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Modelling NHS England 111 demand for primary care services: a discrete event simulation
OBJECTIVES: This feasibility study aimed to model in silico the current healthcare system for patients triaged to a primary care disposition following a call to National Health Service (NHS) 111 and determine the effect of reconfiguring the healthcare system to ensure a timely primary care service c...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496671/ https://www.ncbi.nlm.nih.gov/pubmed/37673448 http://dx.doi.org/10.1136/bmjopen-2023-076203 |
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author | Pilbery, Richard Smith, Madeleine Green, Jonathan Chalk, Daniel O'Keeffe, Colin A |
author_facet | Pilbery, Richard Smith, Madeleine Green, Jonathan Chalk, Daniel O'Keeffe, Colin A |
author_sort | Pilbery, Richard |
collection | PubMed |
description | OBJECTIVES: This feasibility study aimed to model in silico the current healthcare system for patients triaged to a primary care disposition following a call to National Health Service (NHS) 111 and determine the effect of reconfiguring the healthcare system to ensure a timely primary care service contact. DESIGN: Discrete event simulation. SETTING: Single English NHS 111 call centre in Yorkshire. PARTICIPANTS: Callers registered with a Bradford general practitioner who contacted the NHS 111 service in 2021 and were triaged to a primary care disposition. PRIMARY AND SECONDARY OUTCOME MEASURES: Face validity of conceptual model. Comparison between real and simulated data for quarterly counts (and 95% CIs) for patient contact with emergency ambulance (999), 111, and primary and secondary care services. Mean difference and 95% CIs in healthcare system usage between simulations and difference in mean proportion of avoidable admissions for callers who presented to an emergency department (ED). RESULTS: The simulation of the current system estimated that there would be 39 283 (95% CI 39 237 to 39 328) primary care contacts, 2042 (95% CI 2032 to 2051) 999 calls and 1120 (95% CI 1114 to 1127) avoidable ED attendances. Modifying the model to ensure a timely primary care response resulted in a mean percentage increase of 196.1% (95% CI 192.2% to 199.9%) in primary care contacts, and a mean percentage decrease of 78.0% (95% CI 69.8% to 86.2%) in 999 calls and 88.1% (95% CI 81.7% to 94.5%) in ED attendances. Avoidable ED attendances reduced by a mean of −26 (95% CI −35 to −17). CONCLUSION: In this simulated study, ensuring timely contact with a primary care service would lead to a significant reduction in 999 and 111 calls, and ED attendances (although not avoidable ED attendance). However, this is likely to be impractical given the need to almost double current primary care service provision. Further economic and qualitative research is needed to determine whether this intervention would be cost-effective and acceptable to both patients and primary care clinicians. |
format | Online Article Text |
id | pubmed-10496671 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-104966712023-09-13 Modelling NHS England 111 demand for primary care services: a discrete event simulation Pilbery, Richard Smith, Madeleine Green, Jonathan Chalk, Daniel O'Keeffe, Colin A BMJ Open Health Informatics OBJECTIVES: This feasibility study aimed to model in silico the current healthcare system for patients triaged to a primary care disposition following a call to National Health Service (NHS) 111 and determine the effect of reconfiguring the healthcare system to ensure a timely primary care service contact. DESIGN: Discrete event simulation. SETTING: Single English NHS 111 call centre in Yorkshire. PARTICIPANTS: Callers registered with a Bradford general practitioner who contacted the NHS 111 service in 2021 and were triaged to a primary care disposition. PRIMARY AND SECONDARY OUTCOME MEASURES: Face validity of conceptual model. Comparison between real and simulated data for quarterly counts (and 95% CIs) for patient contact with emergency ambulance (999), 111, and primary and secondary care services. Mean difference and 95% CIs in healthcare system usage between simulations and difference in mean proportion of avoidable admissions for callers who presented to an emergency department (ED). RESULTS: The simulation of the current system estimated that there would be 39 283 (95% CI 39 237 to 39 328) primary care contacts, 2042 (95% CI 2032 to 2051) 999 calls and 1120 (95% CI 1114 to 1127) avoidable ED attendances. Modifying the model to ensure a timely primary care response resulted in a mean percentage increase of 196.1% (95% CI 192.2% to 199.9%) in primary care contacts, and a mean percentage decrease of 78.0% (95% CI 69.8% to 86.2%) in 999 calls and 88.1% (95% CI 81.7% to 94.5%) in ED attendances. Avoidable ED attendances reduced by a mean of −26 (95% CI −35 to −17). CONCLUSION: In this simulated study, ensuring timely contact with a primary care service would lead to a significant reduction in 999 and 111 calls, and ED attendances (although not avoidable ED attendance). However, this is likely to be impractical given the need to almost double current primary care service provision. Further economic and qualitative research is needed to determine whether this intervention would be cost-effective and acceptable to both patients and primary care clinicians. BMJ Publishing Group 2023-09-06 /pmc/articles/PMC10496671/ /pubmed/37673448 http://dx.doi.org/10.1136/bmjopen-2023-076203 Text en © Author(s) (or their employer(s)) 2023. Re-use permitted under CC BY. Published by BMJ. https://creativecommons.org/licenses/by/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution 4.0 Unported (CC BY 4.0) license, which permits others to copy, redistribute, remix, transform and build upon this work for any purpose, provided the original work is properly cited, a link to the licence is given, and indication of whether changes were made. See: https://creativecommons.org/licenses/by/4.0/. |
spellingShingle | Health Informatics Pilbery, Richard Smith, Madeleine Green, Jonathan Chalk, Daniel O'Keeffe, Colin A Modelling NHS England 111 demand for primary care services: a discrete event simulation |
title | Modelling NHS England 111 demand for primary care services: a discrete event simulation |
title_full | Modelling NHS England 111 demand for primary care services: a discrete event simulation |
title_fullStr | Modelling NHS England 111 demand for primary care services: a discrete event simulation |
title_full_unstemmed | Modelling NHS England 111 demand for primary care services: a discrete event simulation |
title_short | Modelling NHS England 111 demand for primary care services: a discrete event simulation |
title_sort | modelling nhs england 111 demand for primary care services: a discrete event simulation |
topic | Health Informatics |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10496671/ https://www.ncbi.nlm.nih.gov/pubmed/37673448 http://dx.doi.org/10.1136/bmjopen-2023-076203 |
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