Ethical and Legal Issues in Decision Support

A new approach to medical diagnostic decision support

Data mining is a powerful tool to reduce costs and mitigate errors in the diagnostic analysis and repair of complex engineered system, but it has yet to be applied systematically to the most complex and socially expensive system - the human body. The currently available approaches of knowledge-based and pattern-based artificial intelligence are unsuited to the iterative and often subjective nature of clinician-patient interactions. Furthermore, current electronic health records generally have poor design and low quality for such data mining. Bayesian methods have been developed to suggest multiple possible diagnoses given a set of clinical findings, but the larger problem is advising the physician on useful next steps. A new approach based on inverting Bayesian inference allows identification of the diagnostic actions that are most likely to disambiguate a differential diagnosis at each point in a patient's work-up. This can be combined with personalized cost information to suggest a cost-effective path to the clinician. Because the software is tracking the clinician's decision-making process, it can provide salient suggestions for both diagnoses and diagnostic tests in standard, coded formats that need only to be selected. This would reduce the need to type in free text, which is prone to ambiguities, omissions and errors. As the database of high-quality records grows, the scope, utility and acceptance of the system should also grow automatically, without requiring expert updating or correction.

Ethical and Practical Considerations in the Use of a Predictive Model to Trigger Suicide Prevention Interventions in Healthcare Settings

Predictive models that utilize data from electronic healthcare records (EHR) have been developed, investigated, and appear to provide an important resource for suicide prevention in medical settings. Actuarial approaches to predicting suicide may be particularly important given the relative inability of clinicians to accurately predict suicide. Although research regarding predictive models that utilize EHR is certainly promising, ethical considerations for the use of these models to trigger suicide prevention interventions warrant careful consideration. The current manuscript discusses ethical considerations regarding the use of predictive models in suicide prevention clinical care. The unique characteristics of suicide are explored in terms of how they inform ethical and practical approaches. Additionally, biomedical ethical principles and utilitarian, Kantian, and personal rights ethical models are applied to the topic. Recommendations for navigating the ethical issues are provided as an initial framework for others who are considering the implementation of a predictive model to trigger suicide prevention initiatives.