Timeliness of a potential automated system for national surveillance of healthcare-associated infections in England

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Phuong Quan, David W. Eyre, Stephanie Shadwell, Daniel West, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6322379/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Surveillance of healthcare-associated infections (HCAIs) is important for public health, however data collection and reporting can be burdensome for healthcare staff. In England, local hospital groups are required to submit their monthly HCAI cases to the UK Health Security Agency (UKHSA) by the 15th day after the month end. Aim: Understand the potential timeliness of a centrally-implemented, automated HCAI surveillance system in England, using data feeds already in place at UKHSA. Methods: We set up prospective daily monitoring of existing microbiological and hospital patient data feeds at UKHSA, for seven arbitrary activity dates between 14 Nov 2022 and 1 Sep 2023. For each activity date, we counted the numbers of records relevant to that date that were available on each subsequent day, by laboratory and health provider. Findings: Although new records are received and loaded daily at UKHSA, it took 1-3 weeks for 90% of bloodstream infection records pertaining to specimens collected on a particular date to become available, up to a month for 90% of relevant admission/emergency department dates (relevant for determining onset category), and up to two months for 90% of inpatient diagnosis codes (relevant for determining risk factors). Patterns of receipt from different organisations varied. Conclusion: Implementing HCAI surveillance centrally at UKHSA using existing data feeds would mean slower ascertainment than the current system, but this should be balanced against potential gains in consistency of data across organisations and reduced workloads. Waiting times could be reduced by targeting the slowest organisations for support and/or investment. Infectious Diseases Medical Informatics Healthcare-associated infection surveillance data collection automation Figures Figure 1 Figure 2 Figure 3 INTRODUCTION Surveillance of healthcare-associated infections (HCAI) is an important public health function, and in England, details on cases of key infections are collected locally by healthcare staff and reported to the UK Health Security Agency (UKHSA) via a web-based “Data Capture System” (DCS) portal 1 . While this has been in successful operation for a number of years, the need for specialised data extraction and/or transcription from local IT systems, alongside interpreting medical records, can create a high burden on NHS infection prevention and control and medical staff and budgets. In addition to the DCS portal, the UKHSA has for many years received daily data feeds from a variety of local and national sources for use in its public health monitoring efforts. These include data on positive microbiology results as well as details of hospital admissions (including diagnosis codes that could potentially be used to obtain further information such as risk factors). If the HCAI surveillance process could be implemented centrally at UKHSA using these automated data feeds, this could potentially replace the local processes and reduce the burden on local teams. The aim of this study was to better understand how long it would take to get (near) complete receipt of data points that could potentially be used for centrally-implemented HCAI surveillance in England, since receipt patterns are currently not well understood. METHODS Microbiology data The Second Generation Surveillance System (SGSS) is a data warehouse operated by UKHSA which receives test results from the majority of microbiology laboratories in England. It is updated daily and contains two distinct modules (which are populated from separate data feeds): CDR – this contains microbiologically-confirmed infection episodes, deduplicated to a e.g. 14 or 28-day window depending on the organism AMR – this contains individual antimicrobial susceptibility test results, including both resistant and susceptible results Patient data The Secondary Uses Service (SUS+) 2 is a data warehouse operated by NHS England which contains patient-level information submitted by secondary care providers via a number of Commissioning Data Sets 3 . Among the datasets which are updated and provided to UKHSA on a daily basis are: APC - The Admitted Patient Care dataset contains data regarding inpatient admissions at both NHS and independent sector providers. It includes diagnosis codes which describe the condition(s) the patient was treated for. ECDS – the Emergency Care Data Set contains data regarding emergency department and urgent care attendances. Note, this data source is not to be confused with Hospital Episode Statistics (HES) 4 , which is a downstream product of SUS + and is only produced monthly by NHS England. Analysis methods We set up prospective daily monitoring of each data source, for seven arbitrary “activity dates” between 14 Nov 2022 and 1 Sep 2023 (14 Nov 2022, 6 Dec 2022, 9 Jan 2023, 19/24/29 Aug 2023, 1 Sep 2023). For each activity date, we created time series of the numbers of records that were available on each subsequent day, followed for at least 6 months (examples for activity date 14 Nov 2022 are shown in Fig. 1 ). Percentiles of records available on each date were based on the total number of records received at the end of the study period (22 Mar 2024). For microbiology data we counted the number of (CDR) infection episode records and the number of (AMR) susceptibility records with a particular index specimen date, for a range of bloodstream infections of differing prevalence ( E. coli , S. aureus , K. pneumoniae , Enterococcus spp. , Pseudomonas spp ., S. pneumoniae , K. oxytoca , Acinetobacter spp. ). We also counted the number of laboratories that had reported at least one of the above infections for that specimen date. For patient data we counted the number of (APC) inpatient admissions (i.e. the number admitted, number discharged, and number discharged with one or more diagnosis codes available) and the number of (ECDS) emergency department attendances commencing on a particular activity date. We also counted the number of providers that had reported at least one record for that activity date. Outpatient attendances were not considered. Data and code availability The dataset generated and analysed for this study is available from the Zenodo repository, [https://zenodo.org/doi/ 10.5281/zenodo.12805820 ]. All names of laboratories and health providers have been anonymised. All analyses were conducted using R v4.3.1, and the code is openly available from Zenodo [https://zenodo.org/doi/ 10.5281/zenodo.13354506 ] and GitHub [ https://github.com/oxfordmmm/ukhsa-datafeeds-timeliness-anon ]. RESULTS Microbiology data There were 105 and 99 laboratories that reported bloodstream infection data to the CDR and AMR modules respectively in SGSS during the period of monitoring (14 Nov 2022 through 22 Mar 2024). Combining all the organisms investigated, at the end of the study period (22 Mar 2024) there were between 233–305 CDR records on each of the 7 specimen activity dates, with 50% of the records available within 4–5 days (range across the 7 activity dates), 90% within 8–16 days, and 99% within 76–170 days (Fig. 2A). This varied across different organisms; rarer ones like Acinetobacter spp. were more variable with 90% of records available within 8-183 days, whereas for E. coli 90% of records were available within 7–12 days after the activity date. Of all the laboratories that eventually reported a specimen for the 7 activity dates, 50% sent their first (last) record within 4 (5–6) days, 90% within 6–9 (11–51) days, and 99% within 62–183 (81–283) days (Fig. 2B). Similarly, at the end of the study period (22 Mar 2024) there were between 3133–4136 AMR records on each of the 7 activity dates, with 50% of the records available within 5–9 days, 90% within 10–23 days, and 99% within 21–95 days (Fig. 2A). Again, rarer organisms like Acinetobacter spp. were more variable with 90% of records available within 6-183 days whereas for E. coli 90% of records were available within 13–29 days. Of all the laboratories that eventually reported a specimen for those dates, 50% sent their first (last) record within 4–9 (6–9) days, 90% within 18–29 (22–34) days, and 99% within 92–183 (97–353) days (Fig. 2B). Patient data There was a period of disruption in the loading of APC and ECDS records in August and September 2023, with delays to loading and some temporary instability in the cumulative numbers of records. These activity dates have been included in the analyses but presented separately where appropriate. There were 492 and 191 providers that reported APC and ECDS data respectively during the study period. For all inpatient admissions on the 7 activity dates, 90% of admission records were available within 16–29 days, 90% of discharge dates were available within 18–34 days, and 90% of diagnosis codes available within 52–64 days (Fig. 3A). Of all the providers that eventually reported an admission on the activity dates, 50% sent their first (last) record within 8–26 (58–95) days, 90% within 31–49 (85–202) days, and 99% within 85–110 (97–338) days (Fig. 3B). 64–72% of providers did not submit a record until a discharge date was present (based on whether or not their first submission per activity date contained any records without discharge dates), and 30–41% of providers did not submit a record until both a discharge date and diagnosis codes were present. For emergency department attendances, due to the loading disruption, 4 of the activity dates had the vast majority of their records bulk loaded on a single day, and so were not representative of the time it took for providers to send their data. For the 3 dates that were not disrupted, 50% of records arrived within 3 days, 90% within 8–9 days, and 99% within 52–74 days (Fig. 3A). In contrast, for the 4 disrupted activity dates, 50% of the records arrived within 3–16 days, 90% within 12–17 days, and 99% within 107–120 days. Of all the providers that eventually reported an attendance on the 7 activity dates, 50% sent their first (last) record within 3–16 (4–16) days, 90% within 9–26 (25–64) days, and 99% within 52–187 (134–190) days (Fig. 3B). DISCUSSION New microbiology and patient records are received and loaded at UKHSA every day, however it can take 1–3 weeks for 90% of bloodstream infection records pertaining to specimens collected on a particular date to become available, up to a month for 90% of relevant admission/emergency department dates (relevant for determining onset category), and up to two months for 90% of inpatient diagnosis codes (relevant for determining risk factors). This is likely because many laboratories and providers send their data in batches rather than in daily instalments. It can sometimes take a year for final records to arrive, for unclear reasons. Occasionally, the number of records and laboratories/providers decreased from one day to the next. It is not unexpected for data that has previously been loaded to require modification at a later date, including where that may result in deletion of records, though further detailed investigation would be beneficial to understand the full range of causes and implications of this. Also, loading disruptions can occur unexpectedly and can take time to rectify, such as happened for the patient data during August/September 2023. Automated processes need regular monitoring and maintenance to ensure that they are functioning as expected and to avoid missing data. A limitation of the study is that it only tracked the total numbers of records on each day, and did not track changes to records that had been loaded on previous days. In combination with the previous point, this means that one cannot simply assume that raw data can be used as soon as it arrives; it will also be necessary to establish how long the data should be treated as provisional and when it can be considered to be stable and “complete”. Currently, local hospital groups are required to submit and sign off their monthly HCAI cases by the 15th day after the month end, with UKHSA publishing these numbers a month later. If HCAI cases were to be identified using UKHSA’s system of automated data feeds instead, these deadlines would need to be reconsidered. However, any delay in reporting should be balanced against the potential savings in local teams’ workload, as well as the subsequent consistency of implementation across different hospitals, particularly for optional data fields such as risk factors. Also, the current time-to-availability of relevant records at UKHSA is not a hard limit and could potentially be reduced with improved incentives or infrastructure, particularly at the local laboratories and providers that are currently slowest. As a comparison, during the first pandemic wave of COVID-19 in England, around 85% of laboratory-confirmed cases of COVID-19 were available in SGSS within 4 days of specimen collection, with over 90% of records loaded into SGSS within one day of receipt 5 . While microbiological data is sent directly to UKHSA, patient data is first collated and processed by NHS England, which means that it necessarily arrives later. Therefore, a multi-stage approach to case completion might be appropriate, with the simple notification of the infection first, followed by identification of onset category and other relevant patient information when it becomes available. Local hospital staff could then only be asked to fill in any data not available from the automated system or alternatively just for a focussed subset of cases. CONCLUSIONS HCAI surveillance is a labour-intensive process and so automation is a desirable goal, but implementation at a national level is still rare 6 and the capabilities and limitations of automated data feeds not widely understood. In England, a centrally-implemented system based on UKHSA’s existing data feeds would have slower ascertainment than the current system of data submissions by local teams, but this could be improved with targeted investment. Nevertheless, slower ascertainment should be balanced against potential gains in consistency of implementation across different hospitals and of reduced workloads. Declarations Ethical statement The study was conducted in accordance with relevant UK guidelines and regulations. Datasets were processed under Regulation 3 of the Health Service (Control of Patient Information) Regulations 2002, within a secure environment at UKHSA by approved personnel. No further ethical approval or individual consent was required as the study was conducted to inform service development for routine surveillance, and only use routinely collected, secondary data, with no experimental components. Acknowledgements We would like to thank the HPRU Steering Group. Conflict of interest The authors declare no competing interests. Funding This study was funded by the National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University in partnership with the UK Health Security Agency (UKHSA) (NIHR200915), and supported by the NIHR Biomedical Research Centre, Oxford. ASW is an NIHR Senior Investigator. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Department of Health or the UKHSA. References UK Health Security Agency. Mandatory enhanced MRSA, MSSA and Gram-negative bacteraemia, and Clostridioides difficile infection surveillance: Protocol version 4.4: UK Health Security Agency, 2021. NHS Digital. Secondary Uses Service (SUS) [Available from: https://digital.nhs.uk/services/secondary-uses-service-sus accessed 22 December 2023. NHS Digital. Commissioning Data Sets [Available from: https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/commissioning-data-sets accessed 10 April 2024. NHS Digital. Hospital Episode Statistics (HES) [Available from: https://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/hospital-episode-statistics accessed 23 January 2024. Clare T, Twohig KA, O'Connell AM, et al. Timeliness and completeness of laboratory-based surveillance of COVID-19 cases in England. Public Health 2021;194:163-66. doi: 10.1016/j.puhe.2021.03.012 [published Online First: 2021/05/05] van Mourik MSM, van Rooden SM, Abbas M, et al. PRAISE: providing a roadmap for automated infection surveillance in Europe. Clin Microbiol Infect 2021;27 Suppl 1:S3-S19. doi: 10.1016/j.cmi.2021.02.028 [published Online First: 2021/07/05] Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted 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-6322379","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":434966693,"identity":"c988de81-b2e6-4da5-a926-394d12c396f8","order_by":0,"name":"T. 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Eyre","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"David","middleName":"W.","lastName":"Eyre","suffix":""},{"id":434966695,"identity":"8db0aa71-a5a3-40d7-9219-2e0d08344124","order_by":2,"name":"Stephanie Shadwell","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Stephanie","middleName":"","lastName":"Shadwell","suffix":""},{"id":434966696,"identity":"d975c6b3-f7be-49e0-83b7-4d98befb8918","order_by":3,"name":"Daniel West","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Daniel","middleName":"","lastName":"West","suffix":""},{"id":434966697,"identity":"28098315-6dc1-42a2-899d-ee2840e729b8","order_by":4,"name":"Susan Hopkins","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Susan","middleName":"","lastName":"Hopkins","suffix":""},{"id":434966698,"identity":"76d3819a-d33c-40e7-9c15-f2d1af0b0d79","order_by":5,"name":"Dimple Chudasama","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Dimple","middleName":"","lastName":"Chudasama","suffix":""},{"id":434966699,"identity":"41ccf11e-e73c-42cf-9805-40bb4d30b320","order_by":6,"name":"Berit Muller-Pebody","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Berit","middleName":"","lastName":"Muller-Pebody","suffix":""},{"id":434966700,"identity":"3774d4f3-64c2-42a3-ba67-eeb17ce2c478","order_by":7,"name":"Russell Hope","email":"","orcid":"","institution":"UK Health Security Agency","correspondingAuthor":false,"prefix":"","firstName":"Russell","middleName":"","lastName":"Hope","suffix":""},{"id":434966701,"identity":"e217511c-7b95-4a0e-b1ac-b0dc9d6a3957","order_by":8,"name":"A. Sarah Walker","email":"","orcid":"","institution":"University of Oxford","correspondingAuthor":false,"prefix":"","firstName":"A.","middleName":"Sarah","lastName":"Walker","suffix":""}],"badges":[],"createdAt":"2025-03-27 16:55:51","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-6322379/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6322379/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79586612,"identity":"78e2b675-446a-487a-a03e-0ecaabfa289b","added_by":"auto","created_at":"2025-03-31 12:34:33","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":111323,"visible":true,"origin":"","legend":"\u003cp\u003eA selection of time series showing the availability of records for the specimen/admission/arrival date of 14 Nov 2022, on each subsequent day until the end of the study period (22 Mar 2024). \u003cstrong\u003eA.\u003c/strong\u003e Number of infection episode (CDR) records, for a selection of bacteraemias. \u003cstrong\u003eB.\u003c/strong\u003e Number of laboratories with at least one susceptibility (AMR) record, for a selection of bacteraemias. \u003cstrong\u003eC.\u003c/strong\u003e Number of inpatient admission (APC) records. \u003cstrong\u003eD.\u003c/strong\u003eNumber of providers with at least one emergency department (ECDS) record.\u003c/p\u003e","description":"","filename":"Fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-6322379/v1/e869c328324b6917ea7181d2.png"},{"id":79586608,"identity":"d410939d-fa0e-438d-bc2f-c0112aee306d","added_by":"auto","created_at":"2025-03-31 12:34:33","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":84897,"visible":true,"origin":"","legend":"\u003cp\u003eTime to availability of microbiology data, across all seven activity dates. \u003cstrong\u003eA.\u003c/strong\u003eTime taken for the majority of infection episode (CDR) and antimicrobial susceptibility (AMR) records to become available. \u003cstrong\u003eB.\u003c/strong\u003e Time taken for the first and last infection episode (CDR) and antimicrobial susceptibility (AMR) record to become available from the majority of laboratories. Note: four of the activity dates had shorter follow-up periods of 203-216 days, so the time until the last records arrived for those activity dates would likely be higher if the study had continued for longer.\u003c/p\u003e","description":"","filename":"Fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-6322379/v1/afc72a1f44d72e2dd773c57b.png"},{"id":79586609,"identity":"d2ed5da4-cec8-442a-8d16-c8325d6929d5","added_by":"auto","created_at":"2025-03-31 12:34:33","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":114206,"visible":true,"origin":"","legend":"\u003cp\u003eTime to availability of patient data, across all seven activity dates. \u003cstrong\u003eA.\u003c/strong\u003eTime taken for the majority of inpatient admission (APC) and emergency department (ECDS) records to become available. \u003cstrong\u003eB.\u003c/strong\u003e Time taken for the first and last inpatient admission (APC) and emergency department (ECDS) record to become available from the majority of providers. Note: the four disrupted activity dates had shorter follow-up periods of 203-216 days, so the time until the last records arrived for those activity dates would likely be higher if the study had continued for longer.\u003c/p\u003e","description":"","filename":"Fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-6322379/v1/2fb3d3c5884a0e02e4ff4f95.png"},{"id":79588463,"identity":"e0e2b5b0-e7f1-4b5d-9837-2d8e8a0fec85","added_by":"auto","created_at":"2025-03-31 12:50:33","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":732743,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6322379/v1/f59156e0-c361-4004-b515-16430e1b28d6.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eTimeliness of a potential automated system for national surveillance of healthcare-associated infections in England\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eSurveillance of healthcare-associated infections (HCAI) is an important public health function, and in England, details on cases of key infections are collected locally by healthcare staff and reported to the UK Health Security Agency (UKHSA) via a web-based \u0026ldquo;Data Capture System\u0026rdquo; (DCS) portal\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. While this has been in successful operation for a number of years, the need for specialised data extraction and/or transcription from local IT systems, alongside interpreting medical records, can create a high burden on NHS infection prevention and control and medical staff and budgets.\u003c/p\u003e \u003cp\u003eIn addition to the DCS portal, the UKHSA has for many years received daily data feeds from a variety of local and national sources for use in its public health monitoring efforts. These include data on positive microbiology results as well as details of hospital admissions (including diagnosis codes that could potentially be used to obtain further information such as risk factors). If the HCAI surveillance process could be implemented centrally at UKHSA using these automated data feeds, this could potentially replace the local processes and reduce the burden on local teams.\u003c/p\u003e \u003cp\u003eThe aim of this study was to better understand how long it would take to get (near) complete receipt of data points that could potentially be used for centrally-implemented HCAI surveillance in England, since receipt patterns are currently not well understood.\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiology data\u003c/h2\u003e \u003cp\u003eThe Second Generation Surveillance System (SGSS) is a data warehouse operated by UKHSA which receives test results from the majority of microbiology laboratories in England. It is updated daily and contains two distinct modules (which are populated from separate data feeds):\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eCDR\u003c/b\u003e \u0026ndash; this contains microbiologically-confirmed infection episodes, deduplicated to a e.g. 14 or 28-day window depending on the organism\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAMR\u003c/b\u003e \u0026ndash; this contains individual antimicrobial susceptibility test results, including both resistant and susceptible results\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient data \u003c/h3\u003e\n\u003cp\u003eThe Secondary Uses Service (SUS+)\u003csup\u003e2\u003c/sup\u003e is a data warehouse operated by NHS England which contains patient-level information submitted by secondary care providers via a number of Commissioning Data Sets\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. Among the datasets which are updated and provided to UKHSA on a daily basis are:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eAPC\u003c/b\u003e - The Admitted Patient Care dataset contains data regarding inpatient admissions at both NHS and independent sector providers. It includes diagnosis codes which describe the condition(s) the patient was treated for.\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cb\u003eECDS\u003c/b\u003e \u0026ndash; the Emergency Care Data Set contains data regarding emergency department and urgent care attendances.\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eNote, this data source is not to be confused with Hospital Episode Statistics (HES)\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, which is a downstream product of SUS\u0026thinsp;+\u0026thinsp;and is only produced monthly by NHS England.\u003c/p\u003e\n\u003ch3\u003eAnalysis methods\u003c/h3\u003e\n\u003cp\u003eWe set up prospective daily monitoring of each data source, for seven arbitrary \u0026ldquo;activity dates\u0026rdquo; between 14 Nov 2022 and 1 Sep 2023 (14 Nov 2022, 6 Dec 2022, 9 Jan 2023, 19/24/29 Aug 2023, 1 Sep 2023). For each activity date, we created time series of the numbers of records that were available on each subsequent day, followed for at least 6 months (examples for activity date 14 Nov 2022 are shown in \u003cb\u003eFig.\u0026nbsp;1\u003c/b\u003e). Percentiles of records available on each date were based on the total number of records received at the end of the study period (22 Mar 2024).\u003c/p\u003e \u003cp\u003eFor microbiology data we counted the number of (CDR) infection episode records and the number of (AMR) susceptibility records with a particular index specimen date, for a range of bloodstream infections of differing prevalence (\u003cem\u003eE. coli\u003c/em\u003e, \u003cem\u003eS. aureus\u003c/em\u003e, \u003cem\u003eK. pneumoniae\u003c/em\u003e, \u003cem\u003eEnterococcus spp.\u003c/em\u003e, \u003cem\u003ePseudomonas spp\u003c/em\u003e., \u003cem\u003eS. pneumoniae\u003c/em\u003e, \u003cem\u003eK. oxytoca\u003c/em\u003e, \u003cem\u003eAcinetobacter spp.\u003c/em\u003e). We also counted the number of laboratories that had reported at least one of the above infections for that specimen date.\u003c/p\u003e \u003cp\u003eFor patient data we counted the number of (APC) inpatient admissions (i.e. the number admitted, number discharged, and number discharged with one or more diagnosis codes available) and the number of (ECDS) emergency department attendances commencing on a particular activity date. We also counted the number of providers that had reported at least one record for that activity date. Outpatient attendances were not considered.\u003c/p\u003e\n\u003ch3\u003eData and code availability\u003c/h3\u003e\n\u003cp\u003eThe dataset generated and analysed for this study is available from the Zenodo repository, [https://zenodo.org/doi/\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.12805820\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.12805820\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e]. All names of laboratories and health providers have been anonymised. All analyses were conducted using R v4.3.1, and the code is openly available from Zenodo [https://zenodo.org/doi/\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.5281/zenodo.13354506\u003c/span\u003e\u003cspan address=\"10.5281/zenodo.13354506\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e] and GitHub [\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/oxfordmmm/ukhsa-datafeeds-timeliness-anon\u003c/span\u003e\u003cspan address=\"https://github.com/oxfordmmm/ukhsa-datafeeds-timeliness-anon\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e].\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eMicrobiology data\u003c/h2\u003e \u003cp\u003eThere were 105 and 99 laboratories that reported bloodstream infection data to the CDR and AMR modules respectively in SGSS during the period of monitoring (14 Nov 2022 through 22 Mar 2024).\u003c/p\u003e \u003cp\u003eCombining all the organisms investigated, at the end of the study period (22 Mar 2024) there were between 233\u0026ndash;305 CDR records on each of the 7 specimen activity dates, with 50% of the records available within 4\u0026ndash;5 days (range across the 7 activity dates), 90% within 8\u0026ndash;16 days, and 99% within 76\u0026ndash;170 days (Fig.\u0026nbsp;2A). This varied across different organisms; rarer ones like \u003cem\u003eAcinetobacter spp.\u003c/em\u003e were more variable with 90% of records available within 8-183 days, whereas for \u003cem\u003eE. coli\u003c/em\u003e 90% of records were available within 7\u0026ndash;12 days after the activity date. Of all the laboratories that eventually reported a specimen for the 7 activity dates, 50% sent their first (last) record within 4 (5\u0026ndash;6) days, 90% within 6\u0026ndash;9 (11\u0026ndash;51) days, and 99% within 62\u0026ndash;183 (81\u0026ndash;283) days (Fig.\u0026nbsp;2B).\u003c/p\u003e \u003cp\u003eSimilarly, at the end of the study period (22 Mar 2024) there were between 3133\u0026ndash;4136 AMR records on each of the 7 activity dates, with 50% of the records available within 5\u0026ndash;9 days, 90% within 10\u0026ndash;23 days, and 99% within 21\u0026ndash;95 days (Fig.\u0026nbsp;2A). Again, rarer organisms like \u003cem\u003eAcinetobacter spp.\u003c/em\u003e were more variable with 90% of records available within 6-183 days whereas for \u003cem\u003eE. coli\u003c/em\u003e 90% of records were available within 13\u0026ndash;29 days. Of all the laboratories that eventually reported a specimen for those dates, 50% sent their first (last) record within 4\u0026ndash;9 (6\u0026ndash;9) days, 90% within 18\u0026ndash;29 (22\u0026ndash;34) days, and 99% within 92\u0026ndash;183 (97\u0026ndash;353) days (Fig.\u0026nbsp;2B).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient data\u003c/h3\u003e\n\u003cp\u003eThere was a period of disruption in the loading of APC and ECDS records in August and September 2023, with delays to loading and some temporary instability in the cumulative numbers of records. These activity dates have been included in the analyses but presented separately where appropriate.\u003c/p\u003e \u003cp\u003eThere were 492 and 191 providers that reported APC and ECDS data respectively during the study period.\u003c/p\u003e \u003cp\u003eFor all inpatient admissions on the 7 activity dates, 90% of admission records were available within 16\u0026ndash;29 days, 90% of discharge dates were available within 18\u0026ndash;34 days, and 90% of diagnosis codes available within 52\u0026ndash;64 days (Fig.\u0026nbsp;3A). Of all the providers that eventually reported an admission on the activity dates, 50% sent their first (last) record within 8\u0026ndash;26 (58\u0026ndash;95) days, 90% within 31\u0026ndash;49 (85\u0026ndash;202) days, and 99% within 85\u0026ndash;110 (97\u0026ndash;338) days (Fig.\u0026nbsp;3B). 64\u0026ndash;72% of providers did not submit a record until a discharge date was present (based on whether or not their first submission per activity date contained any records without discharge dates), and 30\u0026ndash;41% of providers did not submit a record until both a discharge date and diagnosis codes were present.\u003c/p\u003e \u003cp\u003eFor emergency department attendances, due to the loading disruption, 4 of the activity dates had the vast majority of their records bulk loaded on a single day, and so were not representative of the time it took for providers to send their data. For the 3 dates that were not disrupted, 50% of records arrived within 3 days, 90% within 8\u0026ndash;9 days, and 99% within 52\u0026ndash;74 days (Fig.\u0026nbsp;3A). In contrast, for the 4 disrupted activity dates, 50% of the records arrived within 3\u0026ndash;16 days, 90% within 12\u0026ndash;17 days, and 99% within 107\u0026ndash;120 days. Of all the providers that eventually reported an attendance on the 7 activity dates, 50% sent their first (last) record within 3\u0026ndash;16 (4\u0026ndash;16) days, 90% within 9\u0026ndash;26 (25\u0026ndash;64) days, and 99% within 52\u0026ndash;187 (134\u0026ndash;190) days (Fig.\u0026nbsp;3B).\u003c/p\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eNew microbiology and patient records are received and loaded at UKHSA every day, however it can take 1\u0026ndash;3 weeks for 90% of bloodstream infection records pertaining to specimens collected on a particular date to become available, up to a month for 90% of relevant admission/emergency department dates (relevant for determining onset category), and up to two months for 90% of inpatient diagnosis codes (relevant for determining risk factors). This is likely because many laboratories and providers send their data in batches rather than in daily instalments. It can sometimes take a year for final records to arrive, for unclear reasons.\u003c/p\u003e \u003cp\u003eOccasionally, the number of records and laboratories/providers decreased from one day to the next. It is not unexpected for data that has previously been loaded to require modification at a later date, including where that may result in deletion of records, though further detailed investigation would be beneficial to understand the full range of causes and implications of this. Also, loading disruptions can occur unexpectedly and can take time to rectify, such as happened for the patient data during August/September 2023. Automated processes need regular monitoring and maintenance to ensure that they are functioning as expected and to avoid missing data.\u003c/p\u003e \u003cp\u003eA limitation of the study is that it only tracked the total numbers of records on each day, and did not track changes to records that had been loaded on previous days. In combination with the previous point, this means that one cannot simply assume that raw data can be used as soon as it arrives; it will also be necessary to establish how long the data should be treated as provisional and when it can be considered to be stable and \u0026ldquo;complete\u0026rdquo;.\u003c/p\u003e \u003cp\u003eCurrently, local hospital groups are required to submit and sign off their monthly HCAI cases by the 15th day after the month end, with UKHSA publishing these numbers a month later. If HCAI cases were to be identified using UKHSA\u0026rsquo;s system of automated data feeds instead, these deadlines would need to be reconsidered. However, any delay in reporting should be balanced against the potential savings in local teams\u0026rsquo; workload, as well as the subsequent consistency of implementation across different hospitals, particularly for optional data fields such as risk factors. Also, the current time-to-availability of relevant records at UKHSA is not a hard limit and could potentially be reduced with improved incentives or infrastructure, particularly at the local laboratories and providers that are currently slowest. As a comparison, during the first pandemic wave of COVID-19 in England, around 85% of laboratory-confirmed cases of COVID-19 were available in SGSS within 4 days of specimen collection, with over 90% of records loaded into SGSS within one day of receipt\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eWhile microbiological data is sent directly to UKHSA, patient data is first collated and processed by NHS England, which means that it necessarily arrives later. Therefore, a multi-stage approach to case completion might be appropriate, with the simple notification of the infection first, followed by identification of onset category and other relevant patient information when it becomes available. Local hospital staff could then only be asked to fill in any data not available from the automated system or alternatively just for a focussed subset of cases.\u003c/p\u003e"},{"header":"CONCLUSIONS","content":"\u003cp\u003eHCAI surveillance is a labour-intensive process and so automation is a desirable goal, but implementation at a national level is still rare\u003csup\u003e\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u003c/sup\u003e and the capabilities and limitations of automated data feeds not widely understood. In England, a centrally-implemented system based on UKHSA\u0026rsquo;s existing data feeds would have slower ascertainment than the current system of data submissions by local teams, but this could be improved with targeted investment. Nevertheless, slower ascertainment should be balanced against potential gains in consistency of implementation across different hospitals and of reduced workloads.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthical statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was conducted in accordance with relevant UK guidelines and regulations. Datasets were processed under Regulation 3 of the Health Service (Control of Patient Information) Regulations 2002, within a secure environment at UKHSA by approved personnel. No further ethical approval or individual consent was required as the study was conducted to inform service development for routine surveillance, and only use routinely collected, secondary data, with no experimental components.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe would like to thank the HPRU Steering Group.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the National Institute for Health Research (NIHR) Health Protection Research Unit in Healthcare Associated Infections and Antimicrobial Resistance at Oxford University in partnership with the UK Health Security Agency (UKHSA) (NIHR200915), and supported by the NIHR Biomedical Research Centre, Oxford. ASW is an NIHR Senior Investigator. The views expressed in this publication are those of the authors and not necessarily those of the NHS, the National Institute for Health Research, the Department of Health or the UKHSA.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eUK Health Security Agency. Mandatory enhanced MRSA, MSSA and Gram-negative bacteraemia, and Clostridioides difficile infection surveillance: Protocol version 4.4: UK Health Security Agency, 2021.\u003c/li\u003e\n \u003cli\u003eNHS Digital. Secondary Uses Service (SUS) [Available from: https://digital.nhs.uk/services/secondary-uses-service-sus accessed 22 December 2023.\u003c/li\u003e\n \u003cli\u003eNHS Digital. Commissioning Data Sets [Available from: https://digital.nhs.uk/data-and-information/data-collections-and-data-sets/data-sets/commissioning-data-sets accessed 10 April 2024.\u003c/li\u003e\n \u003cli\u003eNHS Digital. Hospital Episode Statistics (HES) [Available from: https://digital.nhs.uk/data-and-information/data-tools-and-services/data-services/hospital-episode-statistics accessed 23 January 2024.\u003c/li\u003e\n \u003cli\u003eClare T, Twohig KA, O'Connell AM, et al. Timeliness and completeness of laboratory-based surveillance of COVID-19 cases in England. \u003cem\u003ePublic Health\u003c/em\u003e 2021;194:163-66. doi: 10.1016/j.puhe.2021.03.012 [published Online First: 2021/05/05]\u003c/li\u003e\n \u003cli\u003evan Mourik MSM, van Rooden SM, Abbas M, et al. PRAISE: providing a roadmap for automated infection surveillance in Europe. \u003cem\u003eClin Microbiol Infect\u003c/em\u003e 2021;27 Suppl 1:S3-S19. doi: 10.1016/j.cmi.2021.02.028 [published Online First: 2021/07/05]\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[{"identity":"7036c2f6-4c6b-47c8-93b9-f34b32a84b2e","identifier":"10.13039/501100000272","name":"National Institute for Health Research","awardNumber":"NIHR200915","order_by":0},{"identity":"559df3c9-face-499c-b2b3-83f2d5e4c66f","identifier":"10.13039/501100013373","name":"NIHR Oxford Biomedical Research Centre","awardNumber":"N/A","order_by":1}],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University of Oxford","isAcceptedByJournal":false,"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":"Healthcare-associated infection, surveillance, data collection, automation","lastPublishedDoi":"10.21203/rs.3.rs-6322379/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6322379/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Surveillance of healthcare-associated infections (HCAIs) is important for public health, however data collection and reporting can be burdensome for healthcare staff. In England, local hospital groups are required to submit their monthly HCAI cases to the UK Health Security Agency (UKHSA) by the 15th day after the month end.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAim: \u003c/strong\u003eUnderstand the potential timeliness of a centrally-implemented, automated HCAI surveillance system in England, using data feeds already in place at UKHSA.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We set up prospective daily monitoring of existing microbiological and hospital patient data feeds at UKHSA, for seven arbitrary activity dates between 14 Nov 2022 and 1 Sep 2023. For each activity date, we counted the numbers of records relevant to that date that were available on each subsequent day, by laboratory and health provider.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings:\u003c/strong\u003e Although new records are received and loaded daily at UKHSA, it took 1-3 weeks for 90% of bloodstream infection records pertaining to specimens collected on a particular date to become available, up to a month for 90% of relevant admission/emergency department dates (relevant for determining onset category), and up to two months for 90% of inpatient diagnosis codes (relevant for determining risk factors). Patterns of receipt from different organisations varied.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Implementing HCAI surveillance centrally at UKHSA using existing data feeds would mean slower ascertainment than the current system, but this should be balanced against potential gains in consistency of data across organisations and reduced workloads. Waiting times could be reduced by targeting the slowest organisations for support and/or investment.\u003c/p\u003e","manuscriptTitle":"Timeliness of a potential automated system for national surveillance of healthcare-associated infections in England","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-31 12:34:28","doi":"10.21203/rs.3.rs-6322379/v1","editorialEvents":[{"type":"communityComments","content":0}],"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":"fe329871-4960-4649-9f8c-623b412e4d34","owner":[],"postedDate":"March 31st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":46316434,"name":"Infectious Diseases"},{"id":46316435,"name":"Medical Informatics"}],"tags":[],"updatedAt":"2025-03-31T12:34:28+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-31 12:34:28","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6322379","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6322379","identity":"rs-6322379","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}