Medical Simulation as a Vital Adjunct to Identifying Clinical Life-Threatening Gaps in Austere Environments

  • Journal of the National Medical Association, Volume 110, No. 2, Year 2018

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Study Justification:
– Maternal mortality and morbidity are major causes of death in low-resource countries, particularly in Sub-Saharan Africa.
– Healthcare workforce scarcities in these areas lead to poor perioperative care access and quality.
– Limited capacity for workforce training and skills development hinders progress in healthcare.
– Low-cost, in-situ simulation systems can help identify areas needing improvement and rehearse best practices in target environments.
Study Highlights:
– Nurse anesthetists in Sierra Leone participated in simulation-based obstetric anesthesia scenarios.
– Participants underwent detailed computer-assisted training on the Universal Anesthesia Machine (UAM).
– An expert panel rated the risk of critical incidents within the scenarios.
– Participant responses to critical incidents were observed and assessed.
– Results showed substantial risks to patient care and highlighted gaps in safe anesthesia care.
Recommendations for Lay Reader:
– In-situ simulation-based training can help improve patient care in low-resource settings.
– Identifying and addressing critical gaps in anesthesia care is crucial for reducing maternal mortality and morbidity.
– Further investigations are needed to validate the impact and sustainability of simulation-based training in low-resource environments.
Recommendations for Policy Maker:
– Allocate resources to implement in-situ simulation-based training programs in low-resource countries.
– Prioritize training and skills development for anesthesia providers in these areas.
– Support further research to assess the long-term impact and effectiveness of simulation-based training in improving patient outcomes.
Key Role Players:
– Nurse anesthetists: Participants in the simulation-based training programs.
– Expert panel: Assessed the risk of critical incidents and provided expertise.
– Trainers: Conducted the simulation-based training sessions.
– Policy makers: Responsible for allocating resources and implementing training programs.
Cost Items for Planning Recommendations:
– Training equipment: Low-cost, in-situ simulation systems.
– Training materials: Computer-assisted training programs, debriefing sessions.
– Trainers: Salaries or fees for the trainers conducting the simulation-based training.
– Program implementation: Costs associated with setting up and running the training programs.
– Research: Funding for further investigations to validate the impact and sustainability of simulation-based training.

The strength of evidence for this abstract is 7 out of 10.
The evidence in the abstract is moderately strong. The study conducted simulation-based performance assessment for identifying critical gaps in safe anesthesia care in low-resource settings. The study included a sample size of 21 nurse anesthetists, providing some evidence of feasibility and value. However, the abstract does not provide information on the methodology used for data analysis or the specific results of the study. To improve the strength of the evidence, the abstract could include more details on the methodology, such as statistical analysis methods, and provide specific findings related to the identified risks and the impact of simulation-based training on skills transfer and retention.

Background: Maternal mortality and morbidity are major causes of death in low-resource countries, especially those in Sub-Saharan Africa. Healthcare workforce scarcities present in these locations result in poor perioperative care access and quality. These scarcities also limit the capacity for progressive development and enhancement of workforce training, and skills through continuing medical education. Newly available low-cost, in-situ simulation systems make it possible for a small cadre of trainers to use simulation to identify areas needing improvement and to rehearse best practice approaches, relevant to the context of target environments. Methods: Nurse anesthetists were recruited throughout Sierra Leone to participate in simulation-based obstetric anesthesia scenarios at the country’s national referral maternity hospital. All subjects participated in a detailed computer assisted training program to familiarize themselves with the Universal Anesthesia Machine (UAM). An expert panel rated the morbidity/mortality risk of pre-identified critical incidents within the scenario via the Delphi process. Participant responses to critical incidents were observed during these scenarios. Participants had an obstetric anesthesia pretest and post-test as well as debrief sessions focused on reviewing the significance of critical incident responses observed during the scenario. Results: 21 nurse anesthetists, (20% of anesthesia providers nationally) participated. Median age was 41 years and median experience practicing anesthesia was 3.5 years. Most participants (57.1%) were female, two-thirds (66.7%) performed obstetrics anesthesia daily but 57.1% had no experience using the UAM. During the simulation, participants were observed and assessed on critical incident responses for case preparation with a median score of 7 out of 13 points, anesthesia management with a median score of 10 out of 20 points and rapid sequence intubation with a median score of 3 out of 10 points. Conclusion: This study identified substantial risks to patient care and provides evidence to support the feasibility and value of in-situ simulation-based performance assessment for identifying critical gaps in safe anesthesia care in the low-resource settings. Further investigations may validate the impact and sustainability of simulation based training on skills transfer and retention among anesthesia providers low resource environments.

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Innovation Recommendation: In-situ simulation-based performance assessment for identifying critical gaps in safe anesthesia care in low-resource settings.

Description: This innovation involves using low-cost, in-situ simulation systems to assess the performance of anesthesia providers in low-resource settings. Nurse anesthetists in Sierra Leone participated in simulation-based obstetric anesthesia scenarios at the country’s national referral maternity hospital. They underwent training on the Universal Anesthesia Machine (UAM) and were observed and assessed on their responses to critical incidents during the scenarios. The study identified substantial risks to patient care and demonstrated the feasibility and value of in-situ simulation-based performance assessment in identifying critical gaps in safe anesthesia care in low-resource settings.

Potential Impact: This innovation has the potential to improve access to maternal health by addressing healthcare workforce scarcities and enhancing workforce training and skills. By identifying areas needing improvement and rehearsing best practice approaches, anesthesia providers can improve the quality of perioperative care in low-resource settings. Further investigations may validate the impact and sustainability of simulation-based training on skills transfer and retention among anesthesia providers in low-resource environments.
AI Innovations Description
Recommendation: Based on the study’s findings, the use of in-situ simulation-based training can be recommended as an innovation to improve access to maternal health in low-resource settings. This recommendation is supported by the following key points:

1. Identify critical gaps: In-situ simulation allows healthcare providers to identify areas needing improvement in obstetric anesthesia care. By simulating real-life scenarios, trainers can observe participants’ responses to critical incidents and assess their performance. This helps identify gaps in knowledge, skills, and practices that may contribute to maternal morbidity and mortality.

2. Rehearse best practice approaches: In-situ simulation provides an opportunity for healthcare providers to rehearse best practice approaches relevant to the context of low-resource environments. By familiarizing themselves with the Universal Anesthesia Machine (UAM) and practicing obstetric anesthesia scenarios, participants can enhance their skills and improve the quality of perioperative care.

3. Low-cost and accessible: The availability of low-cost, in-situ simulation systems makes it feasible to implement this training method even in resource-constrained settings. This innovation requires a small cadre of trainers and can be conducted at the country’s national referral maternity hospital or other healthcare facilities.

4. Capacity building: In-situ simulation-based training contributes to the progressive development and enhancement of healthcare workforce training. By providing continuing medical education opportunities, this innovation helps address healthcare workforce scarcities and improves access to quality maternal health care.

Further investigations and studies are needed to validate the impact and sustainability of simulation-based training on skills transfer and retention among anesthesia providers in low-resource environments. However, the initial findings of this study support the feasibility and value of in-situ simulation as a vital adjunct to identifying clinical life-threatening gaps in austere environments.
AI Innovations Methodology
Recommendation: Implementing in-situ simulation-based training programs for anesthesia providers in low-resource settings can significantly improve access to maternal health by identifying critical gaps in safe anesthesia care and enhancing skills transfer and retention.

Methodology to simulate the impact of this recommendation on improving access to maternal health:

1. Identify target population: Determine the specific low-resource settings where the implementation of in-situ simulation-based training programs for anesthesia providers is needed. This could be based on factors such as high maternal mortality rates, limited access to quality perioperative care, and healthcare workforce scarcities.

2. Recruit participants: Reach out to nurse anesthetists and other relevant healthcare professionals in the identified low-resource settings to participate in the simulation-based training programs. Ensure a diverse representation of participants in terms of experience, gender, and clinical practice.

3. Training program: Develop a detailed computer-assisted training program that familiarizes participants with the Universal Anesthesia Machine (UAM) and provides comprehensive obstetric anesthesia scenarios. This program should cover critical incidents and best practice approaches relevant to the target environments.

4. Expert panel assessment: Engage an expert panel to rate the morbidity/mortality risk of pre-identified critical incidents within the simulation scenarios. Use the Delphi process, which involves multiple rounds of anonymous feedback and consensus-building, to ensure objective and reliable ratings.

5. Simulation scenarios: Conduct in-situ simulation-based obstetric anesthesia scenarios at the national referral maternity hospital or other appropriate healthcare facilities in the target settings. Observe and assess participant responses to critical incidents during these scenarios.

6. Pretest and post-test: Administer an obstetric anesthesia pretest and post-test to evaluate the participants’ knowledge and skills improvement after the simulation-based training program. This will help measure the impact of the training on skills transfer and retention.

7. Debrief sessions: Facilitate debrief sessions with participants to review the significance of critical incident responses observed during the simulation scenarios. Encourage open discussion and reflection to enhance learning and identify areas for further improvement.

8. Data analysis: Analyze the collected data, including participant scores on critical incident responses, pretest and post-test results, and feedback from debrief sessions. Assess the impact of the simulation-based training program on identifying critical gaps in safe anesthesia care and improving access to maternal health.

9. Further investigations: Based on the findings, consider conducting additional investigations to validate the long-term impact and sustainability of simulation-based training on skills transfer and retention among anesthesia providers in low-resource environments. This could involve follow-up assessments and monitoring of participants’ performance over an extended period.

By following this methodology, the impact of implementing in-situ simulation-based training programs can be simulated and evaluated, providing valuable insights into improving access to maternal health in low-resource settings.


Statistics:
Citations: 2
Identifiers:
DOI: 10.1016/j.jnma.2017.12.003
ISSN: 00279684
Research Areas:
Health System and Policy, Maternal Access, Maternal and Child Health
Study Countries:
Sierra Leone
Participants Gender:
Female
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