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Background <p>Emergency medical services (EMS) are critical for patient outcomes during emergencies. Organised EMS systems that can promptly locate patients, deliver on-scene care, and transport patients to appropriate facilities are lacking in low- and middle-income countries. Through a long-standing partnership with Rwanda’s Service d’Aide Médicale d’Urgence (SAMU), we developed and implemented an electronic application to capture prehospital times in Kigali, Rwanda. This study validates the accuracy of electronically captured data and establishes baseline response times for emergencies in Kigali.</p> Methods <p>Prospective data on prehospital transport times in Kigali collected from July 2022 to December 2023 were captured using a novel electronic application custom-built by Rwanda Build program (RWB), a local software accelerator in Kigali. RWB data were compared to SAMU’s manually collected data to validate the accuracy of the electronically captured data. Datasets were deterministically linked using patient identifications (ID). Cases with missing patient ID were probabilistically linked using RWB deploy time compared to the paper dataset’s leave time. The primary outcome was total prehospital time, from ambulance deployment to healthcare facility arrival. Secondary outcomes included time intervals of deployment to scene, on scene, scene to healthcare facility, and handoff times. Additional analyses compared response times of subgroups captured by RWB, including emergency type, severity, and prehospital delays.</p> Results <p>After SAMU and RWB data linkage, 6209 patients were included. The primary outcome, total time of ambulance deployment to hospital arrival, took an average of 54.5&#xa0;min (standard deviation (SD) = 22.2) with a 0.53-min difference between paper records and the electronically captured dataset. The 30-s difference suggests the newly implemented electronic collection system is consistent with data recorded manually on EMS run reports. Trauma represented the most common emergency (65.2%), with an average prehospital time of 52.2&#xa0;min (SD = 21.4). “Severe” emergencies took an average of 20&#xa0;min longer to reach hospitals than “mild” cases (<i>p</i> &lt; 0.001). Transit delays and districts with fewer transports were also associated with longer prehospital times.</p> Conclusions <p>EMS is critical to healthcare systems. This study validated an electronic data-collection system to improve EMS services in Rwanda. This is one of the first studies to document EMS quality benchmarks in a sub-Saharan African country.</p>

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Validation of a mobile application for prehospital transport time data collection in Kigali, Rwanda

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月22日

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  • Teresa Bell,
  • Jean Marie Uwitonze,
  • Michael Oravic,
  • Rebecca Maine,
  • Jeanne d’Arc Nyinawankusi,
  • Andrea N. Davis,
  • Jean Nepomuscene Sindikubwabo,
  • Robert Rickard,
  • Melissa H. Watt,
  • Justine Davies,
  • Menelas Nkeshimana,
  • Sudha Jayaraman

摘要

Background

Emergency medical services (EMS) are critical for patient outcomes during emergencies. Organised EMS systems that can promptly locate patients, deliver on-scene care, and transport patients to appropriate facilities are lacking in low- and middle-income countries. Through a long-standing partnership with Rwanda’s Service d’Aide Médicale d’Urgence (SAMU), we developed and implemented an electronic application to capture prehospital times in Kigali, Rwanda. This study validates the accuracy of electronically captured data and establishes baseline response times for emergencies in Kigali.

Methods

Prospective data on prehospital transport times in Kigali collected from July 2022 to December 2023 were captured using a novel electronic application custom-built by Rwanda Build program (RWB), a local software accelerator in Kigali. RWB data were compared to SAMU’s manually collected data to validate the accuracy of the electronically captured data. Datasets were deterministically linked using patient identifications (ID). Cases with missing patient ID were probabilistically linked using RWB deploy time compared to the paper dataset’s leave time. The primary outcome was total prehospital time, from ambulance deployment to healthcare facility arrival. Secondary outcomes included time intervals of deployment to scene, on scene, scene to healthcare facility, and handoff times. Additional analyses compared response times of subgroups captured by RWB, including emergency type, severity, and prehospital delays.

Results

After SAMU and RWB data linkage, 6209 patients were included. The primary outcome, total time of ambulance deployment to hospital arrival, took an average of 54.5 min (standard deviation (SD) = 22.2) with a 0.53-min difference between paper records and the electronically captured dataset. The 30-s difference suggests the newly implemented electronic collection system is consistent with data recorded manually on EMS run reports. Trauma represented the most common emergency (65.2%), with an average prehospital time of 52.2 min (SD = 21.4). “Severe” emergencies took an average of 20 min longer to reach hospitals than “mild” cases (p < 0.001). Transit delays and districts with fewer transports were also associated with longer prehospital times.

Conclusions

EMS is critical to healthcare systems. This study validated an electronic data-collection system to improve EMS services in Rwanda. This is one of the first studies to document EMS quality benchmarks in a sub-Saharan African country.