Outlier analysis for accelerating clinical discovery: An augmented intelligence framework and a systematic review

Clinical discoveries largely depend on dedicated clinicians and scientists to identify and pursue unique and unusual clinical encounters with patients and communicate these through case reports and case series. This process has remained essentially unchanged throughout the history of modern medicine. However, these traditional methods are inefficient, especially considering the modern-day availability of health-related data and the sophistication of computer processing. Outlier analysis has been used in various fields to uncover unique observations, including fraud detection in finance and quality control in manufacturing. We propose that clinical discovery can be formulated as an outlier problem within an augmented intelligence framework to be implemented on any health-related data. Such an augmented intelligence approach would accelerate the identification and pursuit of clinical discoveries, advancing our medical knowledge and uncovering new therapies and management approaches. We define clinical discoveries as contextual outliers measured through an information-based approach and with a novelty-based root cause. Our augmented intelligence framework has five steps: define a patient population with a desired clinical outcome, build a predictive model, identify outliers through appropriate measures, investigate outliers through domain content experts, and generate scientific hypotheses. Recognizing that the field of obstetrics can particularly benefit from this approach, as it is traditionally neglected in commercial research, we conducted a systematic review to explore how outlier analysis is implemented in obstetric research. We identified two obstetrics-related studies that assessed outliers at an aggregate level for purposes outside of clinical discovery. Our findings indicate that using outlier analysis in clinical research in obstetrics and clinical research, in general, requires further development.Author summary: We introduce a new way to accelerate clinical discoveries by applying outlier analysis within an augmented intelligence framework. Traditionally, the unique clinical observations that led to breakthroughs like the discovery of Kawasaki disease and treatments for psychological disorders were reported through detailed case reports and case series. However, these methods often miss many such observations due to the intense demands on clinicians’ time and the inefficiency of the case report and case series systems. Our approach reimagines clinical discovery as an outlier problem, where unusual data points within health datasets, identified through outlier analysis, signal important new findings for experts to investigate. We propose a five-step process that begins with defining a patient population and ends with generating scientific hypotheses. This structured approach enhances our capacity to identify novel medical insights and reduces the reliance on happenstance and the subjective selection of what observations to pursue. This framework represents a significant shift towards a more proactive and data-driven method in medical research and ushers in a new era of clinical discovery.