The case for information fiduciaries: The implementation of a data ethics checklist at Seattle Children's Hospital.

There is little debate about the importance of ethics in health care, and clearly defined rules, regulations, and oaths help ensure patients' trust in the care they receive. However, standards are not as well established for the data professions within health care, even though the responsibility to treat patients in an ethical way extends to the data collected about them. Increasingly, data scientists, analysts, and engineers are becoming fiduciarily responsible for patient safety, treatment, and outcomes, and will require training and tools to meet this responsibility. We developed a data ethics checklist that enables users to consider the possible ethical issues that arise from the development and use of data products. The combination of ethics training for data professionals, a data ethics checklist as part of project management, and a data ethics committee holds potential for providing a framework to initiate dialogues about data ethics and can serve as an ethical touchstone for rapid use within typical analytic workflows, and we recommend the use of this or equivalent tools in deploying new data products in hospitals.

Risk of a second wave of Covid-19 infections: using artificial intelligence to investigate stringency of physical distancing policies in North America.

PURPOSE: Accurately forecasting the occurrence of future covid-19-related cases across relaxed (Sweden) and stringent (USA and Canada) policy contexts has a renewed sense of urgency. Moreover, there is a need for a multidimensional county-level approach to monitor the second wave of covid-19 in the USA. METHOD: We use an artificial intelligence framework based on timeline of policy interventions that triangulated results based on the three approaches-Bayesian susceptible-infected-recovered (SIR), Kalman filter, and machine learning. RESULTS: Our findings suggest three important insights. First, the effective growth rate of covid-19 infections dropped in response to the approximate dates of key policy interventions. We find that the change points for spreading rates approximately coincide with the timelines of policy interventions across respective countries. Second, forecasted trend until mid-June in the USA was downward trending, stable, and linear. Sweden is likely to be heading in the other direction. That is, Sweden's forecasted trend until mid-June appears to be non-linear and upward trending. Canada appears to fall somewhere in the middle-the trend for the same period is flat. Third, a Kalman filter based robustness check indicates that by mid-June the USA will likely have close to two million virus cases, while Sweden will likely have over 44,000 covid-19 cases. CONCLUSION: We show that drop in effective growth rate of covid-19 infections was sharper in the case of stringent policies (USA and Canada) but was more gradual in the case of relaxed policy (Sweden). Our study exhorts policy makers to take these results into account as they consider the implications of relaxing lockdown measures.