Citation

Gillespie A, Reader TW. Risk Anal. 2022; ePub(ePub): ePub.

Copyright

(Copyright © 2022, Society for Risk Analysis, Publisher John Wiley and Sons)

DOI

10.1111/risa.14002

PMID

35945156

Abstract

Safety reporting systems are widely used in healthcare to identify risks to patient safety. But, their effectiveness is undermined if staff do not notice or report incidents. Patients, however, might observe and report these overlooked incidents because they experience the consequences, are highly motivated, and independent of the organization. Online patient feedback may be especially valuable because it is a channel of reporting that allows patients to report without fear of consequence (e.g., anonymously). Harnessing this potential is challenging because online feedback is unstructured and lacks demonstrable validity and added value. Accordingly, we developed an automated language analysis method for measuring the likelihood of patient-reported safety incidents in online patient feedback. Feedback from patients and families (n = 146,685, words = 22,191,427, years = 2013-2019) about acute NHS trusts (hospital conglomerates; n = 134) in England were analyzed. The automated measure had good precision (0.69) and excellent recall (0.98) in identifying incidents; was independent of staff-reported incidents (r = -0.04 to 0.19); and was associated with hospital-level mortality rates (z = 3.87; p < 0.001). The identified safety incidents were often reported as unnoticed (89%) or unresolved (21%), suggesting that patients use online platforms to give visibility to safety concerns they believe have been missed or ignored. Online stakeholder feedback is akin to a safety valve; being independent and unconstrained it provides an outlet for reporting safety issues that may have been unnoticed or unresolved within formal channels.

Language: en

Keywords

patient safety; natural language processing; incident-reporting; online feedback