Article Text

Improving management of hospitalised patients with COVID-19: algorithms and tools for implementation and measurement
  1. Ahmed Salem1,2,
  2. Hossam Elamir3,
  3. Huda Alfoudri4,
  4. Mohammed Shamsah4,
  5. Shams Abdelraheem5,
  6. Ibtissam Abdo3,
  7. Mohammad Galal3,
  8. Lamiaa Ali3,6
  1. 1Anaesthesia and Intensive Care Department, Sabah Al Ahmad Urology Centre, Ministry of Health, Sabah, Kuwait
  2. 2Anaesthesia and Intensive Care Department, Faculty of Medicine, Banha University, Benha, Egypt
  3. 3Quality and Accreditation Directorate, Ministry of Health, Safat, Kuwait
  4. 4Anaesthesia, Critical Care and Pain Management Department, Adan Hospital, Ministry of Health, Hadiya, Kuwait
  5. 5Critical Care Department, Manchester University NHS Foundation Trust, Manchester, Greater Manchester, UK
  6. 6Public Health Department, Fayoum University Faculty of Medicine, Fayoum, Egypt
  1. Correspondence to Dr Hossam Elamir; dr_hossam_elamir{at}hotmail.com

Abstract

Background The COVID-19 pandemic represents an unprecedented challenge to healthcare systems and nations across the world. Particularly challenging are the lack of agreed-upon management guidelines and variations in practice. Our hospital is a large, secondary-care government hospital in Kuwait, which has increased its capacity by approximately 28% to manage the care of patients with COVID-19. The surge in capacity has necessitated the redeployment of staff who are not well-trained to manage such conditions. There was a great need to develop a tool to help redeployed staff in decision-making for patients with COVID-19, a tool which could also be used for training.

Methods Based on the best available clinical knowledge and best practices, an eight member multidisciplinary group of clinical and quality experts undertook the development of a clinical algorithm-based toolkit to guide training and practice for the management of patients with COVID-19. The team followed Horabin and Lewis’ seven-step approach in developing the algorithms and a five-step method in writing them. Moreover, we applied Rosenfeld et al’s five points to each algorithm.

Results A set of seven clinical algorithms and one illustrative layout diagram were developed. The algorithms were augmented with documentation forms, data-collection online forms and spreadsheets and an indicators’ reference sheet to guide implementation and performance measurement. The final version underwent several revisions and amendments prior to approval.

Conclusions A large volume of published literature on the topic of COVID-19 pandemic was translated into a user-friendly, algorithm-based toolkit for the management of patients with COVID-19. This toolkit can be used for training and decision-making to improve the quality of care provided to patients with COVID-19.

  • clinical practice guidelines
  • critical care
  • decision support
  • clinical
  • performance measures
  • quality improvement
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Footnotes

  • Contributors AS reviewed the published literature and drafted the algorithms manually. HA, MS and SA revised and approved the algorithms. HE drew the algorithms using MS Visio, wrote the manuscript and created the data-collection online forms and spreadsheets. IA, MG and LA drafted the documentation forms and HE finalised them. IA and LA drafted the indicators reference sheet and HE and AS finalised it. AS, IA, MG and LA revised the final version of the documentation forms, indicators reference sheet and data-collection online forms and spreadsheets. All authors critically revised the manuscript, gave final approval of the version to be published and are accountable for all aspects of the work. HE is the guarantor of the manuscript.

  • Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

  • Competing interests None declared.

  • Patient consent for publication Not required.

  • Provenance and peer review Not commissioned; externally peer reviewed.

  • Data availability statement All data relevant to the study are included in the article or uploaded as supplementary information.

  • Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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