Can AI enhance the reliability of accident investigations?

Imagine that in a hospital in Libreville, a patient falls in a corridor. The report is filled out, signed, and then archived. Everything seems in order. Yet, the real cause remains unclear: wet floor, lack of signage, poorly stored stretcher, staff overload, or misunderstood procedure?

In a construction SME, the same scene occurs after an injury on site. The quality, hygiene, safety, and environment (QHSE) manager wants to act quickly. An artificial intelligence (AI) suggests a cause and actions. Good assistance, or a risk of incorrect conclusion?

What is it, concretely?

After an accident or near miss, an organization writes a report: observed facts, probable causes, measures to be taken. The problem is that a file can be complete in form but weak in substance. HavenASSURE acts as a quality controller for safety investigations: it reviews the available elements, flags absent evidence, overly quick explanations, and poorly defined corrective actions. The main issue is to prevent a poor analysis from becoming a false lesson learned. For a non-specialist, think of an internal audit: the tool helps to identify inconsistencies, but it does not replace the investigator or the manager who signs off on the decision.

Concrete case: what to do and what not to do

Fictitious educational case: the Clinique du Littoral Libreville, a private healthcare facility, notices several minor incidents in its corridors. Visitors slip, patients fall, stretchers sometimes remain in passage areas. Reports exist, but the causes are poorly addressed in QHSE meetings.

The QHSE manager wants to use AI to review the files and find recurring causes. The temptation would be to conclude quickly: "the staff does not follow the instructions." What is still missing: field evidence, listening to staff, cleaning schedules, floor conditions, signage, lighting, workload, and validation by the safety committee.

What can be done

Define an investigation method: testimonies, photos, chronology, and possible causes.
Use AI to identify missing information, not to assign blame.
Have the conclusions validated by QHSE, human resources, and the relevant department.
Transform each recommendation into a follow-up action: responsible person, deadline, budget, evidence.
Audit the quality of investigations, not just the number of closed reports.

What not to do

Copy-paste the AI's conclusion into the final report without review.
Reduce the accident to human error without examining the organization of work.
Send health or identity data to an external tool without a clear framework.
Measure safety performance solely by the speed of closure.
Use AI as an automatic disciplinary tool against employees.

Questions to Ask Before Acting

Does the AI indicate what evidence it bases its remarks on?
Who validates the final conclusion: QHSE, human resources, management, or the safety committee?
Does the file contain sensitive data: health, identity, images, videos, or geolocation?
Have the concerned workers been heard before concluding?
Do the actions really reduce the risk, or are they limited to retraining?
Does the company keep a clear record of human decisions made after the recommendation?
Does the tool comply with local obligations and the standards of the International Organization for Standardization (ISO), including ISO 45001, ISO 9001, and ISO 14001?

UNIVGA Viewpoint

For Gabon and Francophone Africa, the issue is not just about 'putting AI everywhere'. The real challenge is to strengthen QHSE evidence. The Gabonese Labor Code places health and safety prevention at the heart of the employer's obligations, with a direct responsibility for measures aimed at protecting workers. A tool that helps better document incidents can therefore be useful, provided it does not undermine professional judgment.

QHSE data is sensitive. An incident report may contain medical information, testimonies, images, or elements that could lead to a sanction. The Gabonese framework on personal data reminds us that technologies must not infringe on privacy or freedoms. Therefore, before any deployment, rules must be established: access, retention, confidentiality, clauses with the provider, data location.

UNIVGA's position is clear: to train leaders capable of governing these tools, not to endure them. Future QHSE managers, HR professionals, and auditors must know how to question a recommendation, organize human validation, involve workers, and connect the use of AI to quality, safety, and environmental standards.

Can AI enhance the reliability of accident investigations?

What is it, concretely?

Concrete case: what to do and what not to do

Questions to Ask Before Acting

UNIVGA Viewpoint

Sources

Digital cadastre: how to avoid land errors?

Digital cadastre: how to avoid land errors?

How to frame AI at the university without cheating?

Data centers: how to secure the entrance?

License Plate Cameras: How to Monitor Without Excess?

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