Background The potential for machine learning to disrupt the medical profession is the subject of ongoing debate within biomedical informatics and related fields. Objective This study aimed to explore general practitioners’ (GPs’) opinions about the potential impact of future technology on key tasks in primary care. Methods In June 2018, we conducted a Web-based survey of 720 UK GPs’ opinions about the likelihood of future technology to fully replace GPs in performing 6 key primary care tasks, and, if respondents considered replacement for a particular task likely, to estimate how soon the technological capacity might emerge. This study involved qualitative descriptive analysis of written responses (“comments”) to an open-ended question in the survey. Results Comments were classified into 3 major categories in relation to primary care: (1) limitations of future technology, (2) potential benefits of future technology, and (3) social and ethical concerns. Perceived limitations included the beliefs that communication and empathy are exclusively human competencies; many GPs also considered clinical reasoning and the ability to provide value-based care as necessitating physicians’ judgments. Perceived benefits of technology included expectations about improved efficiencies, in particular with respect to the reduction of administrative burdens on physicians. Social and ethical concerns encompassed multiple, divergent themes including the need to train more doctors to overcome workforce shortfalls and misgivings about the acceptability of future technology to patients. However, some GPs believed that the failure to adopt technological innovations could incur harms to both patients and physicians. Conclusions This study presents timely information on physicians’ views about the scope of artificial intelligence (AI) in primary care. Overwhelmingly, GPs considered the potential of AI to be limited. These views differ from the predictions of biomedical informaticians. More extensive, stand-alone qualitative work would provide a more in-depth understanding of GPs’ views.

We leveraged the largely untapped resource of electronic health record data to address critical clinical and epidemiological questions about Coronavirus Disease 2019 (COVID-19). To do this, we formed an international consortium (4CE) of 96 hospitals across five countries (www.covidclinical.net). Contributors utilized the Informatics for Integrating Biology and the Bedside (i2b2) or Observational Medical Outcomes Partnership (OMOP) platforms to map to a common data model. The group focused on temporal changes in key laboratory test values. Harmonized data were analyzed locally and converted to a shared aggregate form for rapid analysis and visualization of regional differences and global commonalities. Data covered 27,584 COVID-19 cases with 187,802 laboratory tests. Case counts and laboratory trajectories were concordant with existing literature. Laboratory tests at the time of diagnosis showed hospital-level differences equivalent to country-level variation across the consortium partners. Despite the limitations of decentralized data generation, we established a framework to capture the trajectory of COVID-19 disease in patients and their response to interventions.

The National Academy of Medicine has long advocated for a “learning healthcare system” that produces constantly updated reference data during the care process. Moving toward a rapid learning system to solve intractable problems in health demands a balance between protecting patients and making data available to improve health and health care. Public concerns in the U.S. about privacy and the potential for unethical or harmful uses of this data, if not proactively addressed, could upset this balance. New federal laws prioritize sharing health data, including with patient digital tools. U.S. health privacy laws do not cover data collected by many consumer digital technologies and have not been updated to address concerns about the entry of large technology companies into health care. Further, there is increasing recognition that many classes of data not traditionally considered to be healthcare-related, for example consumer credit histories, are indeed predictive of health status and outcomes. We propose a multi-pronged approach to protecting health-relevant data while promoting and supporting beneficial uses and disclosures to improve health and health care for individuals and populations. Such protections should apply to entities collecting health-relevant data regardless of whether they are covered by federal health privacy laws. We focus largely on privacy but also address protections against harms as a critical component of a comprehensive approach to governing health-relevant data. U.S. policymakers and regulators should consider these recommendations in crafting privacy bills and rules. However, our recommendations also can inform best practices even in the absence of new federal requirements.