Role of Artificial Intelligence in Patient Safety Outcomes: Systematic Literature Review

What is it about?

The purpose of this systematic literature review was to identify and analyze quantitative studies utilizing or integrating AI to address and report clinical-level patient safety outcomes.

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Photo by Franck V. on Unsplash

Photo by Franck V. on Unsplash

Why is it important?

Many studies have reported high accuracy of AI in health care. However, its actual influence (negative or positive) can only be realized when it is integrated into clinical settings or interpreted and used by care providers. Therefore, in our view, patient safety and AI performance might not necessarily complement each other. AI in health care depends on data sources such as EHR systems, sensor data, and patient-reported data. EHR systems may contain more severe cases for specific patient populations. Certain patient populations may have more ailments or may be seen at multiple institutions. Certain subgroups of patients with rare diseases may not exist in sufficient numbers for a predictive analytic algorithm. Thus, clinical data retrieved from EHRs might be prone to biases. Owing to these potential biases, AI accuracy might be misleading when trained on a small subgroup or small sample size of patients with rare ailments. Furthermore, patients with limited access to health care may receive fewer diagnostic tests and medications and may have insufficient health information in the EHR to trigger an early intervention. In addition, institutions record patient information differently; as a result, if AI models trained at one institution are implemented to analyze data at another institution, this may result in errors. For instance, machine-learning algorithms developed at a university hospital to predict patient-reported outcome measures, which tend to be documented by patients who have high education as well as high income, may not be applicable when implemented at a community hospital that primarily serves underrepresented patient groups with low income. A review conducted in 2017 reported that only about 54% of studies that developed prediction models based on EHRs accounted for missing data. Recent studies and reviews have been primarily focusing on the performance and influence of AI systems at a diagnostic level, such as for disease identification, and the influence of AI robotics in surgery and disease management; however, there is a lack of studies reviewing and reporting the impact of AI used at the clinical level on patient safety outcomes, as well as characteristics of the AI algorithms used. Thus, it is essential to study how AI has been shown to influence patient safety outcomes at the clinical level, along with reported AI performance in the literature.

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