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Applicability of Regulatory Oversight Requirements for Human Subjects Research to Clinical Decision Support Systems
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In particular, ethical and regulatory oversight should ensure that (1) conditions for use of these systems (including adherence to evidence-based approaches) and the basis for the recommendations they generate are appropriately articulated, (2) systems rely on validated algorithms and address issues of data quality, and (3) sufficient privacy protections exist for patients whose data are used. In this paper, we focus on the question, What constitutes appropriate regulatory oversight of clinical decision support systems? We argue that while use of these systems does not necessarily constitute a research activity subject to the Common Rule, 3 development and implementation of these systems requires a greater level of ethical and regulatory oversight than is generally applied to activities of clinical practice or other health systems-level decisions about care delivery. 2 However, a number of scientific, ethical, and regulatory questions remain regarding development and use of such clinical decision support systems for the purpose of making treatment recommendations. For example, some have argued that clinical decision support systems leveraging data aggregated from patients with similar clinical presentations could be designed to provide real-time, point-of-care feedback to help inform personalized treatment choices. The ability to leverage routinely collected data, both within and across health systems, holds promise for improving the organization and quality of care delivered to patients and for informing diagnostic, treatment, and other decisions based on patients’ needs and individual characteristics. A learning health system has been defined by the National Academy of Medicine (NAM) as one “in which science, informatics, incentives, and culture are aligned for continuous improvement and innovation, with best practices seamlessly embedded in the delivery process and new knowledge captured as an integral by-product of the delivery experience.” 1 The increasing adoption of electronic health records (EHRs) and other technological advances allowing for routine collection of patient-generated data contributes to the infrastructure needed to transform health systems within the United States and abroad into learning health systems. Learning Health Systems and Patient-Centered Care In particular, we argue that the development and use of clinical decision support systems should be governed by a framework that (1) articulates appropriate conditions for their use, (2) includes processes for monitoring data quality and developing and validating algorithms, and (3) sufficiently protects patients’ data. We argue that though using a clinical decision support system does not necessarily constitute a research activity subject to the Common Rule, it requires more ethical and regulatory oversight than activities of clinical practice are generally subjected to. One such opportunity includes a clinical decision support structure that would allow clinicians to query electronic health records (EHRs) such that responses from the EHRs could inform treatment recommendations. A learning health system provides opportunities to leverage data generated in the course of standard clinical care to improve clinical practice.