Harmful data shifts occur when the distribution of data used to train a clinical AI system differs significantly from the distribution of data encountered during deployment, leading to erroneous predictions and potential harm to patients. We evaluated the impact of data shifts on an early warning system (EWS) for in-hospital mortality that uses electronic health record (EHR) data from patients admitted to a general internal medicine service. We found model performance to differ across subgroups of clinical diagnoses, sex and age. To explore the robustness of the model, we evaluated potentially harmful data shifts across demographics, hospital types, seasons, times of hospital admission, and whether the patient was admitted from an acute care institution or nursing home, without relying on model performance. Interestingly, we found that models trained on community hospitals experience harmful data shifts when evaluated on academic hospitals, whereas the models trained on academic hospitals transfer well to the community hospitals. To improve model performance across hospital sites we employed transfer learning, a strategy that stores knowledge gained from learning one domain and applies it to a different but related domain. We found hospital type-specific models that leverage transfer learning, perform better than models that use all available hospitals. Furthermore, we monitored data shifts over time and identified model deterioration during the COVID-19 pandemic. Typically machine learning models remain locked after deployment, however, this can lead to model deterioration due to data shifts that occur over time. We used continual learning, the process of learning from a continual stream of data in a sequential manner, to mitigate data shifts over time and improve model performance. Overall, our study is a crucial step towards the deployment of clinical AI models, by providing strategies and workflows to ensure the safety and efficacy of these models in real-world settings.

The authors have declared no competing interest.

This work is made possible due to the data obtained from the General Medicine Inpatient Initiative (GEMINI). V.S. is supported by Ontario Graduate Scholarship and a Vector institute grant. A.V. is supported by the Temerty Professorship in Artificial Intelligence Research and Education in Medicine at the University of Toronto. D.M. is supported by the CIBC Children's Foundation Chair in Child Health Research. AG is supported by the Varma Family Chair and CIFAR AI Chair.

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

Yes

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

All patient data was collected and approved through GEMINI under the oversight of the research ethics board (REB) at the Toronto Academic Health Science Network (REB reference number 15-087). The extension of the REB approval was issued by the Unity Health Toronto REB (reference number 15-087). A separate REB approval was obtained for Trillium Health Partners. All experiments were performed in accordance with institutional guidelines and regulations.

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

Yes

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

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I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Yes

All data produced in the present study are available to authors with access to GEMINI, upon reasonable request.