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Journal: Journal of the American Medical Informatics Association : JAMIA


The novel coronavirus disease-19 (COVID-19) pandemic has altered our economy, society and healthcare system. While this crisis has presented the US healthcare delivery system with unprecedented challenges, the pandemic has catalyzed rapid adoption of telehealth or the entire spectrum of activities used to deliver care at a distance. Using examples reported by US healthcare organizations including ours, we describe the role telehealth has played in transforming healthcare delivery during the three phases of the US COVID-19 pandemic: 1) Stay-at-Home Outpatient Care; 2) Initial COVID-19 Hospital Surge, and 3) Post-Pandemic Recovery. Within each of these three phases, we examine how people, process and technology work together to support a successful telehealth transformation. Whether healthcare enterprises are ready or not, the new reality is that virtual care has arrived.


Adverse drug events cause substantial morbidity and mortality and are often discovered after a drug comes to market. We hypothesized that Internet users may provide early clues about adverse drug events via their online information-seeking. We conducted a large-scale study of Web search log data gathered during 2010. We pay particular attention to the specific drug pairing of paroxetine and pravastatin, whose interaction was reported to cause hyperglycemia after the time period of the online logs used in the analysis. We also examine sets of drug pairs known to be associated with hyperglycemia and those not associated with hyperglycemia. We find that anonymized signals on drug interactions can be mined from search logs. Compared to analyses of other sources such as electronic health records (EHR), logs are inexpensive to collect and mine. The results demonstrate that logs of the search activities of populations of computer users can contribute to drug safety surveillance.

Concepts: Pharmacology, Sociology, Adverse drug reaction, Electronic health record, Pharmacovigilance, Periodization, Drug safety, Research on Adverse Drug events And Reports


Many research fields, including psychology and basic medical sciences, struggle with poor reproducibility of reported studies. Biomedical and health informatics is unlikely to be immune to these challenges. This paper explores replication in informatics and the unique challenges the discipline faces.

Concepts: Health care, Scientific method, Medicine, Health, Falsifiability, Health science, Pseudoscience, Health sciences


The study sought to determine the impact of a digital sepsis alert on patient outcomes in a UK multisite hospital network.


In response to mounting evidence that use of electronic medical record systems may cause unintended consequences, and even patient harm, the AMIA Board of Directors convened a Task Force on Usability to examine evidence from the literature and make recommendations. This task force was composed of representatives from both academic settings and vendors of electronic health record (EHR) systems. After a careful review of the literature and of vendor experiences with EHR design and implementation, the task force developed 10 recommendations in four areas: (1) human factors health information technology (IT) research, (2) health IT policy, (3) industry recommendations, and (4) recommendations for the clinician end-user of EHR software. These AMIA recommendations are intended to stimulate informed debate, provide a plan to increase understanding of the impact of usability on the effective use of health IT, and lead to safer and higher quality care with the adoption of useful and usable EHR systems.

Concepts: Electronic health record, Electronic medical record, Health informatics, Medical informatics, VistA, Computer physician order entry, Usability, Personal health record


There is increasing awareness that the methodology and findings of research should be transparent. This includes studies using artificial intelligence to develop predictive algorithms that make individualized diagnostic or prognostic risk predictions. We argue that it is paramount to make the algorithm behind any prediction publicly available. This allows independent external validation, assessment of performance heterogeneity across settings and over time, and algorithm refinement or updating. Online calculators and apps may aid uptake if accompanied with sufficient information. For algorithms based on “black box” machine learning methods, software for algorithm implementation is a must. Hiding algorithms for commercial exploitation is unethical, because there is no possibility to assess whether algorithms work as advertised or to monitor when and how algorithms are updated. Journals and funders should demand maximal transparency for publications on predictive algorithms, and clinical guidelines should only recommend publicly available algorithms.


Deep learning has become a promising approach for automated support for clinical diagnosis. When medical data samples are limited, collaboration among multiple institutions is necessary to achieve high algorithm performance. However, sharing patient data often has limitations due to technical, legal, or ethical concerns. In this study, we propose methods of distributing deep learning models as an attractive alternative to sharing patient data.

Concepts: Medicine, Medical imaging, Physician, Distribution, Machine learning, Psychiatry, Technical support, Algorithmic efficiency


To model inconsistencies or distortions among three realities: patients' physical reality; clinicians' mental models of patients' conditions, laboratories, etc; representation of that reality in electronic health records (EHR). To serve as a potential tool for quality improvement of EHRs.

Concepts: Health care, Health care provider, Illness, Mind, Metaphysics, Electronic health record, Health informatics, Medical informatics


Our objective was to review the characteristics, current applications, and evaluation measures of conversational agents with unconstrained natural language input capabilities used for health-related purposes.


To try to lower patient re-identification risks for biomedical research databases containing laboratory test results while also minimizing changes in clinical data interpretation.

Concepts: Decision theory, Data analysis, Orphan drug