EXploring the journeys of Patients who End their Calls prior to Triage by NHS111: The EXPECT study

EXploring the journeys of Patients who End their Calls prior to Triage by NHS111: The EXPECT study | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search EXploring the journeys of Patients who End their Calls prior to Triage by NHS111: The EXPECT study View ORCID Profile Richard Pilbery , View ORCID Profile Jen Lewis , View ORCID Profile Rebecca Simpson doi: https://doi.org/10.1101/2024.10.10.24315243 Richard Pilbery 1 Research paramedic, Yorkshire Ambulance Service NHS Trust, Springhill, Brindley Way , Wakefield 41 Business Park, Wakefield, WF2 0XQ Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Richard Pilbery For correspondence: r.pilbery{at}nhs.net Jen Lewis 2 University of Sheffield Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Jen Lewis Rebecca Simpson 2 University of Sheffield Find this author on Google Scholar Find this author on PubMed Search for this author on this site ORCID record for Rebecca Simpson Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF Abstract Background The English National Health Service (NHS) 111 telephone service aims to assist members of the public with urgent medical care needs. However, each year nearly 18% of the 20.6 million calls to 111 are abandoned prior to speaking to a health advisor. There are concerns that callers who are not triaged may not appropriately seek the correct level of care for their needs. The aim of this study was to explore the patient journey for callers who contact NHS 111 but end the call prior to speaking to a health advisor. The primary objective was to determine whether callers to NHS 111 who end the call prior to triage attend an ED with a non-avoidable cause sooner than who are triaged by an NHS 111 health advisor. Methods We obtained routine data pertaining to all NHS 111 calls made by adult patients registered with a General Practitioner (GP) in the Bradford region of Yorkshire, UK, between the 1st January 2022 and 30th June 2023. Subsequent healthcare access in the 72 hours following each caller’s first (index) call was identified using the Connected Yorkshire research database. We conducted a time-to-event analysis comparing the two cohorts: those whose index call was triaged by an NHS 111 health advisor vs. callers who ended the index call prior to triage. The ‘event’ was defined as an Emergency Department (ED) attendance within 72 hours for a non-avoidable cause. We utilised Kaplan–Meier (KM) curves and conducted log-rank tests to compare the time to first non-avoidable ED attendance between cohorts, and a Cox proportional hazards model adjusted for baseline characteristics. From this, we calculated the adjusted hazard ratio (aHR) of attending an ED with a non-avoidable cause. Results There were 19,056 index non-triaged and 168,609 triaged calls made to NHS 111 by an adult registered with a Bradford GP. A lower proportion of ED attendances in the non-triaged call cohort were non-avoidable compared with the triaged cohort (80.0% compared to 84.6% for triaged calls). In addition, callers in the non-triaged call cohort attended ED later than the triaged call cohort (median 10 vs 8 hours, p<0.001 by log rank test). The time-to-attend ED aHR for non-triaged calls vs triaged calls was 0.32 (95%CI 0.30–0.34). Conclusion The time-to-event analysis found that callers to NHS 111 who do not wait to be triaged, are slower to attend ED with a non-avoidable cause than those who are triaged, and are more likely to attend ED with an avoidable cause than triaged callers. This suggests that, for patients with a serious health problem that would be considered non-avoidable at ED, triaging by NHS 111 supports those patients to seek appropriate help more quickly. In turn, patients with such health conditions who end the call before triage may delay seeking appropriate levels of healthcare. Introduction The National Health Service (NHS) 111 service is a telephone triage service, which aims to assist members of the public with urgent medical care needs and is the successor to the NHS Direct service in England. Its key founding objective was to provide easy access to support for the public with urgent care needs, to ensure they received the “right care, from the right person, in the right place, at the right time” ( UK Government, 2011 ). It is also the key component of the “24/7 Integrated Urgent Care Service” outlined in the NHS Long Term Plan ( NHS England, 2019 ). However, in 2022, nearly 3.7 million callers to NHS 111 ended the call prior to speaking to a health advisor. This represents nearly 18% of the 20.6 million calls to NHS 111 each year ( NHS England, 2023 ) and has raised concerns about callers with urgent care needs not receiving timely care and advice ( Gregory, 2023 ). While the scale of the issue has been quantified, there appears to be no research exploring to the healthcare trajectory or health outcomes of callers who are unable to speak to an NHS 111 health advisor, or why callers do not wait to be triaged, although the delay in answering calls has been mooted as a factor ( Gregory, 2023 ). In addition, there are concerns that callers may seek alternative healthcare services, such as the ambulance service and emergency departments (EDs), but there is no evidence to confirm or refute this. The aim of this study was to compare the patient journey for callers who contact NHS 111 but end the call prior to speaking to a health advisor, and those who are triaged. The primary objective was to determine whether ending an NHS 111 call prior to triage impacts the time taken for a patient with urgent healthcare needs to attend ED. The secondary objective was to determine whether ending an NHS 111 call prior to triage impacts the time taken for a patient to attend ED regardless of urgency. Methods Data We obtained routine, retrospective data from the Connected Bradford research database, which provides linked data for approximately 1.2 million citizens across the Bradford and Airedale region of Yorkshire ( Sohal et al., 2022 ). Datasets include NHS 111 and 999 call data (including abandoned calls to NHS 111 since 2022), as well as primary and secondary care (including ED and in-patient activity for Bradford Royal Infirmary, Calderdale Royal Infirmary and Airedale General Hospital). All datasets are pseudonymised so that researchers cannot identify individual participants. A supplemental dataset containing details of callers who had contacted NHS 111, ended the call after 30 seconds but prior to being triaged by an NHS 111 health advisor, was collated and provided by analysts from Yorkshire Ambulance Service NHS Trust (YAS) to the Connected Bradford research database. They identified these callers by examining triaged NHS 111 call records in the 12 months prior to, and 1 month post, index call. Approximately 80% of these non-triaged calls were able to be matched to an NHS number in this way. The dataset for analysis comprised all NHS 111 calls made by adult (18 years and over) patients registered with a General Practitioner (GP) in the Bradford area at the time of the call between the 1st January 2022 and 30th June 2023. Index NHS 111 calls were identified as those made by patients who had not had been triaged by NHS 111 in the 72 hours prior to the first (index) call. Subsequent healthcare system access in the following 72 hours after the index call (whether triaged or not) was identified by searching the NHS 111 and 999 call, primary care, and hospital emergency department and in-patient admission datasets. Analysis We conducted descriptive and time-to-event analyses comparing the two cohorts (those triaged by an NHS 111 call handler vs callers who ended the call prior to triage). The ‘event’ was defined as an ED attendance within 72 hours for a non-avoidable cause as defined by O’Keeffe et al. (2018) . They defined an avoidable attendance as a patient presenting to a consultant-led ED which provides a 24-hour service with full resuscitation facilities and designated accommodation for the reception of emergency care patients (referred to as a type 1 ED ( NHS Digital, 2023 )), but who do not receive investigations, treatments or referral that required the facilities of that ED. In consultation with the original lead author of the avoidable attendance paper and an experienced ED consultant/clinical academic, several additional discharge codes were categorised as indicative of a potentially avoidable admission (Appendix 1). Primary analysis For the primary outcome analysis we utilised Kaplan–Meier (KM) curves and conducted a log-rank test, to compare the time to first non-avoidable ED attendance and determine whether there was a significant difference in unadjusted time-to-event between cohorts. In addition, a Cox proportional hazards model was used to adjust for clustering of results by caller, and for baseline characteristics that have been implicated as potentially affecting the outcome, including age, sex, ethnicity and index of multiple deprivation (IMD) ( Lewis et al., 2021 ; Pilbery et al., 2023 ). This enabled us to calculate the adjusted hazard ratio (aHR) of attending an ED with a non-avoidable cause for callers who ended the call prior to triage, compared to those who were triaged by a 111 call handler. Secondary analysis The secondary outcome analysis was conducted as for the primary outcome, except the event outcome was ED attendance for any cause. Proportional hazard assumptions A key consideration when undertaking a time-to-event analysis using Cox regression is ensuring that models conform to the proportion hazards assumption ( Bradburn et al. (2003) ). We checked for violations of the proportional hazards assumption by plotting log-log plots, Schoenfeld and Martingale residual plots and testing goodness-of-fit using the method described by Grambsch & Therneau (1994) . Our initial models violated these assumptions. To mitigate this, we adjusted the model by using time-splitting into hourly segments, and stratifying the analysis by the time of call (in-hours vs out-of-hours). While this improved model fit, both the cohort variable (non-triaged or triage call) and patient sex continued to violate the proportional hazards assumption to a small degree. Ethical approval This study was approved by the Bradford Learning Health System Board in accordance with the Connected Yorkshire NHS Research Ethics Committee (REC) approval relating to the Connected Yorkshire research database (17/EM/0254). No separate Health Research Authority (HRA) approval was required for this study. Patient and public involvement The application and protocol for this study was reviewed by the YAS patient research ambassador. In addition, Connected Bradford have an active patient and public involvement group who were involved in the decision to approve this study. Funding statement This study presents independent research by the NIHR Applied Research Collaboration Yorkshire and Humber (ARC YH). This work was supported by the National Institute for Health Research Applied Research Collaboration Yorkshire and Humber but the views expressed in this publication are those of the author(s) and not necessarily those of the National Institute for Health Research or the Department of Health and Social Care. Results Between the 1st January 2022 and 30th June 2023 there were 19,056 index non-triaged calls and 168,609 triaged calls to NHS 111; non-triaged calls comprised approximately 10% of all index calls made by an adult registered with a Bradford GP ( Table 1 ). A lower proportion of ED attendances in the non-triaged cohort were non-avoidable ED attendances compared with triaged calls (80.0% compared to 84.6%). In addition, callers in the non-triaged NHS 111 cohorts attended ED later than the triaged call cohort (median 10 vs 8 hours and 9 vs 7 hours for non-avoidable and all ED attendances, respectively). View this table: View inline View popup Table 1: summary of study data stratified by grouping Kaplan-Meier plots Both the Kaplan-Meier plots ( Figure 1 ) and log-rank tests suggest that there is a significant difference between the non-triaged and triaged call cohorts for non-avoidable, and all, ED attendances (log-rank test p<0.001 for both non-avoidable and all ED attendances). Download figure Open in new tab Figure 1: Kaplan-Meier plots stratified by primary and secondary outcome (note: confidence intervals are present in the figure, but are narrow) Cox regression The adjusted hazard ratio from the Cox regression suggests that non-triaged callers who have not yet attended ED for a non-avoidable cause, are around a third as likely to do so in the next hour compared to a triaged caller, for the 72-hour period following an index 111 call ( Table 2 , Appendix 2). View this table: View inline View popup Download powerpoint Table 2: Cox regression. Discussion As far as we are aware, this is the first study to examine the healthcare trajectory for callers to NHS 111 who end the call prior to triage. The time-to-event analysis suggests that callers who end their 111 call prior to triage and who have not yet attended ED for a non-avoidable cause within 72 hours following the index call, are around a third as likely to do so in the next hour compared to a triaged caller. In addition, non-triaged 111 callers, attended ED later than triaged callers. There are also several notable differences between the groups. The proportion of calls that occured out-of-hours is higher in the non-triaged call group and both the IMD and ethnicity show differences, with a higher proportion of callers in the non-triaged cohort living in the most deprived quintile. It is not clear from the data why that should be, but may indicate a health inequality issue. While there are differences in ethnicity, due to poor recording of this variable (it is missing in almost a quarter of cases), it is difficult to comment further (Scobie, 2021). Since, by definition the non-triaged callers were not triaged, it is not possible to determine the acuity level that would have been assigned to them by NHS 111. However, they did not subsequently attend ED often in the 72 hours following the index call. In the absence of literature directly relating to non-triaged NHS 111 callers, a corollary might be drawn from patients who attend ED do not wait to be seen. Encouringly, these patients were typically in a lower acuity triage category ( Bin Mohamed Ebrahim, 2021 ; Gilligan, 2009 ; Goodacre, 2005 ), although up to 5% of ‘did not waits’ in one study required a subsequent hospital admission ( Blake, 2014 ) and it is unknown how many ultimately had their healthcare need met. For callers who were triaged however, the performance of NHS 111 seems reasonable, with nearly 85% of triaged cases attending ED for a non-avoidable cause. However, we do not know whether this was directly as a result of being advised to attend by NHS 111, or the patient deciding for themselves. A previous study by Lewis et al (2021) for example, demonstrated that patients do not always follow the advice provided by NHS 111 and attend ED even if it that was not the triage disposition reached. In addition, they found that in a number of cases where attendance at ED had not been indicated by NHS 111, the patient subsequently required admission. Strengths and limitations This study has described the demographic and healthcare system access characteristics of a population who are challenging to identify. We have also highlighted that this group may delay attending ED despite having a presentation that warrants attendance. However, we are unable to determine whether there are longer term consequences of delayed (or non-) attendance at ED relating to the reason they contacted NHS 111. In addition, we have no way of knowing whether these callers utilised NHS 111 Online instead, since granular patient level data is not available in the Connected Bradford dataset for NHS 111 Online access. In addition, while the non-triaged call data did successfully identify the caller in most cases, around 20% were not identified and therefore not included. Additionally, the Cox regression did not adjust for other healthcare contacts that may have occured in the 72 hours following the index call, which may have affected the likelihood of a caller attending ED. We did intend on examining primary care contact after the NHS 111 index call but before ED attendance as a covariate, but this resulted in a model which irredeemably violated the proportional hazards assumption and so was removed. Despite mitigations aimed at resolving the proportional hazards assumption violation, we were unable to entirely avoid this, which may affect the accuracy of the results. However, reassuring log-log and Schoenfeld plots (Appendix 2) indicate this violation to be fairly minor, and suggest this may simply be a result of our large sample size and relatively high event rate. Finally, while the Connected Bradford research database has great utility for researchers wishing to explore how patients traverse the wider healthcare system, it is restricted to a discrete geographical region in West Yorkshire, which may affect the generalisability of the results we have reported. Bradford is mainly an urban area and the 13th most deprived local authority in England (out of 333) based on IMD ( City of Bradford Metropolitan District Council, 2019 ). Future work including a larger population is warranted. Conclusion The time-to-event analysis found that callers to NHS 111 who do not wait to be triaged, are slower to attend ED with a non-avoidable cause than those who are triaged and are more likely to attend ED with an avoidable cause than triaged callers. This suggests that, for patients with a serious health problem that would be considered non-avoidable at ED, triaging by NHS 111 supports those patients to seek appropriate help more quickly. In turn, patients with such health conditions who end the call before triage may delay seeking appropriate levels of healthcare. Data Availability The data used in this study was derived from the Connected Bradford research database and as such cannot be freely shared. However, access to source data can be obtained by following the Connected Bradford research database application process. Acknowledgements The authors are grateful for the support and advice provided by Colin O’Keeffe and Dr. Susan Croft in relation to the avoidable ED attendance criteria and an early draft of this paper. Footnotes email: r.pilbery{at}nhs.net References ↵ Bin Mohamed Ebrahim , M. E. , Tang , M. , Vukasovic , M. , & Coggins , A. ( 2021 ). Contemporary evaluation of adverse outcome risks associated with ‘did not wait’ emergency department presentations . Emergency Medicine Australasia , 33 ( 5 ), 932 – 934 . doi: 10.1111/1742-6723.13820 OpenUrl CrossRef ↵ Blake , D. F. , Dissanayake , D. B. , Hay , R. M. , & Brown , L. H. ( 2014 ). ‘ Did not waits’: A regional Australian emergency department experience . Emergency Medicine Australasia , 26 ( 2 ), 145 – 152 . doi: 10.1111/1742-6723.12223 OpenUrl CrossRef ↵ Bradburn , M. J. , Clark , T. G. , Love , S. B. , & Altman , D. G. ( 2003 ). Survival Analysis Part II: Multivariate data analysis – an introduction to concepts and methods . British Journal of Cancer , 89 ( 3 ), 431 – 436 . doi: 10.1038/sj.bjc.6601119 OpenUrl CrossRef PubMed Web of Science ↵ City of Bradford Metropolitan District Council . ( 2019 ). Indices of Deprivation 2019 - Intelligence bulletin . https://ubd.bradford.gov.uk/media/1534/indices-of-deprivation-2019-intelligence-bulletin.pdf ↵ Gilligan , P. , Joseph , D. , Winder , S. , Keeffe , F. O. , Oladipo , O. , Ayodele , T. , Asuquo , Q. , O’Kelly , P. , & Hegarty , D. ( 2009 ). DNW—”Did Not Wait” or “Demographic Needing Work”: A study of the profile of patients who did not wait to be seen in an Irish emergency department . Emergency Medicine Journal , 26 ( 11 ), 780 – 782 . doi: 10.1136/emj.2008.063388 OpenUrl Abstract / FREE Full Text ↵ Goodacre , S. , & Webster , A. ( 2005 ). Who waits longest in the emergency department and who leaves without being seen? Emergency Medicine Journal , 22 ( 2 ), 93 – 96 . doi: 10.1136/emj.2003.007690 OpenUrl Abstract / FREE Full Text ↵ Grambsch , P. , & Therneau , T. ( 1994 ). Proportional hazards tests and diagnostics based on weighted residuals . Biometrika , 81 ( 3 ), 515 – 526 . doi: 10.1093/biomet/81.3.515 OpenUrl CrossRef Web of Science ↵ Gregory , A. ( 2023 ). Tory “neglect” blamed for 3.6m abandoned calls to NHS 111 in England . The Guardian . https://www.theguardian.com/society/2023/apr/10/tory-neglect-blamed-for-36m-abandoned-calls-to-nhs-111-in-england ↵ Lewis , J. , Stone , T. , Simpson , R. , Jacques , R. , O’Keeffe , C. , Croft , S. , & Mason , S. ( 2021 ). Patient compliance with NHS 111 advice: Analysis of adult call and ED attendance data 2013–2017 . PLOS ONE , 16 ( 5 ), e0251362 . doi: 10.1371/journal.pone.0251362 OpenUrl CrossRef PubMed ↵ NHS Digital . ( 2023 ). Emergency care department type [Dita] . https://www.datadictionary.nhs.uk/attributes/emergency_care_department_type.html ↵ NHS England . ( 2019 ). The NHS Long Term Plan . In NHS Long Term Plan . https://www.longtermplan.nhs.uk/publication/nhs-long-term-plan/ ↵ NHS England . ( 2023 ). Integrated Urgent Care Aggregate Data Collection (IUCADC including NHS111) Statistics Apr 2022-Mar 2023 . https://www.england.nhs.uk/statistics/statistical-work-areas/iucadc-new-from-april-2021/integrated-urgent-care-aggregate-data-collection-iucadc-including-nhs111-statistics-apr-2022-mar-2023/ ↵ O’Keeffe , C. , Mason , S. , Jacques , R. , & Nicholl , J. ( 2018 ). Characterising non-urgent users of the emergency department (ED): A retrospective analysis of routine ED data . PLOS ONE , 13 ( 2 ), e0192855 . doi: 10.1371/journal.pone.0192855 OpenUrl CrossRef PubMed ↵ Pilbery , R. , Smith , M. , Green , J. , & Chalk , D. ( 2023 ). An analysis of NHS 111 demand for primary care services . medRxiv . doi: 10.1101/2023.03.20.23287449 OpenUrl Abstract / FREE Full Text Scobie , S. , Spencer , J. , & Raleigh , V. ( 2021 ). Ethnicity coding in English health service datasets . https://www.nuffieldtrust.org.uk/research/ethnicity-coding-in-english-health-service-datasets ↵ Sohal , K. , Mason , D. , Birkinshaw , J. , West , J. , McEachan , R. R. C. , Elshehaly , M. , Cooper , D. , Shore , R. , McCooe , M. , Lawton , T. , Mon-Williams , M. , Sheldon , T. , Bates , C. , Wood , M. , & Wright , J. ( 2022 ). Connected Bradford: A Whole System Data Linkage Accelerator . Wellcome Open Research , 7 , 26 . doi: 10.12688/wellcomeopenres.17526.2 OpenUrl CrossRef PubMed ↵ UK Government . ( 2011 ). NHS 111 . https://web.archive.org/web/20130415084921/ http://www.connectingforhealth.nhs.uk/systemsandservices/pathways/news/nhs111intro.pdf View the discussion thread. Back to top Previous Next Posted October 10, 2024. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. 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