), followed by the United Kingdom (68, 7.0%). There were 357 (37.0%) articles with coauthors affiliated outside the corresponding author's country of affiliation, although with a downward trend over the most recent 5-year period. There has been an increasing trend in the number of authors and consortia/group/committee involvement in publications. Conclusions: Although team science and collaborations have increased recently, ongoing efforts to diversify individuals, groups, and subspecialties may be needed. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

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.

Generative Artificial Intelligence Through ChatGPT and Other Large Language Models in Ophthalmology: Clinical Applications and Challenges.

The rapid progress of large language models (LLMs) driving generative artificial intelligence applications heralds the potential of opportunities in health care. We conducted a review up to April 2023 on Google Scholar, Embase, MEDLINE, and Scopus using the following terms: "large language models," "generative artificial intelligence," "ophthalmology," "ChatGPT," and "eye," based on relevance to this review. From a clinical viewpoint specific to ophthalmologists, we explore from the different stakeholders' perspectives-including patients, physicians, and policymakers-the potential LLM applications in education, research, and clinical domains specific to ophthalmology. We also highlight the foreseeable challenges of LLM implementation into clinical practice, including the concerns of accuracy, interpretability, perpetuating bias, and data security. As LLMs continue to mature, it is essential for stakeholders to jointly establish standards for best practices to safeguard patient safety. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.

Associations of smoking and alcohol consumption with the development of open angle glaucoma: a retrospective cohort study.

OBJECTIVES: To investigate the associations of alcohol consumption and smoking with the development of perimetric glaucoma in patients with suspected glaucoma. DESIGN: A retrospective cohort study of patients suspected to have glaucoma enrolled in the Diagnostic Innovations in Glaucoma Study (DIGS) and the African Descent and Glaucoma Evaluation Study (ADAGES). SETTING: Three tertiary glaucoma centres in the USA. PARTICIPANTS: 825 eyes of 610 patients with glaucoma suspect eyes with normal visual fields (VF) at baseline were followed over an average of 9 years from the DIGS and ADAGES studies. OUTCOME MEASURES: Development of glaucoma was defined as occurrence of three consecutive abnormal VF tests during follow-up. Univariable and multivariable Cox regression models were used to investigate lifestyle-related factors associated with development of VF loss over time. RESULTS: VF tests were abnormal three times in a row in 235 (28.5%) eyes. Alcohol consumption was associated with a higher risk of developing glaucoma (HR 1.57, 95% CI 1.03 to 2.38, p=0.037). In men, the risk of developing glaucoma in alcohol drinkers (HR 1.92, 95% CI 1.00 to 3.68, p=0.048) was greater than non-alcohol drinkers. In individuals of African descent, the risk of developing glaucoma in alcohol drinkers (HR 1.79, 95% CI 1.02 to 3.15, p=0.043) was greater than non-alcohol drinkers. Age was a modifier of the relationship between smoking and glaucomatous VF defects (p=0.048). The risk of developing glaucoma in smokers (HR 1.73, 95% CI 1.10 to 2.72, p=0.019) was greater than never smokers after adjustment for confounding factors in older patients (age >61 years). CONCLUSION: Alcohol consumption was associated with an increased risk of developing glaucoma, particularly in men and individuals of African descent. The risk of developing glaucoma among smokers suspected of having glaucoma was influenced by age, with older individuals having a higher risk than younger people. TRIAL REGISTRATION NUMBER: NCT00221897 and NCT00221923.

Social and Health Care Utilization Factors Associated With Ophthalmic Visit Nonadherence in Glaucoma: An All of Us Study.

PRCIS: In a diverse nationwide cohort, lower education and income levels, cost saving on medications, fewer past-year medical/specialist visits, and concerns regarding dissimilarity with health care providers were risk factors for ophthalmic visit nonadherence among glaucoma patients. PURPOSE: The purpose of this study was to characterize social and health care utilization factors associated with nonadherence with ophthalmic visits among patients with glaucoma. MATERIALS AND METHODS: Glaucoma patients in the All of Us database who completed the Healthcare Access and Utilization Survey were included and categorized into "visit" and "nonvisit" groups based on visit adherence, defined by self-reported past-year encounters with eyecare providers (yes/no). Data regarding potential factors affecting ophthalmic visit adherence, including past-year medical visits, inabilities to afford health care, and self-reported reasons for delayed care, were extracted. χ 2 tests and logistic regression were used to compare the 2 groups. Odds ratios (ORs) of visit adherence were analyzed for potential risk factors. RESULTS: Of 5739 glaucoma patients, 861 (15%) were in the nonvisit group. More participants in the visit group reported past-year general doctor/specialist visits (94%/65%; vs. nonvisit group: 89%/49.3%; P <0.05). The nonvisit group reported greater difficulty in affording medical care and learning about medical conditions, and higher rates of delayed/missed health care access for various concerns ( P <0.05). Older age (OR=1.02, 1.01-1.03), higher education (OR=1.25, 1.13-1.40), and income level (OR=1.06, 1.01-1.11), not employed for wages (OR=1.28, 1.08-1.53), and higher health care utilization in general medical/specialist visits (ORs range:1.08-1.90) were associated with visit adherence ( P <0.05). Visit nonadherence was associated with cost saving on medication (OR=0.62, 0.40-0.97) and delaying/avoiding seeing health care providers because of dissimilarity (OR=0.84, 0.71-0.99) ( P <0.05). CONCLUSIONS: This study builds on prior literature by identifying potentially modifiable factors associated with visit nonadherence and underutilization of eyecare in glaucoma. These may inform strategies to improve real-world ophthalmic visit adherence and identify patients who might benefit from additional support.

Availability of Physical Activity Tracking Data from Wearable Devices for Glaucoma Patients.

Physical activity has been found to potentially modulate glaucoma risk, but the evidence remains inconclusive. The increasing use of wearable physical activity trackers may provide longitudinal and granular data suitable to address this issue, but little is known regarding the characteristics and availability of these data sources. We performed a scoping review and query of data sources on the availability of wearable physical activity data for glaucoma patients. Literature databases (PubMed and MEDLINE) were reviewed with search terms consisting of those related to physical activity trackers and those related to glaucoma, and we evaluated results at the intersection of these two groups. Biomedical databases were also reviewed, for which we completed database queries. We identified eight data sources containing physical activity tracking data for glaucoma, with two being large national databases (UK BioBank and All of Us) and six from individual journal articles providing participant-level information. The number of glaucoma patients with physical activity tracking data available, types of glaucoma-related data, fitness devices utilized, and diversity of participants varied across all sources. Overall, there were limited analyses of these data, suggesting the need for additional research to further investigate how physical activity may alter glaucoma risk.

Trends and practices following the 2016 hydroxychloroquine screening guidelines.

This study aimed to understand the profile of hydroxychloroquine-treated patients, referral patterns, and dosing and to assess the adherence of eye care providers to the latest 2016 screening guidelines provided by the American Academy of Ophthalmology. Patients were identified using electronic health records (EHR) taking hydroxychloroquine and were seen by optometrists, retinal specialists, and non-retinal ophthalmologists. Review of EHR data includes demographic characteristics, indications, and dosing profile of hydroxychloroquine use, eye care provider managing the patient, and imaging modalities performed. A total of 166 patients were included in the study. The most common indications for screening were systemic lupus erythematosus and discoid lupus (52.4%) followed by rheumatoid arthritis (18.7%) and Sjögren's syndrome (9.6%). Ninety-two (55.4%) patients were on a higher-than-recommended dose of > 5 mg/kg/day. Patients who weighed less (mean 63.9 kg) were taking a higher-than-recommended dose (vs. 81.5 kg, p < 0.001). Although retinal specialists adhered best to the use of all three recommended imaging modalities, visual field testing was done appropriately for only 8.3% of Asian and 71.1% of non-Asian patients. In conclusion, there is substantial variability in screening by ophthalmic providers and prescribing practices compared with the current recommendations. In particular, there is a marked deficiency in correct visual field testing in Asian patients. These findings are important to highlight potential interventions to improve screening for hydroxychloroquine toxicity.

Concept Coverage Analysis of Ophthalmic Infections and Trauma among the Standardized Medical Terminologies SNOMED-CT, ICD-10-CM, and ICD-11.

Purpose: Widespread electronic health record adoption has generated a large volume of data and emphasized the need for standardized terminology to describe clinical concepts. Here, we undertook a systematic concept coverage analysis to determine the representation of clinical concepts in ophthalmic infection and ophthalmic trauma among standardized medical terminologies, including the Systematized Nomenclature of Medicine Clinical Terms (SNOMED-CT), the International Classification of Diseases (ICD) version 10 with clinical modifications (ICD-10-CM), and ICD version 11 (ICD-11). Design: Extraction of concepts related to ophthalmic infection and ophthalmic trauma and structured search in terminology browsers. Data Sources: The American Academy of Ophthalmology Basic and Clinical Science Course (BCSC), SNOMED-CT, and ICD-10-CM terminologies from the Observational Health Data Sciences and Informatics Athena browser, and the ICD-11 terminology browser. Methods: Concepts pertaining to ophthalmic infection and ophthalmic trauma were extracted from the 2022 BCSC free text and index terms. We searched terminology browsers to identify corresponding codes and classified the extent of semantic alignment as equal, wide, narrow, or unmatched in each terminology. The overlap of equal concepts in each terminology was represented in a Venn diagram. Main Outcome Measures: Proportions of clinical concepts with corresponding codes at various levels of semantic alignment. Results: A total of 443 concepts were identified: 304 concepts related to ophthalmic infection and 139 concepts related to ophthalmic trauma. The SNOMED-CT had the highest proportion of equal coverage, with 82.0% (249 of 304) among concepts related to ophthalmic infection and 82.0% (115 of 139) among concepts related to ophthalmic trauma. Across all concepts, 28% (124 of 443) were classified as equal in ICD-10-CM and 52.8% (234 of 443) were classified as equal in ICD-11. Conclusions: The SNOMED-CT had significantly better semantic alignment than ICD-10-CM and ICD-11 for ophthalmic infections and ophthalmic trauma. This demonstrates opportunity for continuing advancement of representation of ophthalmic concepts in standardized medical terminologies.