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• Article | Harvard Business Review (website)

  How to Make Remote Monitoring Tech Part of Everyday Health Care

  Samantha F. Sanders, Ariel Dora Stern and William J. Gordon

  Citation:

  Sanders, Samantha F., Ariel Dora Stern, and William J. Gordon. "How to Make Remote Monitoring Tech Part of Everyday Health Care." Harvard Business Review (website) (July 2, 2020).  View Details

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• Article | npj Digital Medicine

  Quantifying the Use of Connected Digital Products in Clinical Research

  Caroline Marra, Jacqueline L. Chen, Andrea Coravos and Ariel D. Stern

  Over recent years, the adoption of connected technologies has grown dramatically, with potential for improving health care delivery, research, and patient experience. Yet, little has been documented about the prevalence and use of connected digital products (e.g., products that capture physiological and behavioral metrics) in formal clinical research. Using 18 years of data from ClinicalTrials.gov, we document substantial growth in the use of connected digital products in clinical trials (~34% CAGR) and show that these products have been used across all phases of research and by a diverse group of trial sponsors. We identify four distinct use cases for how such connected products have been integrated within clinical trial design and suggest implications for various stakeholders engaging in clinical research.

  Keywords: connected digital products; clinical trials; Health Testing and Trials; Technology; Research;

  Citation:

  Marra, Caroline, Jacqueline L. Chen, Andrea Coravos, and Ariel D. Stern. "Quantifying the Use of Connected Digital Products in Clinical Research." Art. 50. npj Digital Medicine 3 (2020).  View Details

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• Article | Biostatistics | April 2020

  Regulatory Oversight, Causal Inference, and Safe and Effective Health Care Machine Learning

  Ariel Dora Stern and W Nicholson Price, II

  In recent years, the applications of Machine Learning (ML) in the health care delivery setting have grown to become both abundant and compelling. Regulators have taken notice of these developments and the U.S. Food and Drug Administration (FDA) has been engaging actively in thinking about how best to facilitate safe and effective use. Although the scope of its oversight for software-driven products is limited, if FDA takes the lead in promoting and facilitating appropriate applications of causal inference as a part of ML development, that leadership is likely to have implications well beyond regulated products.

  Keywords: Machine learning; causal inference; Health Care and Treatment; Safety; Governing Rules, Regulations, and Reforms;

  Citation:

  Stern, Ariel Dora, and W Nicholson Price, II. "Regulatory Oversight, Causal Inference, and Safe and Effective Health Care Machine Learning." Biostatistics 21, no. 2 (April 2020): 363–367.  View Details

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