The COVID-19 pandemic resulted in major disruption to healthcare delivery worldwide causing medical services to adapt their standard practices. Learning how these adaptations result in unintended patient harm is essential to mitigate against future incidents. Incident reporting and learning system data can be used to identify areas to improve patient safety. A classification system is required to make sense of such data to identify learning and priorities for further in-depth investigation. The Patient Safety (PISA) classification system was created for this purpose, but it is not known if classification systems are sufficient to capture novel safety concepts arising from crises like the pandemic. We aimed to review the application of the PISA classification system during the COVID-19 pandemic to appraise whether modifications were required to maintain its meaningful use for the pandemic context.
We conducted a mixed-methods study integrating two phases in an exploratory, sequential design. This included a comparative secondary analysis of patient safety incident reports from two studies conducted during the first wave of the pandemic, where we coded patient-reported incidents from the UK and clinician-reported incidents from France. The findings were presented to a focus group of experts in classification systems and patient safety, and a thematic analysis was conducted on the resultant transcript.
We identified five key themes derived from the data analysis and expert group discussion. These included capitalising on the unique perspective of safety concerns from different groups, that existing frameworks do identify priority areas to investigate further, the objectives of a study shape the data interpretation, the pandemic spotlighted long-standing patient concerns, and the time period in which data are collected offers valuable context to aid explanation. The group consensus was that no COVID-19-specific codes were warranted, and the PISA classification system was fit for purpose.
We have scrutinised the meaningful use of the PISA classification system\'s application during a period of systemic healthcare constraint, the COVID-19 pandemic. Despite these constraints, we found the framework can be successfully applied to incident reports to enable deductive analysis, identify areas for further enquiry and thus support organisational learning. No new or amended codes were warranted. Organisations and investigators can use our findings when reviewing their own classification systems.

Missing item responses are prevalent in educational large-scale assessment studies such as the programme for international student assessment (PISA). The current operational practice scores missing item responses as wrong, but several psychometricians have advocated for a model-based treatment based on latent ignorability assumption. In this approach, item responses and response indicators are jointly modeled conditional on a latent ability and a latent response propensity variable. Alternatively, imputation-based approaches can be used. The latent ignorability assumption is weakened in the Mislevy-Wu model that characterizes a nonignorable missingness mechanism and allows the missingness of an item to depend on the item itself. The scoring of missing item responses as wrong and the latent ignorable model are submodels of the Mislevy-Wu model. In an illustrative simulation study, it is shown that the Mislevy-Wu model provides unbiased model parameters. Moreover, the simulation replicates the finding from various simulation studies from the literature that scoring missing item responses as wrong provides biased estimates if the latent ignorability assumption holds in the data-generating model. However, if missing item responses are generated such that they can only be generated from incorrect item responses, applying an item response model that relies on latent ignorability results in biased estimates. The Mislevy-Wu model guarantees unbiased parameter estimates if the more general Mislevy-Wu model holds in the data-generating model. In addition, this article uses the PISA 2018 mathematics dataset as a case study to investigate the consequences of different missing data treatments on country means and country standard deviations. Obtained country means and country standard deviations can substantially differ for the different scaling models. In contrast to previous statements in the literature, the scoring of missing item responses as incorrect provided a better model fit than a latent ignorable model for most countries. Furthermore, the dependence of the missingness of an item from the item itself after conditioning on the latent response propensity was much more pronounced for constructed-response items than for multiple-choice items. As a consequence, scaling models that presuppose latent ignorability should be refused from two perspectives. First, the Mislevy-Wu model is preferred over the latent ignorable model for reasons of model fit. Second, in the discussion section, we argue that model fit should only play a minor role in choosing psychometric models in large-scale assessment studies because validity aspects are most relevant. Missing data treatments that countries can simply manipulate (and, hence, their students) result in unfair country comparisons.

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