Impact of Language Preference and Comorbidities on Telemedicine in Primary Care

Impact of Language Preference and Comorbidities on Telemedicine in Primary Care | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Short Report Impact of Language Preference and Comorbidities on Telemedicine in Primary Care Amanda Luff, Eva Chang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6347497/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 24 Jul, 2025 Read the published version in Journal of General Internal Medicine → Version 1 posted You are reading this latest preprint version Abstract Investigating how comorbidity and language preference interact to influence telemedicine use can guide the development of targeted interventions to enhance access for non-English language preference patients. This study aims to address how the relationship between comorbidity status and telemedicine use differs by language preference in a large Midwestern healthcare system. General Practice Telemedicine Language Preference Comorbidity Healthcare Access Health Disparities Figures Figure 1 Introduction Patients with non-English language preference (NELP) are less likely to use telemedicine compared to those with English language preference (ELP). 1 – 3 While patients with more comorbidities are more likely to receive telemedicine care, 4 the combined effect of language preference and comorbidity on telemedicine use has not been investigated. Understanding this interaction is crucial as NELP is also associated with multimorbidity. 5 Patients with NELP may encounter unique barriers to telemedicine, such as technological challenges, limited access to interpreter services, and differing perceptions of telemedicine. Investigating how comorbidity and language preference interact to influence telemedicine use can guide the development of targeted interventions to enhance access for NELP patients. This study aims to address how the relationship between comorbidity status and telemedicine use differs by language preference in a large Midwestern healthcare system. Methods The Advocate Aurora Health Institutional Review Board approved this retrospective study with a waiver of informed consent. The study population includes adult patients with at least one visit with a primary care clinician (family medicine or internal medicine) in 2022. Data Collection and Measurements We identified 30-day episodes of care from electronic medical records (EMR), excluding follow-up visits, and classified index visits as in-person or telemedicine. Patients had NELP if they had EMR-documented use or need for interpreter services or a preferred language other than English. We categorized Charlson Comorbidity Index (CCI), comprising 19 comorbid conditions weighted based on 1-year mortality, 6 as minimal (CCI = 0), mild (CCI = 1–2), moderate (CCI = 3–4), or severe (CCI ≥ 5). Statistics We described patient characteristics, stratified by NELP. We used generalized linear mixed models to estimate the odds of telemedicine visits, including an interaction between NELP and CCI and random intercepts at the patient level to allow multiple episodes of care. Adjustment covariates, selected using a directed acyclic graph, included age, sex, race/ethnicity, visit-level insurance, visit state, and number of primary care visits in 2022. We calculated adjusted marginal probabilities of visits being telemedicine. Visits with missing data accounted for less than 5% of the sample and were excluded. We conducted analyses using StataMP 18 (Stata Corporation, College Station, TX). Results After excluding 22,597 patients (39,169 visits) with missing data, the analytic sample included 989,433 patients (2,189,412 visits). Patients with NELP accounted for 3.7% of the sample ( Table 1 ) . Most patients (63.4%) had a CCI in the Mild range. Among NELP patients, 63% reported Spanish as their preferred language. Table 1 Descriptives Characteristics of Patients with Primary Care Visits in 2022, Stratified by Language Preference Language Preference English Non-English Total N (%) 952,752 (96.3%) 36,681 (3.7%) 989,433 (100.0%) Mode of visits in period In-Person Only 896,594 (94.1%) 34,904 (95.2%) 931,498 (94.1%) Any Telemedicine 56,158 (5.9%) 1,777 (4.8%) 57,935 (5.9%) CCI category Minimal 606,290 (63.6%) 20,951 (57.1%) 627,241 (63.4%) Mild 238,881 (25.1%) 10,364 (28.3%) 249,245 (25.2%) Moderate 68,021 (7.1%) 3,347 (9.1%) 71,368 (7.2%) Severe 39,560 (4.2%) 2,019 (5.5%) 41,579 (4.2%) Age in years 18–29 126,141 (13.2%) 1,684 (4.6%) 127,825 (12.9%) 30–49 266,293 (27.9%) 9,420 (25.7%) 275,713 (27.9%) 50–64 281,385 (29.5%) 11,883 (32.4%) 293,268 (29.6%) 65–74 171,720 (18.0%) 7,948 (21.7%) 179,668 (18.2%) 75+ 107,213 (11.3%) 5,746 (15.7%) 112,959 (11.4%) Sex Female 551,332 (57.9%) 21,915 (59.7%) 573,247 (57.9%) Male 401,420 (42.1%) 14,766 (40.3%) 416,186 (42.1%) Patient's race/ethnicity White, Non-Hispanic 691,705 (72.6%) 6,521 (17.8%) 698,226 (70.6%) Black, Non-Hispanic 140,303 (14.7%) 424 (1.2%) 140,727 (14.2%) Asian, Non-Hispanic 33,038 (3.5%) 6,397 (17.4%) 39,435 (4.0%) Hispanic 70,732 (7.4%) 20,491 (55.9%) 91,223 (9.2%) Another Non-Hispanic Race 16,974 (1.8%) 2,848 (7.8%) 19,822 (2.0%) Insurance at first visit in period Commercial 554,630 (58.2%) 14,634 (39.9%) 569,264 (57.5%) Medicare 286,580 (30.1%) 12,186 (33.2%) 298,766 (30.2%) Medicaid or other non-commercial 99,605 (10.5%) 7,362 (20.1%) 106,967 (10.8%) Self-Pay 11,937 (1.3%) 2,499 (6.8%) 14,436 (1.5%) Number of visits in period 1 404,166 (42.4%) 12,843 (35.0%) 417,009 (42.1%) 2 260,136 (27.3%) 10,028 (27.3%) 270,164 (27.3%) 3 140,191 (14.7%) 6,272 (17.1%) 146,463 (14.8%) 4 or more 148,259 (15.6%) 7,538 (20.6%) 155,797 (15.7%) Clinic state Illinois 404,464 (42.5%) 22,872 (62.4%) 427,336 (43.2%) Wisconsin 548,288 (57.5%) 13,809 (37.6%) 562,097 (56.8%) We observed a statistically significant interaction between CCI and NELP ( Fig. 1 ). The probability of telemedicine visits among ELP patients rose slightly with CCI severity (Minimal: 3.1% [95% CI: 3.1, 3.1]; Mild: 3.3% [3.3, 3.4]; Moderate: 3.6% [3.5, 3.7]; Severe: 4.2% [4.0, 4.3]). Among NELP patients, the probability of telemedicine visits was lower than that of ELP patients at minimal and mild CCI but converged with ELP at moderate and severe levels (Minimal: 1.9% [1.7, 2.0]; Mild: 2.3% [2.0, 2.5]; Moderate: 3.2% [2.8, 3.7]; Severe: 4.4% [3.7, 5.1]). Discussion Our findings highlight significant differences in telemedicine use among NELP patients, particularly those with fewer comorbidities. NELP patients with minimal comorbidities were less likely to use telemedicine compared to ELP counterparts; however, as comorbidity severity increased, the differences in telemedicine use attenuated. This suggests that while comorbidity status is a driver of telemedicine use, communication remains a barrier for NELP patients, particularly those with fewer health issues. The lower likelihood of telemedicine use among NELP patients with fewer comorbidities may reflect challenges such as limited access to technology, difficulties navigating telemedicine platforms, and lack of familiarity with the healthcare system. During the study period, our online patient portal was only available in English, posing a significant barrier to telemedicine access for NELP patients. Convergence in likelihood of telemedicine use at higher comorbidity may be attributable to greater familiarity with navigating the healthcare system, as well as greater motivation to access telemedicine services to reduce the burden of frequent medical appointments. 7 The interaction between comorbidity and language preference underscores the need for targeted interventions to improve telemedicine access for patients with NELP. Healthcare systems should consider strategies to enhance the accessibility and usability of telemedicine platforms, such as providing multilingual support, offering resources to help patients navigate these platforms, and increasing awareness of telemedicine services. Declarations Authorship confirmation/contribution statement Amanda Luff: Conceptualization, Methodology, Formal analysis, Writing - Original Draft, Visualization. Eva Chang: Conceptualization, Writing - Original Draft, Supervision, Funding acquisition. Authors’ disclosure The authors have no conflicts of interest to report. Funding statement This research was funded by a Live Well Intramural Pilot Grant from the Advocate Aurora Research Institute References Chunara R, Zhao Y, Chen J et al (2021) Telemedicine and healthcare disparities: a cohort study in a large healthcare system in New York City during COVID-19. J Am Med Inf Assoc 28(1):33–41. 10.1093/jamia/ocaa217 Parker S, Prince A, Thomas L, Song H, Milosevic D, Harris MF (2018) Electronic, mobile and telehealth tools for vulnerable patients with chronic disease: a systematic review and realist synthesis. BMJ Open 8(8):e019192. 10.1136/bmjopen-2017-019192 Casillas A, Abhat A, Vassar SD et al (2021) Not Speaking the Same Language—Lower Portal Use for Limited English Proficient Patients in the Los Angeles Safety Net. J Health Care Poor Underserved 32(4):2055–2070 Khatana SAM, Yang L, Eberly LA, Julien HM, Adusumalli S, Groeneveld PW (2022) Predictors of telemedicine use during the COVID-19 pandemic in the United States–an analysis of a national electronic medical record database. PLoS ONE 17(6):e0269535. 10.1371/journal.pone.0269535 Rawal S, Srighanthan J, Vasantharoopan A, Hu H, Tomlinson G, Cheung AM (2019) Association Between Limited English Proficiency and Revisits and Readmissions After Hospitalization for Patients With Acute and Chronic Conditions in Toronto, Ontario, Canada. JAMA 322(16):1605–1607. 10.1001/jama.2019.13066 Charlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40(5):373–383. 10.1016/0021-9681(87)90171-8 Tahsin F, Bahr T, Shaw J, Shachak A, Gray CS (2024) The relationship between treatment burden and the use of telehealth technologies among patients with chronic conditions: A scoping review. Health Policy Technol 13(2):100855. 10.1016/j.hlpt.2024.100855 Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Published Journal Publication published 24 Jul, 2025 Read the published version in Journal of General Internal Medicine → Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6347497","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Short Report","associatedPublications":[],"authors":[{"id":436517119,"identity":"5df0f03e-41ad-411b-85d9-50e242c616b4","order_by":0,"name":"Amanda Luff","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2klEQVRIiWNgGAWjYLACHgYGGQbmgw8fMDAcIF4LDwNbsrEByVrMJIjSwj/t8LMHb2oYeAyOMbNV/Ki5w8AvffwCXi0St9PMDeccg2i52XPsGYNkX04BfmtuJ5hJ87ABtdzvP3aDh+0wg8EZngS8OuRvp3+T5vkHsaXwzz8itBjczjGT5m2DaGHmbQNpYT+AV4vh7Zwyybl9EjySx5iZpWX7DvNI9vDg94rc7fRtEm++2cjxHWNm/Pjm22E5fh72B/j1QIAEnAWKIwNitKAA4mwZBaNgFIyCkQMArY9CD+H/PeAAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-5186-3557","institution":"Advocate Aurora Research Institute, Advocate Health","correspondingAuthor":true,"prefix":"","firstName":"Amanda","middleName":"","lastName":"Luff","suffix":""},{"id":436517120,"identity":"00c1db75-c89c-4b04-a503-48b7087a4a74","order_by":1,"name":"Eva Chang","email":"","orcid":"https://orcid.org/0000-0003-2547-7780","institution":"Advocate Aurora Research Institute, Advocate Health","correspondingAuthor":false,"prefix":"","firstName":"Eva","middleName":"","lastName":"Chang","suffix":""}],"badges":[],"createdAt":"2025-03-31 19:40:41","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-6347497/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6347497/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s11606-025-09766-2","type":"published","date":"2025-07-25T00:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":79800142,"identity":"49ce1121-e139-4f24-9460-b9bac49ad3e3","added_by":"auto","created_at":"2025-04-03 03:08:52","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":286818,"visible":true,"origin":"","legend":"\u003cp\u003ePredicted Marginal Probabilities for Visits being Telemedicine\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-6347497/v1/22873ead171ec5462c707c26.jpeg"},{"id":87956752,"identity":"09e10903-55f7-4dcf-9a6c-cb8af2a5a1fe","added_by":"auto","created_at":"2025-07-30 19:26:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":763921,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6347497/v1/86e95e2d-fda2-4b9b-a1dd-887b7791245f.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eImpact of Language Preference and Comorbidities on Telemedicine in Primary Care\u003c/p\u003e","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePatients with non-English language preference (NELP) are less likely to use telemedicine compared to those with English language preference (ELP).\u003csup\u003e\u003cspan additionalcitationids=\"CR2\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e While patients with more comorbidities are more likely to receive telemedicine care,\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e the combined effect of language preference and comorbidity on telemedicine use has not been investigated. Understanding this interaction is crucial as NELP is also associated with multimorbidity.\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e Patients with NELP may encounter unique barriers to telemedicine, such as technological challenges, limited access to interpreter services, and differing perceptions of telemedicine. Investigating how comorbidity and language preference interact to influence telemedicine use can guide the development of targeted interventions to enhance access for NELP patients. This study aims to address how the relationship between comorbidity status and telemedicine use differs by language preference in a large Midwestern healthcare system.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e The Advocate Aurora Health Institutional Review Board approved this retrospective study with a waiver of informed consent. The study population includes adult patients with at least one visit with a primary care clinician (family medicine or internal medicine) in 2022.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eData Collection and Measurements\u003c/h2\u003e \u003cp\u003eWe identified 30-day episodes of care from electronic medical records (EMR), excluding follow-up visits, and classified index visits as in-person or telemedicine. Patients had NELP if they had EMR-documented use or need for interpreter services or a preferred language other than English. We categorized Charlson Comorbidity Index (CCI), comprising 19 comorbid conditions weighted based on 1-year mortality,\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e as minimal (CCI\u0026thinsp;=\u0026thinsp;0), mild (CCI\u0026thinsp;=\u0026thinsp;1\u0026ndash;2), moderate (CCI\u0026thinsp;=\u0026thinsp;3\u0026ndash;4), or severe (CCI\u0026thinsp;\u0026ge;\u0026thinsp;5).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStatistics\u003c/h3\u003e\n\u003cp\u003eWe described patient characteristics, stratified by NELP. We used generalized linear mixed models to estimate the odds of telemedicine visits, including an interaction between NELP and CCI and random intercepts at the patient level to allow multiple episodes of care. Adjustment covariates, selected using a directed acyclic graph, included age, sex, race/ethnicity, visit-level insurance, visit state, and number of primary care visits in 2022. We calculated adjusted marginal probabilities of visits being telemedicine. Visits with missing data accounted for less than 5% of the sample and were excluded. We conducted analyses using StataMP 18 (Stata Corporation, College Station, TX).\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAfter excluding 22,597 patients (39,169 visits) with missing data, the analytic sample included 989,433 patients (2,189,412 visits). Patients with NELP accounted for 3.7% of the sample \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e. Most patients (63.4%) had a CCI in the Mild range. Among NELP patients, 63% reported Spanish as their preferred language.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptives Characteristics of Patients with Primary Care Visits in 2022, Stratified by Language Preference\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003eLanguage Preference\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEnglish\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNon-English\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eN (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e952,752 (96.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e36,681 (3.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e989,433 (100.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMode of visits in period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn-Person Only\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e896,594 (94.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e34,904 (95.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e931,498 (94.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAny Telemedicine\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e56,158 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,777 (4.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e57,935 (5.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCCI category\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMinimal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e606,290 (63.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20,951 (57.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e627,241 (63.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMild\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e238,881 (25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,364 (28.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e249,245 (25.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e68,021 (7.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3,347 (9.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e71,368 (7.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSevere\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e39,560 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,019 (5.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e41,579 (4.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge in years\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e126,141 (13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1,684 (4.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e127,825 (12.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e30\u0026ndash;49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e266,293 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e9,420 (25.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e275,713 (27.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e50\u0026ndash;64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e281,385 (29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11,883 (32.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e293,268 (29.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e65\u0026ndash;74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e171,720 (18.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,948 (21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e179,668 (18.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e75+\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e107,213 (11.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5,746 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e112,959 (11.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e551,332 (57.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e21,915 (59.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e573,247 (57.9%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e401,420 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,766 (40.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e416,186 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePatient's race/ethnicity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWhite, Non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e691,705 (72.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,521 (17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e698,226 (70.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBlack, Non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140,303 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e424 (1.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e140,727 (14.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAsian, Non-Hispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e33,038 (3.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,397 (17.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e39,435 (4.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHispanic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70,732 (7.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20,491 (55.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e91,223 (9.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnother Non-Hispanic Race\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16,974 (1.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,848 (7.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e19,822 (2.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eInsurance at first visit in period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCommercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e554,630 (58.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e14,634 (39.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e569,264 (57.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicare\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e286,580 (30.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,186 (33.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e298,766 (30.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicaid or other non-commercial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e99,605 (10.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,362 (20.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e106,967 (10.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-Pay\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e11,937 (1.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2,499 (6.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14,436 (1.5%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eNumber of visits in period\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e404,166 (42.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,843 (35.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e417,009 (42.1%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e260,136 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10,028 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e270,164 (27.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e140,191 (14.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6,272 (17.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e146,463 (14.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4 or more\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e148,259 (15.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,538 (20.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e155,797 (15.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eClinic state\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIllinois\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e404,464 (42.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e22,872 (62.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e427,336 (43.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWisconsin\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e548,288 (57.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,809 (37.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e562,097 (56.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eWe observed a statistically significant interaction between CCI and NELP \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e).\u003c/b\u003e The probability of telemedicine visits among ELP patients rose slightly with CCI severity (Minimal: 3.1% [95% CI: 3.1, 3.1]; Mild: 3.3% [3.3, 3.4]; Moderate: 3.6% [3.5, 3.7]; Severe: 4.2% [4.0, 4.3]). Among NELP patients, the probability of telemedicine visits was lower than that of ELP patients at minimal and mild CCI but converged with ELP at moderate and severe levels (Minimal: 1.9% [1.7, 2.0]; Mild: 2.3% [2.0, 2.5]; Moderate: 3.2% [2.8, 3.7]; Severe: 4.4% [3.7, 5.1]).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur findings highlight significant differences in telemedicine use among NELP patients, particularly those with fewer comorbidities. NELP patients with minimal comorbidities were less likely to use telemedicine compared to ELP counterparts; however, as comorbidity severity increased, the differences in telemedicine use attenuated. This suggests that while comorbidity status is a driver of telemedicine use, communication remains a barrier for NELP patients, particularly those with fewer health issues.\u003c/p\u003e \u003cp\u003eThe lower likelihood of telemedicine use among NELP patients with fewer comorbidities may reflect challenges such as limited access to technology, difficulties navigating telemedicine platforms, and lack of familiarity with the healthcare system. During the study period, our online patient portal was only available in English, posing a significant barrier to telemedicine access for NELP patients. Convergence in likelihood of telemedicine use at higher comorbidity may be attributable to greater familiarity with navigating the healthcare system, as well as greater motivation to access telemedicine services to reduce the burden of frequent medical appointments.\u003csup\u003e\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u003c/sup\u003e\u003c/p\u003e \u003cp\u003eThe interaction between comorbidity and language preference underscores the need for targeted interventions to improve telemedicine access for patients with NELP. Healthcare systems should consider strategies to enhance the accessibility and usability of telemedicine platforms, such as providing multilingual support, offering resources to help patients navigate these platforms, and increasing awareness of telemedicine services.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthorship confirmation/contribution statement\u003c/h2\u003e\n\u003cp\u003e\u003cstrong\u003eAmanda Luff:\u003c/strong\u003e Conceptualization, Methodology, Formal analysis, Writing - Original Draft, Visualization.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEva Chang:\u003c/strong\u003e Conceptualization, Writing - Original Draft, Supervision, Funding acquisition.\u003c/p\u003e\n\u003ch2\u003eAuthors\u0026rsquo; disclosure\u003c/h2\u003e\n\u003cp\u003eThe authors have no conflicts of interest to report.\u003c/p\u003e\n\u003ch2\u003eFunding statement\u003c/h2\u003e\n\u003cp\u003eThis research was funded by a Live Well Intramural Pilot Grant from the Advocate Aurora Research Institute\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eChunara R, Zhao Y, Chen J et al (2021) Telemedicine and healthcare disparities: a cohort study in a large healthcare system in New York City during COVID-19. J Am Med Inf Assoc 28(1):33\u0026ndash;41. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/jamia/ocaa217\u003c/span\u003e\u003cspan address=\"10.1093/jamia/ocaa217\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eParker S, Prince A, Thomas L, Song H, Milosevic D, Harris MF (2018) Electronic, mobile and telehealth tools for vulnerable patients with chronic disease: a systematic review and realist synthesis. BMJ Open 8(8):e019192. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjopen-2017-019192\u003c/span\u003e\u003cspan address=\"10.1136/bmjopen-2017-019192\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCasillas A, Abhat A, Vassar SD et al (2021) Not Speaking the Same Language\u0026mdash;Lower Portal Use for Limited English Proficient Patients in the Los Angeles Safety Net. J Health Care Poor Underserved 32(4):2055\u0026ndash;2070\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKhatana SAM, Yang L, Eberly LA, Julien HM, Adusumalli S, Groeneveld PW (2022) Predictors of telemedicine use during the COVID-19 pandemic in the United States\u0026ndash;an analysis of a national electronic medical record database. PLoS ONE 17(6):e0269535. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0269535\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0269535\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRawal S, Srighanthan J, Vasantharoopan A, Hu H, Tomlinson G, Cheung AM (2019) Association Between Limited English Proficiency and Revisits and Readmissions After Hospitalization for Patients With Acute and Chronic Conditions in Toronto, Ontario, Canada. JAMA 322(16):1605\u0026ndash;1607. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1001/jama.2019.13066\u003c/span\u003e\u003cspan address=\"10.1001/jama.2019.13066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCharlson ME, Pompei P, Ales KL, MacKenzie CR (1987) A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis 40(5):373\u0026ndash;383. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/0021-9681(87)90171-8\u003c/span\u003e\u003cspan address=\"10.1016/0021-9681(87)90171-8\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTahsin F, Bahr T, Shaw J, Shachak A, Gray CS (2024) The relationship between treatment burden and the use of telehealth technologies among patients with chronic conditions: A scoping review. Health Policy Technol 13(2):100855. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.hlpt.2024.100855\u003c/span\u003e\u003cspan address=\"10.1016/j.hlpt.2024.100855\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":false,"highlight":"","institution":"Advocate Health","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Telemedicine, Language Preference, Comorbidity, Healthcare Access, Health Disparities","lastPublishedDoi":"10.21203/rs.3.rs-6347497/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6347497/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eInvestigating how comorbidity and language preference interact to influence telemedicine use can guide the development of targeted interventions to enhance access for non-English language preference patients. This study aims to address how the relationship between comorbidity status and telemedicine use differs by language preference in a large Midwestern healthcare system.\u003c/p\u003e","manuscriptTitle":"Impact of Language Preference and Comorbidities on Telemedicine in Primary Care","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-03 02:52:47","doi":"10.21203/rs.3.rs-6347497/v1","editorialEvents":[{"type":"communityComments","content":1}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"846c9860-742b-451d-9a02-ff4dc8f0f6ac","owner":[],"postedDate":"April 3rd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":46479078,"name":"General Practice"}],"tags":[],"updatedAt":"2025-07-30T19:26:06+00:00","versionOfRecord":{"articleIdentity":"rs-6347497","link":"https://doi.org/10.1007/s11606-025-09766-2","journal":{"identity":"journal-of-general-internal-medicine","isVorOnly":false,"title":"Journal of General Internal Medicine"},"publishedOn":"2025-07-25 00:00:00","publishedOnDateReadable":"July 25th, 2025"},"versionCreatedAt":"2025-04-03 02:52:47","video":"","vorDoi":"10.1007/s11606-025-09766-2","vorDoiUrl":"https://doi.org/10.1007/s11606-025-09766-2","workflowStages":[]},"version":"v1","identity":"rs-6347497","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6347497","identity":"rs-6347497","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}