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Exercise ASKARI SERPENT: enabling clinical data collection during exercises and operations to support future contingency planning and assurance of category-based reporting systems
  1. Iain T Parsons1,2,
  2. RJ Wheatley2 and
  3. P Carter2
  1. 1Department of Critical Care, Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, London, UK
  2. 25 Medical Regiment, Gaza Barracks, Hipswell, North Yorkshire, UK
  1. Correspondence to Capt Iain T Parsons, Department of Critical Care, Royal Brompton Hospital, Royal Brompton and Harefield NHS Foundation Trust, Sydney Street, London SW3 6NP, UK; iainparsons{at}doctors.org.uk

Abstract

Introduction Exercise ASKARI SERPENT (Ex AS) is a British Army exercise that provides primary healthcare (PHC) to Kenyan civilians in support of local health authorities. It is conducted in partnership with the Kenya Defence Force Medical Services (KDFMS). Accurate epidemiological data is critical in planning the exercise and for any future short-notice contingency operations in similar environments. This paper reports epidemiological data for Ex AS using a novel data collection system.

Methods PHC on Ex AS was delivered by trained and validated combat medical technicians (CMTs) using a set of Read-coded protocols. The CMTs were also directly supported and supervised by medical officers and nurses.

Results A total of 3093 consultations were conducted over a 16-day period. Of these, 2707 (87.5%) consultations fell within the remit of the CMT protocols, with only 386 consultations (12.5%) being conducted exclusively by the medical officers or nurses.

Discussion A Read-coded matrix built on CMT protocols is a simple and useful tool, particularly in civilian populations, for collecting morbidity data with the vast majority of conditions accounted for in the protocols. It is anticipated that such a system can better inform training, manning, medical material and pharmaceutical procurement than current category-based morbidity surveillance systems such as EPINATO (NATO epidemiological data). There is clear advantage to directly linking data capture to treatment algorithms. Accuracy, both in terms of numbers and condition, is likely improved. Data is also captured contemporaneously rather than after indeterminate time. Read coding has the added benefit of being an established electronic standard. In addition, the system would support traditional reporting methods such as EPINATO by providing increased assurance.

  • EPIDEMIOLOGY
  • Received October 12, 2014.
  • Revision received March 26, 2015.
  • Accepted April 16, 2015.

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  • Received October 12, 2014.
  • Revision received March 26, 2015.
  • Accepted April 16, 2015.
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