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Op-ed: Our patients deserve better safety reporting. AI could be the answer (27 February 2026)

Summary

A recent US Office of Inspector General (OIG) study revealed a startling statistic: US hospitals are missing approximately half of all patient harm events. While this figure undoubtedly represents a failure, it isn’t a failure of intention or individual providers; it’s a failure of our systems, as they are set up today, to capture the full picture of safety risks that patients are facing. 

At the core of this report lies a simple fact: If we can’t see harm happening, we can’t prevent it. While healthcare has made remarkable progress in reducing infections, preventing falls and minimising medication errors in recent years, we’re still operating with a fundamentally incomplete understanding of the extent of patient harm. 

Some in healthcare safety have suggested that we abandon safety event reporting altogether, arguing that current systems are too ineffective to justify the investment. But this perspective misses a critical point: the problem here lies not in the act of reporting itself, but in how we’re doing it. 

As AI continues to reshape the way we live, work, and process information, Tejal Gandhiit suggests two transformative capabilities that can revolutionise the way we report and address safety events. 

Op-ed: Our patients deserve better safety reporting. AI could be the answer (27 February 2026) https://www.fiercehealthcare.com/ai-and-machine-learning/op-ed-our-patients-deserve-better-safety-re…

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Richard Jones

Members

Hi Tejal

There is a concern that at present, providers can't detect as much as 90% of avoidable harms.  

Where we report excess complications across different populations, we ignore the underlying comorbidities etc.  Only by risk-adjusting for each patient can we detect that 90% and fix it.  

I know this works.  I know the company went bust pushing the rock uphill to convince US healthcare that quality that improves costs as well is important.  

Thanks for sharing this information.

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